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Sample records for clinical decision support

  1. Clinical Decision Support (CDS) Inventory

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Clinical Decision Support (CDS) Inventory contains descriptions of past and present CDS projects across the Federal Government. It includes Federal projects,...

  2. Computerized Clinical Decision Support: Contributions from 2015

    Science.gov (United States)

    Bouaud, J.

    2016-01-01

    Summary Objective To summarize recent research and select the best papers published in 2015 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook. Method A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry (CPOE) systems. The aim was to identify a list of candidate best papers from the retrieved papers that were then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the IMIA editorial team was finally conducted to conclude in the best paper selection. Results Among the 974 retrieved papers, the entire review process resulted in the selection of four best papers. One paper reports on a CDSS routinely applied in pediatrics for more than 10 years, relying on adaptations of the Arden Syntax. Another paper assessed the acceptability and feasibility of an important CPOE evaluation tool in hospitals outside the US where it was developed. The third paper is a systematic, qualitative review, concerning usability flaws of medication-related alerting functions, providing an important evidence-based, methodological contribution in the domain of CDSS design and development in general. Lastly, the fourth paper describes a study quantifying the effect of a complex, continuous-care, guideline-based CDSS on the correctness and completeness of clinicians’ decisions. Conclusions While there are notable examples of routinely used decision support systems, this 2015 review on CDSSs and CPOE systems still shows that, despite methodological contributions, theoretical frameworks, and prototype developments, these technologies are not yet widely spread (at least with their full functionalities) in routine clinical practice. Further research, testing, evaluation, and training are still needed for these tools to be adopted in clinical practice and, ultimately, illustrate

  3. Modelling and Decision Support of Clinical Pathways

    Science.gov (United States)

    Gabriel, Roland; Lux, Thomas

    The German health care market is under a rapid rate of change, forcing especially hospitals to provide high-quality services at low costs. Appropriate measures for more effective and efficient service provision are process orientation and decision support by information technology of clinical pathway of a patient. The essential requirements are adequate modelling of clinical pathways as well as usage of adequate systems, which are capable of assisting the complete path of a patient within a hospital, and preferably also outside of it, in a digital way. To fulfil these specifications the authors present a suitable concept, which meets the challenges of well-structured clinical pathways as well as rather poorly structured diagnostic and therapeutic decisions, by interplay of process-oriented and knowledge-based hospital information systems.

  4. A distributed clinical decision support system architecture

    Directory of Open Access Journals (Sweden)

    Shaker H. El-Sappagh

    2014-01-01

    Full Text Available This paper proposes an open and distributed clinical decision support system architecture. This technical architecture takes advantage of Electronic Health Record (EHR, data mining techniques, clinical databases, domain expert knowledge bases, available technologies and standards to provide decision-making support for healthcare professionals. The architecture will work extremely well in distributed EHR environments in which each hospital has its own local EHR, and it satisfies the compatibility, interoperability and scalability objectives of an EHR. The system will also have a set of distributed knowledge bases. Each knowledge base will be specialized in a specific domain (i.e., heart disease, and the model achieves cooperation, integration and interoperability between these knowledge bases. Moreover, the model ensures that all knowledge bases are up-to-date by connecting data mining engines to each local knowledge base. These data mining engines continuously mine EHR databases to extract the most recent knowledge, to standardize it and to add it to the knowledge bases. This framework is expected to improve the quality of healthcare, reducing medical errors and guaranteeing the safety of patients by helping clinicians to make correct, accurate, knowledgeable and timely decisions.

  5. Clinical Decision Support: Statistical Hopes and Challenges

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan; Zvárová, Jana

    2016-01-01

    Roč. 4, č. 1 (2016), s. 30-34 ISSN 1805-8698 Grant - others:Nadační fond na opdporu vědy(CZ) Neuron Institutional support: RVO:67985807 Keywords : decision support * data mining * multivariate statistics * psychiatry * information based medicine Subject RIV: BB - Applied Statistics, Operational Research

  6. Clinical Decision Support Tools: The Evolution of a Revolution

    NARCIS (Netherlands)

    Mould, D. R.; D'Haens, G.; Upton, R. N.

    2016-01-01

    Dashboard systems for clinical decision support integrate data from multiple sources. These systems, the newest in a long line of dose calculators and other decision support tools, utilize Bayesian approaches to fully individualize dosing using information gathered through therapeutic drug

  7. An online infertility clinical decision support system

    Directory of Open Access Journals (Sweden)

    Fabio Diniz de Souza

    2017-01-01

    Full Text Available Objective: To explore some possibilities of computer applications in medicine, and to discuss an online infertility clinical decision support system.Methods: Retrospective data were obtained from 52 couples, and then entered into the online tool. Both its results and the initial diagnoses obtained by the treating physicians were compared with the final diagnoses established by laparoscopy and other diagnostic tests (semen analysis, hormone analysis, endometrial biopsy, ultrasound and hysteroscopy. The initial hypothesis of the research was that the online tool's output was statistically associated with the final diagnoses. In order to verify that hypothesis, a chi-square (χ2 test with Yates' correction for continuity (P<0.05 was performed to verify if the online tool's and the doctor's diagnoses were statistically associated with the final diagnoses.Results: Four etiological factors were present in more than 50% of the couples (ovarian, tubal-peritoneal, uterine, and endometriosis. The statistical results confirmed the research hypothesis for eight out of the nine etiological factors (ovarian, tubal-peritoneal, uterine, cervical, male, vaginal, psychosomatic, and endometriosis; P<0.05. Since there were no cases related to the immune factor in the sample, further clinical data are necessary in order to assess the online tool's performance for that factor.Conclusions: The online tool tends to present more false-positives than false-negatives, whereas the expert physician tends to present more false-negatives than false-positives. Therefore, the online tool and the doctor seem to complement each other. Finally, the obtained results suggest that the infertility online tool discussed herein might be a useful research and instructional tool.

  8. An online infertility clinical decision support system

    Directory of Open Access Journals (Sweden)

    Fabio Diniz de Souza

    2017-09-01

    Full Text Available Objective: To explore some possibilities of computer applications in medicine, and to discuss an online infertility clinical decision support system. Methods: Retrospective data were obtained from 52 couples, and then entered into the online tool. Both its results and the initial diagnoses obtained by the treating physicians were compared with the final diagnoses established by laparoscopy and other diagnostic tests (semen analysis, hormone analysis, endometrial biopsy, ultrasound and hysteroscopy. The initial hypothesis of the research was that the online tool’s output was statistically associated with the final diagnoses. In order to verify that hypothesis, a chi-square (氈2 test with Yates’ correction for continuity (P<0.05 was performed to verify if the online tool’s and the doctor’s diagnoses were statistically associated with the final diagnoses. Results: Four etiological factors were present in more than 50% of the couples (ovarian, tubal-peritoneal, uterine, and endometriosis. The statistical results confirmed the research hypothesis for eight out of the nine etiological factors (ovarian, tubal-peritoneal, uterine, cervical, male, vaginal, psychosomatic, and endometriosis; P<0.05. Since there were no cases related to the immune factor in the sample, further clinical data are necessary in order to assess the online tool’s performance for that factor. Conclusions: The online tool tends to present more false-positives than false negatives, whereas the expert physician tends to present more false-negatives than false-positives. Therefore, the online tool and the doctor seem to complement each other. Finally, the obtained results suggest that the infertility online tool discussed herein might be a useful research and instructional tool.

  9. TUW @ TREC Clinical Decision Support Track

    Science.gov (United States)

    2014-11-01

    and the ShARe/CLEF eHealth Evaluation Lab [8,3] running in 2013 and 2014. Here we briefly describe the goals of the first TREC Clinical Decision...Wendy W. Chapman, David Mart́ınez, Guido Zuccon, and João R. M. Palotti. Overview of the share/clef ehealth evalu- ation lab 2014. In Information Access...Zuccon. Overview of the share/clef ehealth evaluation lab 2013. In Information Access Evaluation. Multilinguality, Multimodality, and Visualization

  10. Nurses' Clinical Decision Making on Adopting a Wound Clinical Decision Support System.

    Science.gov (United States)

    Khong, Peck Chui Betty; Hoi, Shu Yin; Holroyd, Eleanor; Wang, Wenru

    2015-07-01

    Healthcare information technology systems are considered the ideal tool to inculcate evidence-based nursing practices. The wound clinical decision support system was built locally to support nurses to manage pressure ulcer wounds in their daily practice. However, its adoption rate is not optimal. The study's objective was to discover the concepts that informed the RNs' decisions to adopt the wound clinical decision support system as an evidence-based technology in their nursing practice. This was an exploratory, descriptive, and qualitative design using face-to-face interviews, individual interviews, and active participatory observation. A purposive, theoretical sample of 14 RNs was recruited from one of the largest public tertiary hospitals in Singapore after obtaining ethics approval. After consenting, the nurses were interviewed and observed separately. Recruitment stopped when data saturation was reached. All transcribed interview data underwent a concurrent thematic analysis, whereas observational data were content analyzed independently and subsequently triangulated with the interview data. Eight emerging themes were identified, namely, use of the wound clinical decision support system, beliefs in the wound clinical decision support system, influences of the workplace culture, extent of the benefits, professional control over nursing practices, use of knowledge, gut feelings, and emotions (fear, doubt, and frustration). These themes represented the nurses' mental outlook as they made decisions on adopting the wound clinical decision support system in light of the complexities of their roles and workloads. This research has provided insight on the nurses' thoughts regarding their decision to interact with the computer environment in a Singapore context. It captured the nurses' complex thoughts when deciding whether to adopt or reject information technology as they practice in a clinical setting.

  11. Clinical decision support in community pharmacy

    NARCIS (Netherlands)

    Heringa, M.|info:eu-repo/dai/nl/412650207

    2017-01-01

    The occurrence of preventable patient harm caused by drug use has been shown over and over again. Clinical risk management is a concept to prevent patient harm by risk-reducing strategies. It is a systematic approach including risk detection, assessment, management and evaluation. One of the

  12. Grand Challenges in Clinical Decision Support v10

    Science.gov (United States)

    Sittig, Dean F.; Wright, Adam; Osheroff, Jerome A.; Middleton, Blackford; Teich, Jonathan M.; Ash, Joan S.; Campbell, Emily; Bates, David W.

    2008-01-01

    There is a pressing need for high-quality, effective means of designing, developing, presenting, implementing, evaluating, and maintaining all types of clinical decision support capabilities for clinicians, patients and consumers. Using an iterative, consensus-building process we identified a rank-ordered list of the top 10 grand challenges in clinical decision support. This list was created to educate and inspire researchers, developers, funders, and policy-makers. The list of challenges in order of importance that they be solved if patients and organizations are to begin realizing the fullest benefits possible of these systems consists of: Improve the human-computer interface; Disseminate best practices in CDS design, development, and implementation; Summarize patient-level information; Prioritize and filter recommendations to the user; Create an architecture for sharing executable CDS modules and services; Combine recommendations for patients with co-morbidities; Prioritize CDS content development and implementation; Create internet-accessible clinical decision support repositories; Use freetext information to drive clinical decision support; Mine large clinical databases to create new CDS. Identification of solutions to these challenges is critical if clinical decision support is to achieve its potential and improve the quality, safety and efficiency of healthcare. PMID:18029232

  13. The impact of an electronic clinical decision support for pulmonary ...

    African Journals Online (AJOL)

    State-of-the-art electronic radiology workflow can provide clinical decision support (CDS) for specialised imaging requests, but there has been limited work on the clinical impact of CDS in PE, particularly in resource-constrained environments. Objective. To determine the impact of an electronic CDS for PE on the efficiency ...

  14. Improving clinical decision support using data mining techniques

    Science.gov (United States)

    Burn-Thornton, Kath E.; Thorpe, Simon I.

    1999-02-01

    Physicians, in their ever-demanding jobs, are looking to decision support systems for aid in clinical diagnosis. However, clinical decision support systems need to be of sufficiently high accuracy that they help, rather than hinder, the physician in his/her diagnosis. Decision support systems with accuracies, of patient state determination, of greater than 80 percent, are generally perceived to be sufficiently accurate to fulfill the role of helping the physician. We have previously shown that data mining techniques have the potential to provide the underpinning technology for clinical decision support systems. In this paper, an extension of the work in reverence 2, we describe how changes in data mining methodologies, for the analysis of 12-lead ECG data, improve the accuracy by which data mining algorithms determine which patients are suffering from heart disease. We show that the accuracy of patient state prediction, for all the algorithms, which we investigated, can be increased by up to 6 percent, using the combination of appropriate test training ratios and 5-fold cross-validation. The use of cross-validation greater than 5-fold, appears to reduce the improvement in algorithm classification accuracy gained by the use of this validation method. The accuracy of 84 percent in patient state predictions, obtained using the algorithm OCI, suggests that this algorithm will be capable of providing the required accuracy for clinical decision support systems.

  15. Using Clinical Decision Support Software in Health Insurance Company

    Science.gov (United States)

    Konovalov, R.; Kumlander, Deniss

    This paper proposes the idea to use Clinical Decision Support software in Health Insurance Company as a tool to reduce the expenses related to Medication Errors. As a prove that this class of software will help insurance companies reducing the expenses, the research was conducted in eight hospitals in United Arab Emirates to analyze the amount of preventable common Medication Errors in drug prescription.

  16. IBM’s Health Analytics and Clinical Decision Support

    Science.gov (United States)

    Sun, J.; Knoop, S.; Shabo, A.; Carmeli, B.; Sow, D.; Syed-Mahmood, T.; Rapp, W.

    2014-01-01

    Summary Objectives This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making. Methods Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given. Results There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data. Conclusion Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation. PMID:25123736

  17. Real-Time Clinical Decision Support Decreases Inappropriate Plasma Transfusion.

    Science.gov (United States)

    Shah, Neil; Baker, Steven A; Spain, David; Shieh, Lisa; Shepard, John; Hadhazy, Eric; Maggio, Paul; Goodnough, Lawrence T

    2017-08-01

    To curtail inappropriate plasma transfusions, we instituted clinical decision support as an alert upon order entry if the patient's recent international normalized ratio (INR) was 1.7 or less. The alert was suppressed for massive transfusion and within operative or apheresis settings. The plasma order was automatically removed upon alert acceptance while clinical exception reasons allowed for continued transfusion. Alert impact was studied comparing a 7-month control period with a 4-month intervention period. Monthly plasma utilization decreased 17.4%, from a mean ± SD of 3.40 ± 0.48 to 2.82 ± 0.6 plasma units per hundred patient days (95% confidence interval [CI] of difference, -0.1 to 1.3). Plasma transfused below an INR of 1.7 or less decreased from 47.6% to 41.6% (P = .0002; odds ratio, 0.78; 95% CI, 0.69-0.89). The alert recommendation was accepted 33% of the time while clinical exceptions were chosen in the remaining cases (active bleeding, 31%; other clinical indication, 33%; and apheresis, 2%). Alert acceptance rate varied significantly among different provider specialties. Clinical decision support can help curtail inappropriate plasma use but needs to be part of a comprehensive strategy including audit and feedback for comprehensive, long-term changes. © American Society for Clinical Pathology, 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  18. Evaluating online diagnostic decision support tools for the clinical setting.

    Science.gov (United States)

    Pryor, Marie; White, David; Potter, Bronwyn; Traill, Roger

    2012-01-01

    Clinical decision support tools available at the point of care are an effective adjunct to support clinicians to make clinical decisions and improve patient outcomes. We developed a methodology and applied it to evaluate commercially available online clinical diagnostic decision support (DDS) tools for use at the point of care. We identified 11 commercially available DDS tools and assessed these against an evaluation instrument that included 6 categories; general information, content, quality control, search, clinical results and other features. We developed diagnostically challenging clinical case scenarios based on real patient experience that were commonly missed by junior medical staff. The evaluation was divided into 2 phases; an initial evaluation of all identified and accessible DDS tools conducted by the Clinical Information Access Portal (CIAP) team and a second phase that further assessed the top 3 tools identified in the initial evaluation phase. An evaluation panel consisting of senior and junior medical clinicians from NSW Health conducted the second phase. Of the eleven tools that were assessed against the evaluation instrument only 4 tools completely met the DDS definition that was adopted for this evaluation and were able to produce a differential diagnosis. From the initial phase of the evaluation 4 DDS tools scored 70% or more (maximum score 96%) for the content category, 8 tools scored 65% or more (maximum 100%) for the quality control category, 5 tools scored 65% or more (maximum 94%) for the search category, and 4 tools score 70% or more (maximum 81%) for the clinical results category. The second phase of the evaluation was focused on assessing diagnostic accuracy for the top 3 tools identified in the initial phase. Best Practice ranked highest overall against the 6 clinical case scenarios used. Overall the differentiating factor between the top 3 DDS tools was determined by diagnostic accuracy ranking, ease of use and the confidence and

  19. Clinical decision support must be useful, functional is not enough

    DEFF Research Database (Denmark)

    Kortteisto, Tiina; Komulainen, Jorma; Mäkelä, Marjukka

    2012-01-01

    the use of computer-based clinical decision support (eCDS) in primary care and how different professional groups experience it. Our aim was to describe specific reasons for using or not using eCDS among primary care professionals. METHODS: The setting was a Finnish primary health care organization with 48......ABSTRACT: BACKGROUND: Health information technology, particularly electronic decision support systems, can reduce the existing gap between evidence-based knowledge and health care practice but professionals have to accept and use this information. Evidence is scant on which features influence...... professionals receiving patient-specific guidance at the point of care. Multiple data (focus groups, questionnaire and spontaneous feedback) were analyzed using deductive content analysis and descriptive statistics. RESULTS: The content of the guidance is a significant feature of the primary care professional...

  20. Bayesian networks for clinical decision support: A rational approach to dynamic decision-making under uncertainty

    NARCIS (Netherlands)

    Gerven, M.A.J. van

    2007-01-01

    This dissertation deals with decision support in the context of clinical oncology. (Dynamic) Bayesian networks are used as a framework for (dynamic) decision-making under uncertainty and applied to a variety of diagnostic, prognostic, and treatment problems in medicine. It is shown that the proposed

  1. The value of participatory development to support antimicrobial stewardship with a clinical decision support system

    NARCIS (Netherlands)

    Beerlage-de Jong, Nienke; Wentzel, Jobke; Hendrix, Ron; van Gemert-Pijnen, Lisette

    2017-01-01

    Background: Current clinical decision support systems (CDSSs) for antimicrobial stewardship programs (ASPs) are guideline- or expert-driven. They are focused on (clinical) content, not on supporting real-time workflow. Thus, CDSSs fail to optimally support prudent antimicrobial prescribing in daily

  2. The value of participatory development to support antimicrobial stewardship with a clinical decision support system

    NARCIS (Netherlands)

    Beerlage-de Jong, Nienke; Wentzel, M.J.; Hendrix, Ron; van Gemert-Pijnen, Julia E.W.C.

    Background Current clinical decision support systems (CDSSs) for antimicrobial stewardship programs (ASPs) are guideline- or expert-driven. They are focused on (clinical) content, not on supporting real-time workflow. Thus, CDSSs fail to optimally support prudent antimicrobial prescribing in daily

  3. Evaluation of RxNorm for Medication Clinical Decision Support.

    Science.gov (United States)

    Freimuth, Robert R; Wix, Kelly; Zhu, Qian; Siska, Mark; Chute, Christopher G

    2014-01-01

    We evaluated the potential use of RxNorm to provide standardized representations of generic drug name and route of administration to facilitate management of drug lists for clinical decision support (CDS) rules. We found a clear representation of generic drug name but not route of administration. We identified several issues related to data quality, including erroneous or missing defined relationships, and the use of different concept hierarchies to represent the same drug. More importantly, we found extensive semantic precoordination of orthogonal concepts related to route and dose form, which would complicate the use of RxNorm for drug-based CDS. This study demonstrated that while RxNorm is a valuable resource for the standardization of medications used in clinical practice, additional work is required to enhance the terminology so that it can support expanded use cases, such as managing drug lists for CDS.

  4. A Clinical Decision Support System for Breast Cancer Patients

    Science.gov (United States)

    Fernandes, Ana S.; Alves, Pedro; Jarman, Ian H.; Etchells, Terence A.; Fonseca, José M.; Lisboa, Paulo J. G.

    This paper proposes a Web clinical decision support system for clinical oncologists and for breast cancer patients making prognostic assessments, using the particular characteristics of the individual patient. This system comprises three different prognostic modelling methodologies: the clinically widely used Nottingham prognostic index (NPI); the Cox regression modelling and a partial logistic artificial neural network with automatic relevance determination (PLANN-ARD). All three models yield a different prognostic index that can be analysed together in order to obtain a more accurate prognostic assessment of the patient. Missing data is incorporated in the mentioned models, a common issue in medical data that was overcome using multiple imputation techniques. Risk group assignments are also provided through a methodology based on regression trees, where Boolean rules can be obtained expressed with patient characteristics.

  5. Clinical Decision Support Knowledge Management: Strategies for Success.

    Science.gov (United States)

    Khalifa, Mohamed; Alswailem, Osama

    2015-01-01

    Clinical Decision Support Systems have been shown to increase quality of care, patient safety, improve adherence to guidelines for prevention and treatment, and avoid medication errors. Such systems depend mainly on two types of content; the clinical information related to patients and the medical knowledge related to the specialty that informs the system rules and alerts. At King Faisal Specialist Hospital and Research Center, Saudi Arabia, the Health Information Technology Affairs worked on identifying best strategies and recommendations for successful CDSS knowledge management. A review of literature was conducted to identify main areas of challenges and factors of success. A qualitative survey was used over six months' duration to collect opinions, experiences and suggestions from both IT and healthcare professionals. Recommendations were categorized into ten main topics that should be addressed during the development and implementation of CDSS knowledge management tools in the hospital.

  6. Parametric vs. Nonparametric Regression Modelling within Clinical Decision Support

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan; Zvárová, Jana

    2017-01-01

    Roč. 5, č. 1 (2017), s. 21-27 ISSN 1805-8698 R&D Projects: GA ČR GA17-01251S Institutional support: RVO:67985807 Keywords : decision support systems * decision rules * statistical analysis * nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Statistics and probability

  7. Guideline Formalization and Knowledge Representation for Clinical Decision Support

    Directory of Open Access Journals (Sweden)

    Tiago OLIVEIRA

    2012-09-01

    Full Text Available Normal 0 21 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} The prevalence of situations of medical error and defensive medicine in healthcare institutions is a great concern of the medical community. Clinical Practice Guidelines are regarded by most researchers as a way to mitigate theseoccurrences; however, there is a need to make them interactive, easier to update and to deploy. This paper provides a model for Computer-Interpretable Guidelines based on the generic tasks of the clinical process, devised to be included in the framework of a Clinical Decision Support System. Aiming to represent medical recommendations in a simple and intuitive way. Hence, this work proposes a knowledge representation formalism that uses an Extension to Logic Programming to handle incomplete information. This model is used to represent different cases of missing, conflicting and inexact information with the aid of a method to quantify its quality. The integration of the guideline model with the knowledge representation formalism yields a clinical decision model that relies on the development of multiple information scenarios and the exploration of different clinical hypotheses.

  8. Guideline Formalization and Knowledge Representation for Clinical Decision Support

    Directory of Open Access Journals (Sweden)

    Paulo NOVAIS

    2013-07-01

    Full Text Available Normal 0 21 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} The prevalence of situations of medical error and defensive medicine in healthcare institutions is a great concern of the medical community. Clinical Practice Guidelines are regarded by most researchers as a way to mitigate these occurrences; however, there is a need to make them interactive, easier to update and to deploy. This paper provides a model for Computer-Interpretable Guidelines based on the generic tasks of the clinical process, devised to be included in the framework of a Clinical Decision Support System. Aiming to represent medical recommendations in a simple and intuitive way. Hence, this work proposes a knowledge representation formalism that uses an Extension to Logic Programming to handle incomplete information. This model is used to represent different cases of missing, conflicting and inexact information with the aid of a method to quantify its quality. The integration of the guideline model with the knowledge representation formalism yields a clinical decision model that relies on the development of multiple information scenarios and the exploration of different clinical hypotheses.

  9. Duplicate laboratory test reduction using a clinical decision support tool.

    Science.gov (United States)

    Procop, Gary W; Yerian, Lisa M; Wyllie, Robert; Harrison, A Marc; Kottke-Marchant, Kandice

    2014-05-01

    Duplicate laboratory tests that are unwarranted increase unnecessary phlebotomy, which contributes to iatrogenic anemia, decreased patient satisfaction, and increased health care costs. We employed a clinical decision support tool (CDST) to block unnecessary duplicate test orders during the computerized physician order entry (CPOE) process. We assessed laboratory cost savings after 2 years and searched for untoward patient events associated with this intervention. This CDST blocked 11,790 unnecessary duplicate test orders in these 2 years, which resulted in a cost savings of $183,586. There were no untoward effects reported associated with this intervention. The movement to CPOE affords real-time interaction between the laboratory and the physician through CDSTs that signal duplicate orders. These interactions save health care dollars and should also increase patient satisfaction and well-being.

  10. SANDS: an architecture for clinical decision support in a National Health Information Network.

    Science.gov (United States)

    Wright, Adam; Sittig, Dean F

    2007-10-11

    A new architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support) is introduced and its performance evaluated. The architecture provides a method for performing clinical decision support across a network, as in a health information exchange. Using the prototype we demonstrated that, first, a number of useful types of decision support can be carried out using our architecture; and, second, that the architecture exhibits desirable reliability and performance characteristics.

  11. Creating and sharing clinical decision support content with Web 2.0: Issues and examples.

    Science.gov (United States)

    Wright, Adam; Bates, David W; Middleton, Blackford; Hongsermeier, Tonya; Kashyap, Vipul; Thomas, Sean M; Sittig, Dean F

    2009-04-01

    Clinical decision support is a powerful tool for improving healthcare quality and patient safety. However, developing a comprehensive package of decision support interventions is costly and difficult. If used well, Web 2.0 methods may make it easier and less costly to develop decision support. Web 2.0 is characterized by online communities, open sharing, interactivity and collaboration. Although most previous attempts at sharing clinical decision support content have worked outside of the Web 2.0 framework, several initiatives are beginning to use Web 2.0 to share and collaborate on decision support content. We present case studies of three efforts: the Clinfowiki, a world-accessible wiki for developing decision support content; Partners Healthcare eRooms, web-based tools for developing decision support within a single organization; and Epic Systems Corporation's Community Library, a repository for sharing decision support content for customers of a single clinical system vendor. We evaluate the potential of Web 2.0 technologies to enable collaborative development and sharing of clinical decision support systems through the lens of three case studies; analyzing technical, legal and organizational issues for developers, consumers and organizers of clinical decision support content in Web 2.0. We believe the case for Web 2.0 as a tool for collaborating on clinical decision support content appears strong, particularly for collaborative content development within an organization.

  12. Impact of a Clinical Decision Support System on Pharmacy Clinical Interventions, Documentation Efforts, and Costs

    OpenAIRE

    Calloway, Stacy; Akilo, Hameed A.; Bierman, Kyle

    2013-01-01

    Health care organizations are turning to electronic clinical decision support systems (CDSSs) to increase quality of patient care and promote a safer environment. A CDSS is a promising approach to the aggregation and use of patient data to identify patients who would most benefit from interventions by pharmacy clinicians. However, there are limited published reports describing the impact of CDSS on clinical pharmacy measures. In February 2011, Good Shepherd Medical Center, a 425-bed acute car...

  13. [Knowledge management system for laboratory work and clinical decision support].

    Science.gov (United States)

    Inada, Masanori; Sato, Mayumi; Yoneyama, Akiko

    2011-05-01

    This paper discusses a knowledge management system for clinical laboratories. In the clinical laboratory of Toranomon Hospital, we receive about 20 questions relevant to laboratory tests per day from medical doctors or co-medical staff. These questions mostly involve the essence to appropriately accomplish laboratory tests. We have to answer them carefully and suitably because an incorrect answer may cause a medical accident. Up to now, no method has been in place to achieve a rapid response and standardized answers. For this reason, the laboratory staff have responded to various questions based on their individual knowledge. We began to develop a knowledge management system to promote the knowledge of staff working for the laboratory. This system is a type of knowledge base for assisting the work, such as inquiry management, laboratory consultation, process management, and clinical support. It consists of several functions: guiding laboratory test information, managing inquiries from medical staff, reporting results of patient consultation, distributing laboratory staffs notes, and recording guidelines for laboratory medicine. The laboratory test information guide has 2,000 records of medical test information registered in the database with flexible retrieval. The inquiry management tool provides a methos to record all questions, answer easily, and retrieve cases. It helps staff to respond appropriately in a short period of time. The consulting report system treats patients' claims regarding medical tests. The laboratory staffs notes enter a file management system so they can be accessed to aid in clinical support. Knowledge sharing using this function can achieve the transition from individual to organizational learning. Storing guidelines for laboratory medicine will support EBM. Finally, it is expected that this system will support intellectual activity concerning laboratory work and contribute to the practice of knowledge management for clinical work support.

  14. Pregnancy outcomes in Ghana : Relavance of clinical decision making support tools for frontline providers of care

    OpenAIRE

    Amoakoh-Coleman, M.

    2016-01-01

    Ghana’s slow progress towards attaining millennium development goal 5 has been associated with gaps in quality of care, particularly quality of clinical decision making for clients. This thesis reviews the relevance and effect of clinical decision making support tools on pregnancy outcomes. Relevance of three clinical decision making support tools available to frontline providers of care in the Greater Accra region is discussed. These are routine maternal health service delivery data populati...

  15. Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design.

    Science.gov (United States)

    Islam, Roosan; Weir, Charlene R; Jones, Makoto; Del Fiol, Guilherme; Samore, Matthew H

    2015-11-30

    Clinical experts' cognitive mechanisms for managing complexity have implications for the design of future innovative healthcare systems. The purpose of the study is to examine the constituents of decision complexity and explore the cognitive strategies clinicians use to control and adapt to their information environment. We used Cognitive Task Analysis (CTA) methods to interview 10 Infectious Disease (ID) experts at the University of Utah and Salt Lake City Veterans Administration Medical Center. Participants were asked to recall a complex, critical and vivid antibiotic-prescribing incident using the Critical Decision Method (CDM), a type of Cognitive Task Analysis (CTA). Using the four iterations of the Critical Decision Method, questions were posed to fully explore the incident, focusing in depth on the clinical components underlying the complexity. Probes were included to assess cognitive and decision strategies used by participants. The following three themes emerged as the constituents of decision complexity experienced by the Infectious Diseases experts: 1) the overall clinical picture does not match the pattern, 2) a lack of comprehension of the situation and 3) dealing with social and emotional pressures such as fear and anxiety. All these factors contribute to decision complexity. These factors almost always occurred together, creating unexpected events and uncertainty in clinical reasoning. Five themes emerged in the analyses of how experts deal with the complexity. Expert clinicians frequently used 1) watchful waiting instead of over- prescribing antibiotics, engaged in 2) theory of mind to project and simulate other practitioners' perspectives, reduced very complex cases into simple 3) heuristics, employed 4) anticipatory thinking to plan and re-plan events and consulted with peers to share knowledge, solicit opinions and 5) seek help on patient cases. The cognitive strategies to deal with decision complexity found in this study have important

  16. SANDS: a service-oriented architecture for clinical decision support in a National Health Information Network.

    Science.gov (United States)

    Wright, Adam; Sittig, Dean F

    2008-12-01

    In this paper, we describe and evaluate a new distributed architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support), which leverages current health information exchange efforts and is based on the principles of a service-oriented architecture. The architecture allows disparate clinical information systems and clinical decision support systems to be seamlessly integrated over a network according to a set of interfaces and protocols described in this paper. The architecture described is fully defined and developed, and six use cases have been developed and tested using a prototype electronic health record which links to one of the existing prototype National Health Information Networks (NHIN): drug interaction checking, syndromic surveillance, diagnostic decision support, inappropriate prescribing in older adults, information at the point of care and a simple personal health record. Some of these use cases utilize existing decision support systems, which are either commercially or freely available at present, and developed outside of the SANDS project, while other use cases are based on decision support systems developed specifically for the project. Open source code for many of these components is available, and an open source reference parser is also available for comparison and testing of other clinical information systems and clinical decision support systems that wish to implement the SANDS architecture. The SANDS architecture for decision support has several significant advantages over other architectures for clinical decision support. The most salient of these are:

  17. Clinical Decision Support to Implement CYP2D6 Drug-Gene Interaction.

    Science.gov (United States)

    Caraballo, Pedro J; Parkulo, Mark; Blair, David; Elliott, Michelle; Schultz, Cloann; Sutton, Joseph; Rao, Padma; Bruflat, Jamie; Bleimeyer, Robert; Crooks, John; Gabrielson, Donald; Nicholson, Wayne; Rohrer Vitek, Carolyn; Wix, Kelly; Bielinski, Suzette J; Pathak, Jyotishman; Kullo, Iftikhar

    2015-01-01

    The level of CYP2D6 metabolic activity can be predicted by pharmacogenomic testing, and concomitant use of clinical decision support has the potential to prevent adverse effects from those drugs metabolized by this enzyme. Our initial findings after implementation of clinical decision support alerts integrated in the electronic health records suggest high feasibility, but also identify important challenges.

  18. Implementation of workflow engine technology to deliver basic clinical decision support functionality.

    Science.gov (United States)

    Huser, Vojtech; Rasmussen, Luke V; Oberg, Ryan; Starren, Justin B

    2011-04-10

    Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. We describe an implementation of a free workflow technology

  19. Implementation of workflow engine technology to deliver basic clinical decision support functionality

    Science.gov (United States)

    2011-01-01

    Background Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. Results We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. Conclusions We describe an implementation of

  20. Encounter Decision Aid vs. Clinical Decision Support or Usual Care to Support Patient-Centered Treatment Decisions in Osteoporosis: The Osteoporosis Choice Randomized Trial II.

    Directory of Open Access Journals (Sweden)

    Annie LeBlanc

    Full Text Available Osteoporosis Choice, an encounter decision aid, can engage patients and clinicians in shared decision making about osteoporosis treatment. Its effectiveness compared to the routine provision to clinicians of the patient's estimated risk of fracture using the FRAX calculator is unknown.Patient-level, randomized, three-arm trial enrolling women over 50 with osteopenia or osteoporosis eligible for treatment with bisphosphonates, where the use of Osteoporosis Choice was compared to FRAX only and to usual care to determine impact on patient knowledge, decisional conflict, involvement in the decision-making process, decision to start and adherence to bisphosphonates.We enrolled 79 women in the three arms. Because FRAX estimation alone and usual care produced similar results, we grouped them for analysis. Compared to these, use of Osteoporosis Choice increased patient knowledge (median score 6 vs. 4, p = .01, improved understanding of fracture risk and risk reduction with bisphosphonates (p = .01 and p<.0001, respectively, had no effect on decision conflict, and increased patient engagement in the decision making process (OPTION scores 57% vs. 43%, p = .001. Encounters with the decision aid were 0.8 minutes longer (range: 33 minutes shorter to 3.0 minutes longer. There were twice as many patients receiving and filling prescriptions in the decision aid arm (83% vs. 40%, p = .07; medication adherence at 6 months was no different across arms.Supporting both patients and clinicians during the clinical encounter with the Osteoporosis Choice decision aid efficiently improves treatment decision making when compared to usual care with or without clinical decision support with FRAX results.clinical trials.gov NCT00949611.

  1. Pregnancy outcomes in Ghana : Relavance of clinical decision making support tools for frontline providers of care

    NARCIS (Netherlands)

    Amoakoh-Coleman, M.

    2016-01-01

    Ghana’s slow progress towards attaining millennium development goal 5 has been associated with gaps in quality of care, particularly quality of clinical decision making for clients. This thesis reviews the relevance and effect of clinical decision making support tools on pregnancy

  2. Fuzzy logic in clinical practice decision support systems

    NARCIS (Netherlands)

    Warren, Jim; Beliakov, Gleb; van der Zwaag, B.J.

    Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or

  3. User-centered design to improve clinical decision support in primary care.

    Science.gov (United States)

    Brunner, Julian; Chuang, Emmeline; Goldzweig, Caroline; Cain, Cindy L; Sugar, Catherine; Yano, Elizabeth M

    2017-08-01

    A growing literature has demonstrated the ability of user-centered design to make clinical decision support systems more effective and easier to use. However, studies of user-centered design have rarely examined more than a handful of sites at a time, and have frequently neglected the implementation climate and organizational resources that influence clinical decision support. The inclusion of such factors was identified by a systematic review as "the most important improvement that can be made in health IT evaluations." (1) Identify the prevalence of four user-centered design practices at United States Veterans Affairs (VA) primary care clinics and assess the perceived utility of clinical decision support at those clinics; (2) Evaluate the association between those user-centered design practices and the perceived utility of clinical decision support. We analyzed clinic-level survey data collected in 2006-2007 from 170 VA primary care clinics. We examined four user-centered design practices: 1) pilot testing, 2) provider satisfaction assessment, 3) formal usability assessment, and 4) analysis of impact on performance improvement. We used a regression model to evaluate the association between user-centered design practices and the perceived utility of clinical decision support, while accounting for other important factors at those clinics, including implementation climate, available resources, and structural characteristics. We also examined associations separately at community-based clinics and at hospital-based clinics. User-centered design practices for clinical decision support varied across clinics: 74% conducted pilot testing, 62% conducted provider satisfaction assessment, 36% conducted a formal usability assessment, and 79% conducted an analysis of impact on performance improvement. Overall perceived utility of clinical decision support was high, with a mean rating of 4.17 (±.67) out of 5 on a composite measure. "Analysis of impact on performance

  4. Acceptance of clinical decision support surveillance technology in the clinical pharmacy.

    Science.gov (United States)

    English, Dan; Ankem, Kalyani; English, Kathleen

    2017-03-01

    There are clinical and economic benefits to incorporating clinical decision support systems (CDSSs) in patient care interventions in the clinical pharmacy setting. However, user dissatisfaction and resistance to HIT can prevent optimal use of such systems, particularly when users employ system workarounds and overrides. The present study applied a modified version of the unified theory of acceptance and use of technology (UTAUT) to evaluate the disposition and satisfaction with CDSS among clinical pharmacists who perform surveillance to identify potential medication therapy interventions on patients in the hospital setting. A survey of clinical pharmacists (N = 48) was conducted. Partial least squares (PLS) regression was used to analyze the influence of the UTAUT-related variables on behavioral intention and satisfaction with CDSS among clinical pharmacists. While behavioral intention did not predict actual use of HIT, facilitating conditions had a direct effect on pharmacists' use of CDSS. Likewise, satisfaction with CDSS was found to have a direct effect on use, with more satisfied users being less inclined to employ workarounds or overrides of the system. Based on the findings, organizational structures that facilitate CDSS use and user satisfaction affect the extent to which pharmacy and health care management maximize use in the clinical pharmacy setting.

  5. A Proposed Clinical Decision Support Architecture Capable of Supporting Whole Genome Sequence Information

    Directory of Open Access Journals (Sweden)

    Brandon M. Welch

    2014-04-01

    Full Text Available Whole genome sequence (WGS information may soon be widely available to help clinicians personalize the care and treatment of patients. However, considerable barriers exist, which may hinder the effective utilization of WGS information in a routine clinical care setting. Clinical decision support (CDS offers a potential solution to overcome such barriers and to facilitate the effective use of WGS information in the clinic. However, genomic information is complex and will require significant considerations when developing CDS capabilities. As such, this manuscript lays out a conceptual framework for a CDS architecture designed to deliver WGS-guided CDS within the clinical workflow. To handle the complexity and breadth of WGS information, the proposed CDS framework leverages service-oriented capabilities and orchestrates the interaction of several independently-managed components. These independently-managed components include the genome variant knowledge base, the genome database, the CDS knowledge base, a CDS controller and the electronic health record (EHR. A key design feature is that genome data can be stored separately from the EHR. This paper describes in detail: (1 each component of the architecture; (2 the interaction of the components; and (3 how the architecture attempts to overcome the challenges associated with WGS information. We believe that service-oriented CDS capabilities will be essential to using WGS information for personalized medicine.

  6. A proposed clinical decision support architecture capable of supporting whole genome sequence information.

    Science.gov (United States)

    Welch, Brandon M; Loya, Salvador Rodriguez; Eilbeck, Karen; Kawamoto, Kensaku

    2014-04-04

    Whole genome sequence (WGS) information may soon be widely available to help clinicians personalize the care and treatment of patients. However, considerable barriers exist, which may hinder the effective utilization of WGS information in a routine clinical care setting. Clinical decision support (CDS) offers a potential solution to overcome such barriers and to facilitate the effective use of WGS information in the clinic. However, genomic information is complex and will require significant considerations when developing CDS capabilities. As such, this manuscript lays out a conceptual framework for a CDS architecture designed to deliver WGS-guided CDS within the clinical workflow. To handle the complexity and breadth of WGS information, the proposed CDS framework leverages service-oriented capabilities and orchestrates the interaction of several independently-managed components. These independently-managed components include the genome variant knowledge base, the genome database, the CDS knowledge base, a CDS controller and the electronic health record (EHR). A key design feature is that genome data can be stored separately from the EHR. This paper describes in detail: (1) each component of the architecture; (2) the interaction of the components; and (3) how the architecture attempts to overcome the challenges associated with WGS information. We believe that service-oriented CDS capabilities will be essential to using WGS information for personalized medicine.

  7. Ontology-based clinical decision support system applied on diabetes

    OpenAIRE

    Mukabunani, Alphonsine

    2017-01-01

    Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Medical diagnosis is a multi-step process which is complex as it requires the consideration of many factors. Additionally, the accuracy of diagnosis varies depending on the skill and knowledge a physician has in the medical field. Using ICT solution, the physicians can be assisted so that they can make an accurate decision. Many applications have been developed to enhance physician performance and i...

  8. The impact of an electronic clinical decision support for pulmonary ...

    African Journals Online (AJOL)

    emergency unit were positive for PE. ... technology. ... 1 Division of Radiodiagnosis, Department of Medical Imaging and Clinical Oncology, Faculty of Medicine and ... Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa.

  9. Factors Predicting Oncology Care Providers' Behavioral Intention to Adopt Clinical Decision Support Systems

    Science.gov (United States)

    Wolfenden, Andrew

    2012-01-01

    The purpose of this quantitative correlation study was to examine the predictors of user behavioral intention on the decision of oncology care providers to adopt or reject the clinical decision support system. The Unified Theory of Acceptance and Use of Technology (UTAUT) formed the foundation of the research model and survey instrument. The…

  10. Information management to enable personalized medicine: stakeholder roles in building clinical decision support.

    Science.gov (United States)

    Downing, Gregory J; Boyle, Scott N; Brinner, Kristin M; Osheroff, Jerome A

    2009-10-08

    Advances in technology and the scientific understanding of disease processes are presenting new opportunities to improve health through individualized approaches to patient management referred to as personalized medicine. Future health care strategies that deploy genomic technologies and molecular therapies will bring opportunities to prevent, predict, and pre-empt disease processes but will be dependent on knowledge management capabilities for health care providers that are not currently available. A key cornerstone to the potential application of this knowledge will be effective use of electronic health records. In particular, appropriate clinical use of genomic test results and molecularly-targeted therapies present important challenges in patient management that can be effectively addressed using electronic clinical decision support technologies. Approaches to shaping future health information needs for personalized medicine were undertaken by a work group of the American Health Information Community. A needs assessment for clinical decision support in electronic health record systems to support personalized medical practices was conducted to guide health future development activities. Further, a suggested action plan was developed for government, researchers and research institutions, developers of electronic information tools (including clinical guidelines, and quality measures), and standards development organizations to meet the needs for personalized approaches to medical practice. In this article, we focus these activities on stakeholder organizations as an operational framework to help identify and coordinate needs and opportunities for clinical decision support tools to enable personalized medicine. This perspective addresses conceptual approaches that can be undertaken to develop and apply clinical decision support in electronic health record systems to achieve personalized medical care. In addition, to represent meaningful benefits to personalized

  11. Information management to enable personalized medicine: stakeholder roles in building clinical decision support

    Directory of Open Access Journals (Sweden)

    Brinner Kristin M

    2009-10-01

    Full Text Available Abstract Background Advances in technology and the scientific understanding of disease processes are presenting new opportunities to improve health through individualized approaches to patient management referred to as personalized medicine. Future health care strategies that deploy genomic technologies and molecular therapies will bring opportunities to prevent, predict, and pre-empt disease processes but will be dependent on knowledge management capabilities for health care providers that are not currently available. A key cornerstone to the potential application of this knowledge will be effective use of electronic health records. In particular, appropriate clinical use of genomic test results and molecularly-targeted therapies present important challenges in patient management that can be effectively addressed using electronic clinical decision support technologies. Discussion Approaches to shaping future health information needs for personalized medicine were undertaken by a work group of the American Health Information Community. A needs assessment for clinical decision support in electronic health record systems to support personalized medical practices was conducted to guide health future development activities. Further, a suggested action plan was developed for government, researchers and research institutions, developers of electronic information tools (including clinical guidelines, and quality measures, and standards development organizations to meet the needs for personalized approaches to medical practice. In this article, we focus these activities on stakeholder organizations as an operational framework to help identify and coordinate needs and opportunities for clinical decision support tools to enable personalized medicine. Summary This perspective addresses conceptual approaches that can be undertaken to develop and apply clinical decision support in electronic health record systems to achieve personalized medical care. In

  12. Pilot study of a point-of-use decision support tool for cancer clinical trials eligibility.

    Science.gov (United States)

    Breitfeld, P P; Weisburd, M; Overhage, J M; Sledge, G; Tierney, W M

    1999-01-01

    Many adults with cancer are not enrolled in clinical trials because caregivers do not have the time to match the patient's clinical findings with varying eligibility criteria associated with multiple trials for which the patient might be eligible. The authors developed a point-of-use portable decision support tool (DS-TRIEL) to automate this matching process. The support tool consists of a hand-held computer with a programmable relational database. A two-level hierarchic decision framework was used for the identification of eligible subjects for two open breast cancer clinical trials. The hand-held computer also provides protocol consent forms and schemas to further help the busy oncologist. This decision support tool and the decision framework on which it is based could be used for multiple trials and different cancer sites.

  13. Computer Decision Support to Improve Autism Screening and Care in Community Pediatric Clinics

    Science.gov (United States)

    Bauer, Nerissa S.; Sturm, Lynne A.; Carroll, Aaron E.; Downs, Stephen M.

    2013-01-01

    An autism module was added to an existing computer decision support system (CDSS) to facilitate adherence to recommended guidelines for screening for autism spectrum disorders in primary care pediatric clinics. User satisfaction was assessed by survey and informal feedback at monthly meetings between clinical staff and the software team. To assess…

  14. A pilot study of distributed knowledge management and clinical decision support in the cloud.

    Science.gov (United States)

    Dixon, Brian E; Simonaitis, Linas; Goldberg, Howard S; Paterno, Marilyn D; Schaeffer, Molly; Hongsermeier, Tonya; Wright, Adam; Middleton, Blackford

    2013-09-01

    Implement and perform pilot testing of web-based clinical decision support services using a novel framework for creating and managing clinical knowledge in a distributed fashion using the cloud. The pilot sought to (1) develop and test connectivity to an external clinical decision support (CDS) service, (2) assess the exchange of data to and knowledge from the external CDS service, and (3) capture lessons to guide expansion to more practice sites and users. The Clinical Decision Support Consortium created a repository of shared CDS knowledge for managing hypertension, diabetes, and coronary artery disease in a community cloud hosted by Partners HealthCare. A limited data set for primary care patients at a separate health system was securely transmitted to a CDS rules engine hosted in the cloud. Preventive care reminders triggered by the limited data set were returned for display to clinician end users for review and display. During a pilot study, we (1) monitored connectivity and system performance, (2) studied the exchange of data and decision support reminders between the two health systems, and (3) captured lessons. During the six month pilot study, there were 1339 patient encounters in which information was successfully exchanged. Preventive care reminders were displayed during 57% of patient visits, most often reminding physicians to monitor blood pressure for hypertensive patients (29%) and order eye exams for patients with diabetes (28%). Lessons learned were grouped into five themes: performance, governance, semantic interoperability, ongoing adjustments, and usability. Remote, asynchronous cloud-based decision support performed reasonably well, although issues concerning governance, semantic interoperability, and usability remain key challenges for successful adoption and use of cloud-based CDS that will require collaboration between biomedical informatics and computer science disciplines. Decision support in the cloud is feasible and may be a reasonable

  15. Knowledge bases, clinical decision support systems, and rapid learning in oncology.

    Science.gov (United States)

    Yu, Peter Paul

    2015-03-01

    One of the most important benefits of health information technology is to assist the cognitive process of the human mind in the face of vast amounts of health data, limited time for decision making, and the complexity of the patient with cancer. Clinical decision support tools are frequently cited as a technologic solution to this problem, but to date useful clinical decision support systems (CDSS) have been limited in utility and implementation. This article describes three unique sources of health data that underlie fundamentally different types of knowledge bases which feed into CDSS. CDSS themselves comprise a variety of models which are discussed. The relationship of knowledge bases and CDSS to rapid learning health systems design is critical as CDSS are essential drivers of rapid learning in clinical care. Copyright © 2015 by American Society of Clinical Oncology.

  16. Big Data as a Driver for Clinical Decision Support Systems: A Learning Health Systems Perspective

    Directory of Open Access Journals (Sweden)

    Arianna Dagliati

    2018-05-01

    Full Text Available Big data technologies are nowadays providing health care with powerful instruments to gather and analyze large volumes of heterogeneous data collected for different purposes, including clinical care, administration, and research. This makes possible to design IT infrastructures that favor the implementation of the so-called “Learning Healthcare System Cycle,” where healthcare practice and research are part of a unique and synergic process. In this paper we highlight how “Big Data enabled” integrated data collections may support clinical decision-making together with biomedical research. Two effective implementations are reported, concerning decision support in Diabetes and in Inherited Arrhythmogenic Diseases.

  17. Developing an Interactive Data Visualization Tool to Assess the Impact of Decision Support on Clinical Operations.

    Science.gov (United States)

    Huber, Timothy C; Krishnaraj, Arun; Monaghan, Dayna; Gaskin, Cree M

    2018-05-18

    Due to mandates from recent legislation, clinical decision support (CDS) software is being adopted by radiology practices across the country. This software provides imaging study decision support for referring providers at the point of order entry. CDS systems produce a large volume of data, providing opportunities for research and quality improvement. In order to better visualize and analyze trends in this data, an interactive data visualization dashboard was created using a commercially available data visualization platform. Following the integration of a commercially available clinical decision support product into the electronic health record, a dashboard was created using a commercially available data visualization platform (Tableau, Seattle, WA). Data generated by the CDS were exported from the data warehouse, where they were stored, into the platform. This allowed for real-time visualization of the data generated by the decision support software. The creation of the dashboard allowed the output from the CDS platform to be more easily analyzed and facilitated hypothesis generation. Integrating data visualization tools into clinical decision support tools allows for easier data analysis and can streamline research and quality improvement efforts.

  18. GELLO: an object-oriented query and expression language for clinical decision support.

    Science.gov (United States)

    Sordo, Margarita; Ogunyemi, Omolola; Boxwala, Aziz A; Greenes, Robert A

    2003-01-01

    GELLO is a purpose-specific, object-oriented (OO) query and expression language. GELLO is the result of a concerted effort of the Decision Systems Group (DSG) working with the HL7 Clinical Decision Support Technical Committee (CDSTC) to provide the HL7 community with a common format for data encoding and manipulation. GELLO will soon be submitted for ballot to the HL7 CDSTC for consideration as a standard.

  19. Group Decision Process Support

    DEFF Research Database (Denmark)

    Gøtze, John; Hijikata, Masao

    1997-01-01

    Introducing the notion of Group Decision Process Support Systems (GDPSS) to traditional decision-support theorists.......Introducing the notion of Group Decision Process Support Systems (GDPSS) to traditional decision-support theorists....

  20. Towards meaningful medication-related clinical decision support: recommendations for an initial implementation.

    Science.gov (United States)

    Phansalkar, S; Wright, A; Kuperman, G J; Vaida, A J; Bobb, A M; Jenders, R A; Payne, T H; Halamka, J; Bloomrosen, M; Bates, D W

    2011-01-01

    Clinical decision support (CDS) can improve safety, quality, and cost-effectiveness of patient care, especially when implemented in computerized provider order entry (CPOE) applications. Medication-related decision support logic forms a large component of the CDS logic in any CPOE system. However, organizations wishing to implement CDS must either purchase the computable clinical content or develop it themselves. Content provided by vendors does not always meet local expectations. Most organizations lack the resources to customize the clinical content and the expertise to implement it effectively. In this paper, we describe the recommendations of a national expert panel on two basic medication-related CDS areas, specifically, drug-drug interaction (DDI) checking and duplicate therapy checking. The goals of this study were to define a starter set of medication-related alerts that healthcare organizations can implement in their clinical information systems. We also draw on the experiences of diverse institutions to highlight the realities of implementing medication decision support. These findings represent the experiences of institutions with a long history in the domain of medication decision support, and the hope is that this guidance may improve the feasibility and efficiency CDS adoption across healthcare settings.

  1. Doing the right things and doing things right : inpatient drug surveillance assisted by clinical decision support

    NARCIS (Netherlands)

    Helmons, Pieter J.; Suijkerbuijk, Bas O.; Nannan Panday, Prashant V.; Kosterink, Jos G. W.

    Increased budget constraints and a continuous focus on improved quality require an efficient inpatient drug surveillance process. We describe a hospital-wide drug surveillance strategy consisting of a multidisciplinary evaluation of drug surveillance activities and using clinical decision support to

  2. Mobile clinical decision support systems and applications: a literature and commercial review.

    Science.gov (United States)

    Martínez-Pérez, Borja; de la Torre-Díez, Isabel; López-Coronado, Miguel; Sainz-de-Abajo, Beatriz; Robles, Montserrat; García-Gómez, Juan Miguel

    2014-01-01

    The latest advances in eHealth and mHealth have propitiated the rapidly creation and expansion of mobile applications for health care. One of these types of applications are the clinical decision support systems, which nowadays are being implemented in mobile apps to facilitate the access to health care professionals in their daily clinical decisions. The aim of this paper is twofold. Firstly, to make a review of the current systems available in the literature and in commercial stores. Secondly, to analyze a sample of applications in order to obtain some conclusions and recommendations. Two reviews have been done: a literature review on Scopus, IEEE Xplore, Web of Knowledge and PubMed and a commercial review on Google play and the App Store. Five applications from each review have been selected to develop an in-depth analysis and to obtain more information about the mobile clinical decision support systems. Ninety-two relevant papers and 192 commercial apps were found. Forty-four papers were focused only on mobile clinical decision support systems. One hundred seventy-one apps were available on Google play and 21 on the App Store. The apps are designed for general medicine and 37 different specialties, with some features common in all of them despite of the different medical fields objective. The number of mobile clinical decision support applications and their inclusion in clinical practices has risen in the last years. However, developers must be careful with their interface or the easiness of use, which can impoverish the experience of the users.

  3. Clinical decision support for whole genome sequence information leveraging a service-oriented architecture: a prototype.

    Science.gov (United States)

    Welch, Brandon M; Rodriguez-Loya, Salvador; Eilbeck, Karen; Kawamoto, Kensaku

    2014-01-01

    Whole genome sequence (WGS) information could soon be routinely available to clinicians to support the personalized care of their patients. At such time, clinical decision support (CDS) integrated into the clinical workflow will likely be necessary to support genome-guided clinical care. Nevertheless, developing CDS capabilities for WGS information presents many unique challenges that need to be overcome for such approaches to be effective. In this manuscript, we describe the development of a prototype CDS system that is capable of providing genome-guided CDS at the point of care and within the clinical workflow. To demonstrate the functionality of this prototype, we implemented a clinical scenario of a hypothetical patient at high risk for Lynch Syndrome based on his genomic information. We demonstrate that this system can effectively use service-oriented architecture principles and standards-based components to deliver point of care CDS for WGS information in real-time.

  4. A service oriented approach for guidelines-based clinical decision support using BPMN.

    Science.gov (United States)

    Rodriguez-Loya, Salvador; Aziz, Ayesha; Chatwin, Chris

    2014-01-01

    Evidence-based medical practice requires that clinical guidelines need to be documented in such a way that they represent a clinical workflow in its most accessible form. In order to optimize clinical processes to improve clinical outcomes, we propose a Service Oriented Architecture (SOA) based approach for implementing clinical guidelines that can be accessed from an Electronic Health Record (EHR) application with a Web Services enabled communication mechanism with the Enterprise Service Bus. We have used Business Process Modelling Notation (BPMN) for modelling and presenting the clinical pathway in the form of a workflow. The aim of this study is to produce spontaneous alerts in the healthcare workflow in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD). The use of BPMN as a tool to automate clinical guidelines has not been previously employed for providing Clinical Decision Support (CDS).

  5. Development of a clinical decision support system for diabetes care: A pilot study.

    Directory of Open Access Journals (Sweden)

    Livvi Li Wei Sim

    Full Text Available Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require

  6. Using Best Practices to Extract, Organize, and Reuse Embedded Decision Support Content Knowledge Rules from Mature Clinical Systems.

    Science.gov (United States)

    DesAutels, Spencer J; Fox, Zachary E; Giuse, Dario A; Williams, Annette M; Kou, Qing-Hua; Weitkamp, Asli; Neal R, Patel; Bettinsoli Giuse, Nunzia

    2016-01-01

    Clinical decision support (CDS) knowledge, embedded over time in mature medical systems, presents an interesting and complex opportunity for information organization, maintenance, and reuse. To have a holistic view of all decision support requires an in-depth understanding of each clinical system as well as expert knowledge of the latest evidence. This approach to clinical decision support presents an opportunity to unify and externalize the knowledge within rules-based decision support. Driven by an institutional need to prioritize decision support content for migration to new clinical systems, the Center for Knowledge Management and Health Information Technology teams applied their unique expertise to extract content from individual systems, organize it through a single extensible schema, and present it for discovery and reuse through a newly created Clinical Support Knowledge Acquisition and Archival Tool (CS-KAAT). CS-KAAT can build and maintain the underlying knowledge infrastructure needed by clinical systems.

  7. Using Best Practices to Extract, Organize, and Reuse Embedded Decision Support Content Knowledge Rules from Mature Clinical Systems

    Science.gov (United States)

    DesAutels, Spencer J.; Fox, Zachary E.; Giuse, Dario A.; Williams, Annette M.; Kou, Qing-hua; Weitkamp, Asli; Neal R, Patel; Bettinsoli Giuse, Nunzia

    2016-01-01

    Clinical decision support (CDS) knowledge, embedded over time in mature medical systems, presents an interesting and complex opportunity for information organization, maintenance, and reuse. To have a holistic view of all decision support requires an in-depth understanding of each clinical system as well as expert knowledge of the latest evidence. This approach to clinical decision support presents an opportunity to unify and externalize the knowledge within rules-based decision support. Driven by an institutional need to prioritize decision support content for migration to new clinical systems, the Center for Knowledge Management and Health Information Technology teams applied their unique expertise to extract content from individual systems, organize it through a single extensible schema, and present it for discovery and reuse through a newly created Clinical Support Knowledge Acquisition and Archival Tool (CS-KAAT). CS-KAAT can build and maintain the underlying knowledge infrastructure needed by clinical systems. PMID:28269846

  8. Evaluating a Clinical Decision Support Interface for End-of-Life Nurse Care.

    Science.gov (United States)

    Febretti, Alessandro; Stifter, Janet; Keenan, Gail M; Lopez, Karen D; Johnson, Andrew; Wilkie, Diana J

    2014-01-01

    Clinical Decision Support Systems (CDSS) are tools that assist healthcare personnel in the decision-making process for patient care. Although CDSSs have been successfully deployed in the clinical setting to assist physicians, few CDSS have been targeted at professional nurses, the largest group of health providers. We present our experience in designing and testing a CDSS interface embedded within a nurse care planning and documentation tool. We developed four prototypes based on different CDSS feature designs, and tested them in simulated end-of-life patient handoff sessions with a group of 40 nurse clinicians. We show how our prototypes directed nurses towards an optimal care decision that was rarely performed in unassisted practice. We also discuss the effect of CDSS layout and interface navigation in a nurse's acceptance of suggested actions. These findings provide insights into effective nursing CDSS design that are generalizable to care scenarios different than end-of-life.

  9. PCA safety data review after clinical decision support and smart pump technology implementation.

    Science.gov (United States)

    Prewitt, Judy; Schneider, Susan; Horvath, Monica; Hammond, Julia; Jackson, Jason; Ginsberg, Brian

    2013-06-01

    Medication errors account for 20% of medical errors in the United States with the largest risk at prescribing and administration. Analgesics or opioids are frequently used medications that can be associated with patient harm when prescribed or administered improperly. In an effort to decrease medication errors, Duke University Hospital implemented clinical decision support via computer provider order entry (CPOE) and "smart pump" technology, 2/2008, with the goal to decrease patient-controlled analgesia (PCA) adverse events. This project evaluated PCA safety events, reviewing voluntary report system and adverse drug events via surveillance (ADE-S), on intermediate and step-down units preimplementation and postimplementation of clinical decision support via CPOE and PCA smart pumps for the prescribing and administration of opioids therapy in the adult patient requiring analgesia for acute pain. Voluntary report system and ADE-S PCA events decreased based upon 1000 PCA days; ADE-S PCA events per 1000 PCA days decreased 22%, from 5.3 (pre) to 4.2 (post) (P = 0.09). Voluntary report system events decreased 72%, from 2.4/1000 PCA days (pre) to 0.66/1000 PCA days (post) and was statistically significant (P PCA events between time periods in both the ADE-S and voluntary report system data, thus supporting the recommendation of clinical decision support via CPOE and PCA smart pump technology.

  10. Integrating complex business processes for knowledge-driven clinical decision support systems.

    Science.gov (United States)

    Kamaleswaran, Rishikesan; McGregor, Carolyn

    2012-01-01

    This paper presents in detail the component of the Complex Business Process for Stream Processing framework that is responsible for integrating complex business processes to enable knowledge-driven Clinical Decision Support System (CDSS) recommendations. CDSSs aid the clinician in supporting the care of patients by providing accurate data analysis and evidence-based recommendations. However, the incorporation of a dynamic knowledge-management system that supports the definition and enactment of complex business processes and real-time data streams has not been researched. In this paper we discuss the process web service as an innovative method of providing contextual information to a real-time data stream processing CDSS.

  11. Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis.

    Science.gov (United States)

    Zarinabad, Niloufar; Meeus, Emma M; Manias, Karen; Foster, Katharine; Peet, Andrew

    2018-05-02

    Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care. The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data. The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumors in children (cohort of 48 children, with 37 malignant and 11 benign tumors). Mevislab software and Python have been used for the development of MIROR. Regions of interests were drawn around benign and malignant body tumors on different diffusion parametric maps, and extracted information was used to discriminate the malignant tumors from benign tumors. Using MIROR, the various histogram parameters derived for each tumor case when compared with the information in the repository provided additional information for tumor characterization and facilitated the discrimination between benign and malignant tumors. Clinical decision support system cross-validation showed high sensitivity and specificity in discriminating between these tumor groups using histogram parameters. MIROR, as a diagnostic tool and repository, allowed the interpretation and analysis of magnetic resonance imaging images to be more accessible and comprehensive for clinicians. It aims to increase clinicians' skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allows integration of analyses that are not readily available clinically and streamlines the future developments. ©Niloufar Zarinabad, Emma M Meeus, Karen Manias

  12. Medical Device Integrated Vital Signs Monitoring Application with Real-Time Clinical Decision Support.

    Science.gov (United States)

    Moqeem, Aasia; Baig, Mirza; Gholamhosseini, Hamid; Mirza, Farhaan; Lindén, Maria

    2018-01-01

    This research involves the design and development of a novel Android smartphone application for real-time vital signs monitoring and decision support. The proposed application integrates market available, wireless and Bluetooth connected medical devices for collecting vital signs. The medical device data collected by the app includes heart rate, oxygen saturation and electrocardiograph (ECG). The collated data is streamed/displayed on the smartphone in real-time. This application was designed by adopting six screens approach (6S) mobile development framework and focused on user-centered approach and considered clinicians-as-a-user. The clinical engagement, consultations, feedback and usability of the application in the everyday practices were considered critical from the initial phase of the design and development. Furthermore, the proposed application is capable to deliver rich clinical decision support in real-time using the integrated medical device data.

  13. Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges.

    Science.gov (United States)

    Baig, Mirza Mansoor; GholamHosseini, Hamid; Moqeem, Aasia A; Mirza, Farhaan; Lindén, Maria

    2017-11-01

    Supporting clinicians in decision making using advanced technologies has been an active research area in biomedical engineering during the past years. Among a wide range of ubiquitous systems, smartphone applications have been increasingly developed in healthcare settings to help clinicians as well as patients. Today, many smartphone applications, from basic data analysis to advanced patient monitoring, are available to clinicians and patients. Such applications are now increasingly integrating into healthcare for clinical decision support, and therefore, concerns around accuracy, stability, and dependency of these applications are rising. In addition, lack of attention to the clinicians' acceptability, as well as the low impact on the medical professionals' decision making, are posing more serious issues on the acceptability of smartphone applications. This article reviews smartphone-based decision support applications, focusing on hospital care settings and their overall impact of these applications on the wider clinical workflow. Additionally, key challenges and barriers of the current ubiquitous device-based healthcare applications are identified. Finally, this article addresses current challenges, future directions, and the adoption of mobile healthcare applications.

  14. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: Methods of a decision-maker-researcher partnership systematic review

    OpenAIRE

    Wilczynski Nancy L; Haynes R Brian

    2010-01-01

    Abstract Background Computerized clinical decision support systems are information technology-based systems designed to improve clinical decision-making. As with any healthcare intervention with claims to improve process of care or patient outcomes, decision support systems should be rigorously evaluated before widespread dissemination into clinical practice. Engaging healthcare providers and managers in the review process may facilitate knowledge translation and uptake. The objective of this...

  15. [A computerised clinical decision-support system for the management of depression in Primary Care].

    Science.gov (United States)

    Aragonès, Enric; Comín, Eva; Cavero, Myriam; Pérez, Víctor; Molina, Cristina; Palao, Diego

    Despite its clinical relevance and its importance as a public health problem, there are major gaps in the management of depression. Evidence-based clinical guidelines are useful to improve processes and clinical outcomes. In order to make their implementation easier these guidelines have been transformed into computerised clinical decision support systems. In this article, a description is presented on the basics and characteristics of a new computerised clinical guideline for the management of major depression, developed in the public health system in Catalonia. This tool helps the clinician to establish reliable and accurate diagnoses of depression, to choose the best treatment a priori according to the disease and the patient characteristics. It also emphasises the importance of systematic monitoring to assess the clinical course, and to adjust therapeutic interventions to the patient's needs at all times. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

  16. EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect

    OpenAIRE

    Bennett, Casey; Doub, Tom; Selove, Rebecca

    2012-01-01

    Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients' health conditions and responses to treatment over time. ...

  17. The potential of predictive analytics to provide clinical decision support in depression treatment planning.

    Science.gov (United States)

    Kessler, Ronald C

    2018-01-01

    To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD). Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem. Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

  18. Privacy-preserving clinical decision support system using Gaussian kernel-based classification.

    Science.gov (United States)

    Rahulamathavan, Yogachandran; Veluru, Suresh; Phan, Raphael C-W; Chambers, Jonathon A; Rajarajan, Muttukrishnan

    2014-01-01

    A clinical decision support system forms a critical capability to link health observations with health knowledge to influence choices by clinicians for improved healthcare. Recent trends toward remote outsourcing can be exploited to provide efficient and accurate clinical decision support in healthcare. In this scenario, clinicians can use the health knowledge located in remote servers via the Internet to diagnose their patients. However, the fact that these servers are third party and therefore potentially not fully trusted raises possible privacy concerns. In this paper, we propose a novel privacy-preserving protocol for a clinical decision support system where the patients' data always remain in an encrypted form during the diagnosis process. Hence, the server involved in the diagnosis process is not able to learn any extra knowledge about the patient's data and results. Our experimental results on popular medical datasets from UCI-database demonstrate that the accuracy of the proposed protocol is up to 97.21% and the privacy of patient data is not compromised.

  19. Clinical decision support systems in child and adolescent psychiatry: a systematic review.

    Science.gov (United States)

    Koposov, Roman; Fossum, Sturla; Frodl, Thomas; Nytrø, Øystein; Leventhal, Bennett; Sourander, Andre; Quaglini, Silvana; Molteni, Massimo; de la Iglesia Vayá, María; Prokosch, Hans-Ulrich; Barbarini, Nicola; Milham, Michael Peter; Castellanos, Francisco Xavier; Skokauskas, Norbert

    2017-11-01

    Psychiatric disorders are amongst the most prevalent and impairing conditions in childhood and adolescence. Unfortunately, it is well known that general practitioners (GPs) and other frontline health providers (i.e., child protection workers, public health nurses, and pediatricians) are not adequately trained to address these ubiquitous problems (Braddick et al. Child and Adolescent mental health in Europe: infrastructures, policy and programmes, European Communities, 2009; Levav et al. Eur Child Adolesc Psychiatry 13:395-401, 2004). Advances in technology may offer a solution to this problem with clinical decision support systems (CDSS) that are designed to help professionals make sound clinical decisions in real time. This paper offers a systematic review of currently available CDSS for child and adolescent mental health disorders prepared according to the PRISMA-Protocols (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols). Applying strict eligibility criteria, the identified studies (n = 5048) were screened. Ten studies, describing eight original clinical decision support systems for child and adolescent psychiatric disorders, fulfilled inclusion criteria. Based on this systematic review, there appears to be a need for a new, readily available CDSS for child neuropsychiatric disorder which promotes evidence-based, best practices, while enabling consideration of national variation in practices by leveraging data-reuse to generate predictions regarding treatment outcome, addressing a broader cluster of clinical disorders, and targeting frontline practice environments.

  20. Advancing clinical decision support using lessons from outside of healthcare: an interdisciplinary systematic review

    Directory of Open Access Journals (Sweden)

    Wu Helen W

    2012-08-01

    Full Text Available Abstract Background Greater use of computerized decision support (DS systems could address continuing safety and quality problems in healthcare, but the healthcare field has struggled to implement DS technology. This study surveys DS experience across multiple non-healthcare disciplines for new insights that are generalizable to healthcare provider decisions. In particular, it sought design principles and lessons learned from the other disciplines that could inform efforts to accelerate the adoption of clinical decision support (CDS. Methods Our systematic review drew broadly from non-healthcare databases in the basic sciences, social sciences, humanities, engineering, business, and defense: PsychINFO, BusinessSource Premier, Social Sciences Abstracts, Web of Science, and Defense Technical Information Center. Because our interest was in DS that could apply to clinical decisions, we selected articles that (1 provided a review, overview, discussion of lessons learned, or an evaluation of design or implementation aspects of DS within a non-healthcare discipline and (2 involved an element of human judgment at the individual level, as opposed to decisions that can be fully automated or that are made at the organizational level. Results Clinical decisions share some similarities with decisions made by military commanders, business managers, and other leaders: they involve assessing new situations and choosing courses of action with major consequences, under time pressure, and with incomplete information. We identified seven high-level DS system design features from the non-healthcare literature that could be applied to CDS: providing broad, system-level perspectives; customizing interfaces to specific users and roles; making the DS reasoning transparent; presenting data effectively; generating multiple scenarios covering disparate outcomes (e.g., effective; effective with side effects; ineffective; allowing for contingent adaptations; and facilitating

  1. Advancing clinical decision support using lessons from outside of healthcare: an interdisciplinary systematic review.

    Science.gov (United States)

    Wu, Helen W; Davis, Paul K; Bell, Douglas S

    2012-08-17

    Greater use of computerized decision support (DS) systems could address continuing safety and quality problems in healthcare, but the healthcare field has struggled to implement DS technology. This study surveys DS experience across multiple non-healthcare disciplines for new insights that are generalizable to healthcare provider decisions. In particular, it sought design principles and lessons learned from the other disciplines that could inform efforts to accelerate the adoption of clinical decision support (CDS). Our systematic review drew broadly from non-healthcare databases in the basic sciences, social sciences, humanities, engineering, business, and defense: PsychINFO, BusinessSource Premier, Social Sciences Abstracts, Web of Science, and Defense Technical Information Center. Because our interest was in DS that could apply to clinical decisions, we selected articles that (1) provided a review, overview, discussion of lessons learned, or an evaluation of design or implementation aspects of DS within a non-healthcare discipline and (2) involved an element of human judgment at the individual level, as opposed to decisions that can be fully automated or that are made at the organizational level. Clinical decisions share some similarities with decisions made by military commanders, business managers, and other leaders: they involve assessing new situations and choosing courses of action with major consequences, under time pressure, and with incomplete information. We identified seven high-level DS system design features from the non-healthcare literature that could be applied to CDS: providing broad, system-level perspectives; customizing interfaces to specific users and roles; making the DS reasoning transparent; presenting data effectively; generating multiple scenarios covering disparate outcomes (e.g., effective; effective with side effects; ineffective); allowing for contingent adaptations; and facilitating collaboration. The article provides examples of

  2. A programmable rules engine to provide clinical decision support using HTML forms.

    Science.gov (United States)

    Heusinkveld, J; Geissbuhler, A; Sheshelidze, D; Miller, R

    1999-01-01

    The authors have developed a simple method for specifying rules to be applied to information on HTML forms. This approach allows clinical experts, who lack the programming expertise needed to write CGI scripts, to construct and maintain domain-specific knowledge and ordering capabilities within WizOrder, the order-entry and decision support system used at Vanderbilt Hospital. The clinical knowledge base maintainers use HTML editors to create forms and spreadsheet programs for rule entry. A test environment has been developed which uses Netscape to display forms; the production environment displays forms using an embedded browser.

  3. A First Step towards a Clinical Decision Support System for Post-traumatic Stress Disorders.

    Science.gov (United States)

    Ma, Sisi; Galatzer-Levy, Isaac R; Wang, Xuya; Fenyö, David; Shalev, Arieh Y

    2016-01-01

    PTSD is distressful and debilitating, following a non-remitting course in about 10% to 20% of trauma survivors. Numerous risk indicators of PTSD have been identified, but individual level prediction remains elusive. As an effort to bridge the gap between scientific discovery and practical application, we designed and implemented a clinical decision support pipeline to provide clinically relevant recommendation for trauma survivors. To meet the specific challenge of early prediction, this work uses data obtained within ten days of a traumatic event. The pipeline creates personalized predictive model for each individual, and computes quality metrics for each predictive model. Clinical recommendations are made based on both the prediction of the model and its quality, thus avoiding making potentially detrimental recommendations based on insufficient information or suboptimal model. The current pipeline outperforms the acute stress disorder, a commonly used clinical risk factor for PTSD development, both in terms of sensitivity and specificity.

  4. Clinical decision support improves quality of telephone triage documentation--an analysis of triage documentation before and after computerized clinical decision support.

    Science.gov (United States)

    North, Frederick; Richards, Debra D; Bremseth, Kimberly A; Lee, Mary R; Cox, Debra L; Varkey, Prathibha; Stroebel, Robert J

    2014-03-20

    Clinical decision support (CDS) has been shown to be effective in improving medical safety and quality but there is little information on how telephone triage benefits from CDS. The aim of our study was to compare triage documentation quality associated with the use of a clinical decision support tool, ExpertRN©. We examined 50 triage documents before and after a CDS tool was used in nursing triage. To control for the effects of CDS training we had an additional control group of triage documents created by nurses who were trained in the CDS tool, but who did not use it in selected notes. The CDS intervention cohort of triage notes was compared to both the pre-CDS notes and the CDS trained (but not using CDS) cohort. Cohorts were compared using the documentation standards of the American Academy of Ambulatory Care Nursing (AAACN). We also compared triage note content (documentation of associated positive and negative features relating to the symptoms, self-care instructions, and warning signs to watch for), and documentation defects pertinent to triage safety. Three of five AAACN documentation standards were significantly improved with CDS. There was a mean of 36.7 symptom features documented in triage notes for the CDS group but only 10.7 symptom features in the pre-CDS cohort (p < 0.0001) and 10.2 for the cohort that was CDS-trained but not using CDS (p < 0.0001). The difference between the mean of 10.2 symptom features documented in the pre-CDS and the mean of 10.7 symptom features documented in the CDS-trained but not using was not statistically significant (p = 0.68). CDS significantly improves triage note documentation quality. CDS-aided triage notes had significantly more information about symptoms, warning signs and self-care. The changes in triage documentation appeared to be the result of the CDS alone and not due to any CDS training that came with the CDS intervention. Although this study shows that CDS can improve documentation, further study is needed

  5. A study of diverse clinical decision support rule authoring environments and requirements for integration

    Directory of Open Access Journals (Sweden)

    Zhou Li

    2012-11-01

    Full Text Available Abstract Background Efficient rule authoring tools are critical to allow clinical Knowledge Engineers (KEs, Software Engineers (SEs, and Subject Matter Experts (SMEs to convert medical knowledge into machine executable clinical decision support rules. The goal of this analysis was to identify the critical success factors and challenges of a fully functioning Rule Authoring Environment (RAE in order to define requirements for a scalable, comprehensive tool to manage enterprise level rules. Methods The authors evaluated RAEs in active use across Partners Healthcare, including enterprise wide, ambulatory only, and system specific tools, with a focus on rule editors for reminder and medication rules. We conducted meetings with users of these RAEs to discuss their general experience and perceived advantages and limitations of these tools. Results While the overall rule authoring process is similar across the 10 separate RAEs, the system capabilities and architecture vary widely. Most current RAEs limit the ability of the clinical decision support (CDS interventions to be standardized, sharable, interoperable, and extensible. No existing system meets all requirements defined by knowledge management users. Conclusions A successful, scalable, integrated rule authoring environment will need to support a number of key requirements and functions in the areas of knowledge representation, metadata, terminology, authoring collaboration, user interface, integration with electronic health record (EHR systems, testing, and reporting.

  6. Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support.

    Science.gov (United States)

    Zhou, Xuezhong; Chen, Shibo; Liu, Baoyan; Zhang, Runsun; Wang, Yinghui; Li, Ping; Guo, Yufeng; Zhang, Hua; Gao, Zhuye; Yan, Xiufeng

    2010-01-01

    Traditional Chinese medicine (TCM) is a scientific discipline, which develops the related theories from the long-term clinical practices. The large-scale clinical data are the core empirical knowledge source for TCM research. This paper introduces a clinical data warehouse (CDW) system, which incorporates the structured electronic medical record (SEMR) data for medical knowledge discovery and TCM clinical decision support (CDS). We have developed the clinical reference information model (RIM) and physical data model to manage the various information entities and their relationships in TCM clinical data. An extraction-transformation-loading (ETL) tool is implemented to integrate and normalize the clinical data from different operational data sources. The CDW includes online analytical processing (OLAP) and complex network analysis (CNA) components to explore the various clinical relationships. Furthermore, the data mining and CNA methods are used to discover the valuable clinical knowledge from the data. The CDW has integrated 20,000 TCM inpatient data and 20,000 outpatient data, which contains manifestations (e.g. symptoms, physical examinations and laboratory test results), diagnoses and prescriptions as the main information components. We propose a practical solution to accomplish the large-scale clinical data integration and preprocessing tasks. Meanwhile, we have developed over 400 OLAP reports to enable the multidimensional analysis of clinical data and the case-based CDS. We have successfully conducted several interesting data mining applications. Particularly, we use various classification methods, namely support vector machine, decision tree and Bayesian network, to discover the knowledge of syndrome differentiation. Furthermore, we have applied association rule and CNA to extract the useful acupuncture point and herb combination patterns from the clinical prescriptions. A CDW system consisting of TCM clinical RIM, ETL, OLAP and data mining as the core

  7. Enhancing Worker Health Through Clinical Decision Support (CDS): An Introduction to a Compilation.

    Science.gov (United States)

    Filios, Margaret S; Storey, Eileen; Baron, Sherry; Luensman, Genevieve B; Shiffman, Richard N

    2017-11-01

    This article outlines an approach to developing clinical decision support (CDS) for conditions related to work and health. When incorporated in electronic health records, such CDS will assist primary care providers (PCPs) care for working patients. Three groups of Subject Matter Experts (SMEs) identified relevant clinical practice guidelines, best practices, and reviewed published literature concerning work-related asthma, return-to-work, and management of diabetes at work. SMEs developed one recommendation per topic that could be supported by electronic CDS. Reviews with PCPs, staff, and health information system implementers in five primary care settings confirmed that the approach was important and operationally sound. This compendium is intended to stimulate a dialogue between occupational health specialists and PCPs that will enhance the use of work information about patients in the primary care setting.

  8. Computerized clinical decision support systems for chronic disease management: a decision-maker-researcher partnership systematic review.

    Science.gov (United States)

    Roshanov, Pavel S; Misra, Shikha; Gerstein, Hertzel C; Garg, Amit X; Sebaldt, Rolf J; Mackay, Jean A; Weise-Kelly, Lorraine; Navarro, Tamara; Wilczynski, Nancy L; Haynes, R Brian

    2011-08-03

    The use of computerized clinical decision support systems (CCDSSs) may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease) and associated patient outcomes (such as effects on biomarkers and clinical exacerbations). We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of relevant outcomes. Of 55 included trials, 87% (n = 48) measured system impact on the process of care and 52% (n = 25) of those demonstrated statistically significant improvements. Sixty-five percent (36/55) of trials measured impact on, typically, non-major (surrogate) patient outcomes, and 31% (n = 11) of those demonstrated benefits. Factors of interest to decision makers, such as cost, user satisfaction, system interface and feature sets, unique design and deployment characteristics, and effects on user workflow were rarely investigated or reported. A small majority (just over half) of CCDSSs improved care processes in chronic disease management and some improved patient health. Policy makers, healthcare administrators, and practitioners should be aware that the evidence of CCDSS effectiveness is limited, especially with respect to the small number and size of studies measuring patient outcomes.

  9. Automatic Decision Support for Clinical Diagnostic Literature Using Link Analysis in a Weighted Keyword Network.

    Science.gov (United States)

    Li, Shuqing; Sun, Ying; Soergel, Dagobert

    2017-12-23

    We present a novel approach to recommending articles from the medical literature that support clinical diagnostic decision-making, giving detailed descriptions of the associated ideas and principles. The specific goal is to retrieve biomedical articles that help answer questions of a specified type about a particular case. Based on the filtered keywords, MeSH(Medical Subject Headings) lexicon and the automatically extracted acronyms, the relationship between keywords and articles was built. The paper gives a detailed description of the process of by which keywords were measured and relevant articles identified based on link analysis in a weighted keywords network. Some important challenges identified in this study include the extraction of diagnosis-related keywords and a collection of valid sentences based on the keyword co-occurrence analysis and existing descriptions of symptoms. All data were taken from medical articles provided in the TREC (Text Retrieval Conference) clinical decision support track 2015. Ten standard topics and one demonstration topic were tested. In each case, a maximum of five articles with the highest relevance were returned. The total user satisfaction of 3.98 was 33% higher than average. The results also suggested that the smaller the number of results, the higher the average satisfaction. However, a few shortcomings were also revealed since medical literature recommendation for clinical diagnostic decision support is so complex a topic that it cannot be fully addressed through the semantic information carried solely by keywords in existing descriptions of symptoms. Nevertheless, the fact that these articles are actually relevant will no doubt inspire future research.

  10. The potential value on medication safety of a clinical decision support system in intensive care patients with renal insufficiency.

    NARCIS (Netherlands)

    Helmons, P.J.; Grouls, R.J.E.; Roos, A.N.; Bindels, A.J.G.H.; Clercq, de P.A.; Wessels-Basten, S.J.W.; Ackerman, E.W.; Korsten, H.H.M.

    2007-01-01

    Clinical decision support systems (CDSS) are defined as electronic or non-electronic systems designed to aid in clinical decision making, using characteristics of individual patients to generate patient-specific assessments or recommendations that are then presented to clinicians for consideration

  11. The NIAID Division of AIDS enterprise information system: integrated decision support for global clinical research programs

    Science.gov (United States)

    Gupta, Nitin; Varghese, Suresh; Virkar, Hemant

    2011-01-01

    The National Institute of Allergy and Infectious Diseases (NIAID) Division of AIDS (DAIDS) Enterprise Information System (DAIDS-ES) is a web-based system that supports NIAID in the scientific, strategic, and tactical management of its global clinical research programs for HIV/AIDS vaccines, prevention, and therapeutics. Different from most commercial clinical trials information systems, which are typically protocol-driven, the DAIDS-ES was built to exchange information with those types of systems and integrate it in ways that help scientific program directors lead the research effort and keep pace with the complex and ever-changing global HIV/AIDS pandemic. Whereas commercially available clinical trials support systems are not usually disease-focused, DAIDS-ES was specifically designed to capture and incorporate unique scientific, demographic, and logistical aspects of HIV/AIDS treatment, prevention, and vaccine research in order to provide a rich source of information to guide informed decision-making. Sharing data across its internal components and with external systems, using defined vocabularies, open standards and flexible interfaces, the DAIDS-ES enables NIAID, its global collaborators and stakeholders, access to timely, quality information about NIAID-supported clinical trials which is utilized to: (1) analyze the research portfolio, assess capacity, identify opportunities, and avoid redundancies; (2) help support study safety, quality, ethics, and regulatory compliance; (3) conduct evidence-based policy analysis and business process re-engineering for improved efficiency. This report summarizes how the DAIDS-ES was conceptualized, how it differs from typical clinical trial support systems, the rationale for key design choices, and examples of how it is being used to advance the efficiency and effectiveness of NIAID's HIV/AIDS clinical research programs. PMID:21816958

  12. Personalised Care Plan Management Utilizing Guideline-Driven Clinical Decision Support Systems.

    Science.gov (United States)

    Laleci Erturkmen, Gokce Banu; Yuksel, Mustafa; Sarigul, Bunyamin; Lilja, Mikael; Chen, Rong; Arvanitis, Theodoros N

    2018-01-01

    Older age is associated with an increased accumulation of multiple chronic conditions. The clinical management of patients suffering from multiple chronic conditions is very complex, disconnected and time-consuming with the traditional care settings. Integrated care is a means to address the growing demand for improved patient experience and health outcomes of multimorbid and long-term care patients. Care planning is a prevalent approach of integrated care, where the aim is to deliver more personalized and targeted care creating shared care plans by clearly articulating the role of each provider and patient in the care process. In this paper, we present a method and corresponding implementation of a semi-automatic care plan management tool, integrated with clinical decision support services which can seamlessly access and assess the electronic health records (EHRs) of the patient in comparison with evidence based clinical guidelines to suggest personalized recommendations for goals and interventions to be added to the individualized care plans.

  13. The Research of Clinical Decision Support System Based on Three-Layer Knowledge Base Model

    Directory of Open Access Journals (Sweden)

    Yicheng Jiang

    2017-01-01

    Full Text Available In many clinical decision support systems, a two-layer knowledge base model (disease-symptom of rule reasoning is used. This model often does not express knowledge very well since it simply infers disease from the presence of certain symptoms. In this study, we propose a three-layer knowledge base model (disease-symptom-property to utilize more useful information in inference. The system iteratively calculates the probability of patients who may suffer from diseases based on a multisymptom naive Bayes algorithm, in which the specificity of these disease symptoms is weighted by the estimation of the degree of contribution to diagnose the disease. It significantly reduces the dependencies between attributes to apply the naive Bayes algorithm more properly. Then, the online learning process for parameter optimization of the inference engine was completed. At last, our decision support system utilizing the three-layer model was formally evaluated by two experienced doctors. By comparisons between prediction results and clinical results, our system can provide effective clinical recommendations to doctors. Moreover, we found that the three-layer model can improve the accuracy of predictions compared with the two-layer model. In light of some of the limitations of this study, we also identify and discuss several areas that need continued improvement.

  14. Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation.

    Science.gov (United States)

    Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

    2017-06-01

    This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care.

  15. Clinical Decision Support Tools for Selecting Interventions for Patients with Disabling Musculoskeletal Disorders

    DEFF Research Database (Denmark)

    Gross, Douglas P; Armijo-Olivo, Susan; Shaw, William S

    2016-01-01

    Purpose We aimed to identify and inventory clinical decision support (CDS) tools for helping front-line staff select interventions for patients with musculoskeletal (MSK) disorders. Methods We used Arksey and O'Malley's scoping review framework which progresses through five stages: (1) identifying...... the research question; (2) identifying relevant studies; (3) selecting studies for analysis; (4) charting the data; and (5) collating, summarizing and reporting results. We considered computer-based, and other available tools, such as algorithms, care pathways, rules and models. Since this research crosses...

  16. Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision.

    Science.gov (United States)

    Middleton, B; Sittig, D F; Wright, A

    2016-08-02

    The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040. Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS. In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery. CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.

  17. Analysis of Nursing Clinical Decision Support Requests and Strategic Plan in a Large Academic Health System.

    Science.gov (United States)

    Whalen, Kimberly; Bavuso, Karen; Bouyer-Ferullo, Sharon; Goldsmith, Denise; Fairbanks, Amanda; Gesner, Emily; Lagor, Charles; Collins, Sarah

    2016-01-01

    To understand requests for nursing Clinical Decision Support (CDS) interventions at a large integrated health system undergoing vendor-based EHR implementation. In addition, to establish a process to guide both short-term implementation and long-term strategic goals to meet nursing CDS needs. We conducted an environmental scan to understand current state of nursing CDS over three months. The environmental scan consisted of a literature review and an analysis of CDS requests received from across our health system. We identified existing high priority CDS and paper-based tools used in nursing practice at our health system that guide decision-making. A total of 46 nursing CDS requests were received. Fifty-six percent (n=26) were specific to a clinical specialty; 22 percent (n=10) were focused on facilitating clinical consults in the inpatient setting. "Risk Assessments/Risk Reduction/Promotion of Healthy Habits" (n=23) was the most requested High Priority Category received for nursing CDS. A continuum of types of nursing CDS needs emerged using the Data-Information-Knowledge-Wisdom Conceptual Framework: 1) facilitating data capture, 2) meeting information needs, 3) guiding knowledge-based decision making, and 4) exposing analytics for wisdom-based clinical interpretation by the nurse. Identifying and prioritizing paper-based tools that can be modified into electronic CDS is a challenge. CDS strategy is an evolving process that relies on close collaboration and engagement with clinical sites for short-term implementation and should be incorporated into a long-term strategic plan that can be optimized and achieved overtime. The Data-Information-Knowledge-Wisdom Conceptual Framework in conjunction with the High Priority Categories established may be a useful tool to guide a strategic approach for meeting short-term nursing CDS needs and aligning with the organizational strategic plan.

  18. Genetic Stratification in Myeloid Diseases: From Risk Assessment to Clinical Decision Support Tool

    Directory of Open Access Journals (Sweden)

    Yishai Ofran

    2014-10-01

    Full Text Available Genetic aberrations have become a dominant factor in the stratification of myeloid malignancies. Cytogenetic and a few mutation studies are the backbone of risk assessment models of myeloid malignancies which are a major consideration in clinical decisions, especially patient assignment for allogeneic stem cell transplantation. Progress in our understanding of the genetic basis of the pathogenesis of myeloid malignancies and the growing capabilities of mass sequencing may add new roles for the clinical usage of genetic data. A few recently identified mutations recognized to be associated with specific diseases or clinical scenarios may soon become part of the diagnostic criteria of such conditions. Mutational studies may also advance our capabilities for a more efficient patient selection process, assigning the most effective therapy at the best timing for each patient. The clinical utility of genetic data is anticipated to advance further with the adoption of deep sequencing and next-generation sequencing techniques. We herein suggest some future potential applications of sequential genetic data to identify pending deteriorations at time points which are the best for aggressive interventions such as allogeneic stem cell transplantation. Genetics is moving from being mostly a prognostic factor to becoming a multitasking decision support tool for hematologists. Physicians must pay attention to advances in molecular hematology as it will soon be accessible and influential for most of our patients.

  19. Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

    Directory of Open Access Journals (Sweden)

    Clark Michael E

    2010-04-01

    Full Text Available Abstract Background Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients. Methods Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR, and a set of clinical tools. Results The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools. Conclusions Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The

  20. [Human body meridian spatial decision support system for clinical treatment and teaching of acupuncture and moxibustion].

    Science.gov (United States)

    Wu, Dehua

    2016-01-01

    The spatial position and distribution of human body meridian are expressed limitedly in the decision support system (DSS) of acupuncture and moxibustion at present, which leads to the failure to give the effective quantitative analysis on the spatial range and the difficulty for the decision-maker to provide a realistic spatial decision environment. Focusing on the limit spatial expression in DSS of acupuncture and moxibustion, it was proposed that on the basis of the geographic information system, in association of DSS technology, the design idea was developed on the human body meridian spatial DSS. With the 4-layer service-oriented architecture adopted, the data center integrated development platform was taken as the system development environment. The hierarchical organization was done for the spatial data of human body meridian via the directory tree. The structured query language (SQL) server was used to achieve the unified management of spatial data and attribute data. The technologies of architecture, configuration and plug-in development model were integrated to achieve the data inquiry, buffer analysis and program evaluation of the human body meridian spatial DSS. The research results show that the human body meridian spatial DSS could reflect realistically the spatial characteristics of the spatial position and distribution of human body meridian and met the constantly changeable demand of users. It has the powerful spatial analysis function and assists with the scientific decision in clinical treatment and teaching of acupuncture and moxibustion. It is the new attempt to the informatization research of human body meridian.

  1. A mathematical model for interpretable clinical decision support with applications in gynecology.

    Directory of Open Access Journals (Sweden)

    Vanya M C A Van Belle

    Full Text Available Over time, methods for the development of clinical decision support (CDS systems have evolved from interpretable and easy-to-use scoring systems to very complex and non-interpretable mathematical models. In order to accomplish effective decision support, CDS systems should provide information on how the model arrives at a certain decision. To address the issue of incompatibility between performance, interpretability and applicability of CDS systems, this paper proposes an innovative model structure, automatically leading to interpretable and easily applicable models. The resulting models can be used to guide clinicians when deciding upon the appropriate treatment, estimating patient-specific risks and to improve communication with patients.We propose the interval coded scoring (ICS system, which imposes that the effect of each variable on the estimated risk is constant within consecutive intervals. The number and position of the intervals are automatically obtained by solving an optimization problem, which additionally performs variable selection. The resulting model can be visualised by means of appealing scoring tables and color bars. ICS models can be used within software packages, in smartphone applications, or on paper, which is particularly useful for bedside medicine and home-monitoring. The ICS approach is illustrated on two gynecological problems: diagnosis of malignancy of ovarian tumors using a dataset containing 3,511 patients, and prediction of first trimester viability of pregnancies using a dataset of 1,435 women. Comparison of the performance of the ICS approach with a range of prediction models proposed in the literature illustrates the ability of ICS to combine optimal performance with the interpretability of simple scoring systems.The ICS approach can improve patient-clinician communication and will provide additional insights in the importance and influence of available variables. Future challenges include extensions of the

  2. Integrative review of clinical decision support for registered nurses in acute care settings.

    Science.gov (United States)

    Dunn Lopez, Karen; Gephart, Sheila M; Raszewski, Rebecca; Sousa, Vanessa; Shehorn, Lauren E; Abraham, Joanna

    2017-03-01

    To report on the state of the science of clinical decision support (CDS) for hospital bedside nurses. We performed an integrative review of qualitative and quantitative peer-reviewed original research studies using a structured search of PubMed, Embase, Cumulative Index to Nursing and Applied Health Literature (CINAHL), Scopus, Web of Science, and IEEE Xplore (Institute of Electrical and Electronics Engineers Xplore Digital Library). We included articles that reported on CDS targeting bedside nurses and excluded in stages based on rules for titles, abstracts, and full articles. We extracted research design and methods, CDS purpose, electronic health record integration, usability, and process and patient outcomes. Our search yielded 3157 articles. After removing duplicates and applying exclusion rules, 28 articles met the inclusion criteria. The majority of studies were single-site, descriptive or qualitative (43%) or quasi-experimental (36%). There was only 1 randomized controlled trial. The purpose of most CDS was to support diagnostic decision-making (36%), guideline adherence (32%), medication management (29%), and situational awareness (25%). All the studies that included process outcomes (7) and usability outcomes (4) and also had analytic procedures to detect changes in outcomes demonstrated statistically significant improvements. Three of 4 studies that included patient outcomes and also had analytic procedures to detect change showed statistically significant improvements. No negative effects of CDS were found on process, usability, or patient outcomes. Clinical support systems targeting bedside nurses have positive effects on outcomes and hold promise for improving care quality; however, this research is lagging behind studies of CDS targeting medical decision-making in both volume and level of evidence. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions

  3. A task-based support architecture for developing point-of-care clinical decision support systems for the emergency department.

    Science.gov (United States)

    Wilk, S; Michalowski, W; O'Sullivan, D; Farion, K; Sayyad-Shirabad, J; Kuziemsky, C; Kukawka, B

    2013-01-01

    The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter. The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate. The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE--a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED. The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs.

  4. Multidisciplinary Modelling of Symptoms and Signs with Archetypes and SNOMED-CT for Clinical Decision Support.

    Science.gov (United States)

    Marco-Ruiz, Luis; Maldonado, J Alberto; Karlsen, Randi; Bellika, Johan G

    2015-01-01

    Clinical Decision Support Systems (CDSS) help to improve health care and reduce costs. However, the lack of knowledge management and modelling hampers their maintenance and reuse. Current EHR standards and terminologies can allow the semantic representation of the data and knowledge of CDSS systems boosting their interoperability, reuse and maintenance. This paper presents the modelling process of respiratory conditions' symptoms and signs by a multidisciplinary team of clinicians and information architects with the help of openEHR, SNOMED and clinical information modelling tools for a CDSS. The information model of the CDSS was defined by means of an archetype and the knowledge model was implemented by means of an SNOMED-CT based ontology.

  5. Development and impact of computerised decision support systems for clinical management of depression: A systematic review.

    Science.gov (United States)

    Triñanes, Yolanda; Atienza, Gerardo; Louro-González, Arturo; de-las-Heras-Liñero, Elena; Alvarez-Ariza, María; Palao, Diego J

    2015-01-01

    One of the proposals for improving clinical practice is to introduce computerised decision support systems (CDSS) and integrate these with electronic medical records. Accordingly, this study sought to systematically review evidence on the effectiveness of CDSS in the management of depression. A search was performed in Medline, EMBASE and PsycInfo, in order to do this. The quality of quantitative studies was assessed using the SIGN method, and qualitative studies using the CASPe checklist. Seven studies were identified (3 randomised clinical trials, 3 non-randomised trials, and one qualitative study). The CDSS assessed incorporated content drawn from guidelines and other evidence-based products. In general, the CDSS had a positive impact on different aspects, such as the screening and diagnosis, treatment, improvement in depressive symptoms and quality of life, and referral of patients. The use of CDSS could thus serve to optimise care of depression in various scenarios by providing recommendations based on the best evidence available and facilitating decision-making in clinical practice. Copyright © 2014 SEP y SEPB. Published by Elsevier España. All rights reserved.

  6. The Development of a Clinical Decision Support System for the Management of Pediatric Food Allergy.

    Science.gov (United States)

    Otto, Alana K; Dyer, Ashley A; Warren, Christopher M; Walkner, Madeline; Smith, Bridget M; Gupta, Ruchi S

    2017-06-01

    Pediatricians are often first-line providers for children with food allergy. Food allergy management guidelines have been developed but are cumbersome and confusing, and significant variation exists in pediatricians' management practices. We therefore consolidated the guidelines into 5 key steps for pediatricians caring for patients with food allergy and used rapid-cycle improvement methods to create a clinical decision support system to facilitate the management of food allergy in the primary care setting. This report details the development of the Food Allergy Support Tool (FAST), its pilot testing in 4 primary care pediatric practices, and our ongoing efforts to improve its utility and ease of use. Key themes identified during these processes include the importance of both initial and ongoing provider education as well as the limitations of a tool that must be actively initiated by providers.

  7. Automatic identification of high impact articles in PubMed to support clinical decision making.

    Science.gov (United States)

    Bian, Jiantao; Morid, Mohammad Amin; Jonnalagadda, Siddhartha; Luo, Gang; Del Fiol, Guilherme

    2017-09-01

    The practice of evidence-based medicine involves integrating the latest best available evidence into patient care decisions. Yet, critical barriers exist for clinicians' retrieval of evidence that is relevant for a particular patient from primary sources such as randomized controlled trials and meta-analyses. To help address those barriers, we investigated machine learning algorithms that find clinical studies with high clinical impact from PubMed®. Our machine learning algorithms use a variety of features including bibliometric features (e.g., citation count), social media attention, journal impact factors, and citation metadata. The algorithms were developed and evaluated with a gold standard composed of 502 high impact clinical studies that are referenced in 11 clinical evidence-based guidelines on the treatment of various diseases. We tested the following hypotheses: (1) our high impact classifier outperforms a state-of-the-art classifier based on citation metadata and citation terms, and PubMed's® relevance sort algorithm; and (2) the performance of our high impact classifier does not decrease significantly after removing proprietary features such as citation count. The mean top 20 precision of our high impact classifier was 34% versus 11% for the state-of-the-art classifier and 4% for PubMed's® relevance sort (p=0.009); and the performance of our high impact classifier did not decrease significantly after removing proprietary features (mean top 20 precision=34% vs. 36%; p=0.085). The high impact classifier, using features such as bibliometrics, social media attention and MEDLINE® metadata, outperformed previous approaches and is a promising alternative to identifying high impact studies for clinical decision support. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Usability Testing of a Complex Clinical Decision Support Tool in the Emergency Department: Lessons Learned.

    Science.gov (United States)

    Press, Anne; McCullagh, Lauren; Khan, Sundas; Schachter, Andy; Pardo, Salvatore; McGinn, Thomas

    2015-09-10

    As the electronic health record (EHR) becomes the preferred documentation tool across medical practices, health care organizations are pushing for clinical decision support systems (CDSS) to help bring clinical decision support (CDS) tools to the forefront of patient-physician interactions. A CDSS is integrated into the EHR and allows physicians to easily utilize CDS tools. However, often CDSS are integrated into the EHR without an initial phase of usability testing, resulting in poor adoption rates. Usability testing is important because it evaluates a CDSS by testing it on actual users. This paper outlines the usability phase of a study, which will test the impact of integration of the Wells CDSS for pulmonary embolism (PE) diagnosis into a large urban emergency department, where workflow is often chaotic and high stakes decisions are frequently made. We hypothesize that conducting usability testing prior to integration of the Wells score into an emergency room EHR will result in increased adoption rates by physicians. The objective of the study was to conduct usability testing for the integration of the Wells clinical prediction rule into a tertiary care center's emergency department EHR. We conducted usability testing of a CDS tool in the emergency department EHR. The CDS tool consisted of the Wells rule for PE in the form of a calculator and was triggered off computed tomography (CT) orders or patients' chief complaint. The study was conducted at a tertiary hospital in Queens, New York. There were seven residents that were recruited and participated in two phases of usability testing. The usability testing employed a "think aloud" method and "near-live" clinical simulation, where care providers interacted with standardized patients enacting a clinical scenario. Both phases were audiotaped, video-taped, and had screen-capture software activated for onscreen recordings. Phase I: Data from the "think-aloud" phase of the study showed an overall positive outlook on

  9. Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine.

    Science.gov (United States)

    Castaneda, Christian; Nalley, Kip; Mannion, Ciaran; Bhattacharyya, Pritish; Blake, Patrick; Pecora, Andrew; Goy, Andre; Suh, K Stephen

    2015-01-01

    , and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care.

  10. Change-Point Detection Method for Clinical Decision Support System Rule Monitoring.

    Science.gov (United States)

    Liu, Siqi; Wright, Adam; Hauskrecht, Milos

    2017-06-01

    A clinical decision support system (CDSS) and its components can malfunction due to various reasons. Monitoring the system and detecting its malfunctions can help one to avoid any potential mistakes and associated costs. In this paper, we investigate the problem of detecting changes in the CDSS operation, in particular its monitoring and alerting subsystem, by monitoring its rule firing counts. The detection should be performed online, that is whenever a new datum arrives, we want to have a score indicating how likely there is a change in the system. We develop a new method based on Seasonal-Trend decomposition and likelihood ratio statistics to detect the changes. Experiments on real and simulated data show that our method has a lower delay in detection compared with existing change-point detection methods.

  11. Service oriented architecture for clinical decision support: a systematic review and future directions.

    Science.gov (United States)

    Loya, Salvador Rodriguez; Kawamoto, Kensaku; Chatwin, Chris; Huser, Vojtech

    2014-12-01

    The use of a service-oriented architecture (SOA) has been identified as a promising approach for improving health care by facilitating reliable clinical decision support (CDS). A review of the literature through October 2013 identified 44 articles on this topic. The review suggests that SOA related technologies such as Business Process Model and Notation (BPMN) and Service Component Architecture (SCA) have not been generally adopted to impact health IT systems' performance for better care solutions. Additionally, technologies such as Enterprise Service Bus (ESB) and architectural approaches like Service Choreography have not been generally exploited among researchers and developers. Based on the experience of other industries and our observation of the evolution of SOA, we found that the greater use of these approaches have the potential to significantly impact SOA implementations for CDS.

  12. Multi-criteria clinical decision support: A primer on the use of multiple criteria decision making methods to promote evidence-based, patient-centered healthcare.

    Science.gov (United States)

    Dolan, James G

    2010-01-01

    Current models of healthcare quality recommend that patient management decisions be evidence-based and patient-centered. Evidence-based decisions require a thorough understanding of current information regarding the natural history of disease and the anticipated outcomes of different management options. Patient-centered decisions incorporate patient preferences, values, and unique personal circumstances into the decision making process and actively involve both patients along with health care providers as much as possible. Fundamentally, therefore, evidence-based, patient-centered decisions are multi-dimensional and typically involve multiple decision makers.Advances in the decision sciences have led to the development of a number of multiple criteria decision making methods. These multi-criteria methods are designed to help people make better choices when faced with complex decisions involving several dimensions. They are especially helpful when there is a need to combine "hard data" with subjective preferences, to make trade-offs between desired outcomes, and to involve multiple decision makers. Evidence-based, patient-centered clinical decision making has all of these characteristics. This close match suggests that clinical decision support systems based on multi-criteria decision making techniques have the potential to enable patients and providers to carry out the tasks required to implement evidence-based, patient-centered care effectively and efficiently in clinical settings.The goal of this paper is to give readers a general introduction to the range of multi-criteria methods available and show how they could be used to support clinical decision-making. Methods discussed include the balance sheet, the even swap method, ordinal ranking methods, direct weighting methods, multi-attribute decision analysis, and the analytic hierarchy process (AHP).

  13. Interoperability of clinical decision-support systems and electronic health records using archetypes: a case study in clinical trial eligibility.

    Science.gov (United States)

    Marcos, Mar; Maldonado, Jose A; Martínez-Salvador, Begoña; Boscá, Diego; Robles, Montserrat

    2013-08-01

    Clinical decision-support systems (CDSSs) comprise systems as diverse as sophisticated platforms to store and manage clinical data, tools to alert clinicians of problematic situations, or decision-making tools to assist clinicians. Irrespective of the kind of decision-support task CDSSs should be smoothly integrated within the clinical information system, interacting with other components, in particular with the electronic health record (EHR). However, despite decades of developments, most CDSSs lack interoperability features. We deal with the interoperability problem of CDSSs and EHRs by exploiting the dual-model methodology. This methodology distinguishes a reference model and archetypes. A reference model is represented by a stable and small object-oriented model that describes the generic properties of health record information. For their part, archetypes are reusable and domain-specific definitions of clinical concepts in the form of structured and constrained combinations of the entities of the reference model. We rely on archetypes to make the CDSS compatible with EHRs from different institutions. Concretely, we use archetypes for modelling the clinical concepts that the CDSS requires, in conjunction with a series of knowledge-intensive mappings relating the archetypes to the data sources (EHR and/or other archetypes) they depend on. We introduce a comprehensive approach, including a set of tools as well as methodological guidelines, to deal with the interoperability of CDSSs and EHRs based on archetypes. Archetypes are used to build a conceptual layer of the kind of a virtual health record (VHR) over the EHR whose contents need to be integrated and used in the CDSS, associating them with structural and terminology-based semantics. Subsequently, the archetypes are mapped to the EHR by means of an expressive mapping language and specific-purpose tools. We also describe a case study where the tools and methodology have been employed in a CDSS to support

  14. "Think aloud" and "Near live" usability testing of two complex clinical decision support tools.

    Science.gov (United States)

    Richardson, Safiya; Mishuris, Rebecca; O'Connell, Alexander; Feldstein, David; Hess, Rachel; Smith, Paul; McCullagh, Lauren; McGinn, Thomas; Mann, Devin

    2017-10-01

    Low provider adoption continues to be a significant barrier to realizing the potential of clinical decision support. "Think Aloud" and "Near Live" usability testing were conducted on two clinical decision support tools. Each was composed of an alert, a clinical prediction rule which estimated risk of either group A Streptococcus pharyngitis or pneumonia and an automatic order set based on risk. The objective of this study was to further understanding of the facilitators of usability and to evaluate the types of additional information gained from proceeding to "Near Live" testing after completing "Think Aloud". This was a qualitative observational study conducted at a large academic health care system with 12 primary care providers. During "Think Aloud" testing, participants were provided with written clinical scenarios and asked to verbalize their thought process while interacting with the tool. During "Near Live" testing participants interacted with a mock patient. Morae usability software was used to record full screen capture and audio during every session. Participant comments were placed into coding categories and analyzed for generalizable themes. Themes were compared across usability methods. "Think Aloud" and "Near Live" usability testing generated similar themes under the coding categories visibility, workflow, content, understand-ability and navigation. However, they generated significantly different themes under the coding categories usability, practical usefulness and medical usefulness. During both types of testing participants found the tool easier to use when important text was distinct in its appearance, alerts were passive and appropriately timed, content was up to date, language was clear and simple, and each component of the tool included obvious indicators of next steps. Participant comments reflected higher expectations for usability and usefulness during "Near Live" testing. For example, visit aids, such as automatically generated order sets

  15. Implementation of a Mobile Clinical Decision Support Application to Augment Local Antimicrobial Stewardship.

    Science.gov (United States)

    Hoff, Brian M; Ford, Diana C; Ince, Dilek; Ernst, Erika J; Livorsi, Daniel J; Heintz, Brett H; Masse, Vincent; Brownlee, Michael J; Ford, Bradley A

    2018-01-01

    Medical applications for mobile devices allow clinicians to leverage microbiological data and standardized guidelines to treat patients with infectious diseases. We report the implementation of a mobile clinical decision support (CDS) application to augment local antimicrobial stewardship. We detail the implementation of our mobile CDS application over 20 months. Application utilization data were collected and evaluated using descriptive statistics to quantify the impact of our implementation. Project initiation focused on engaging key stakeholders, developing a business case, and selecting a mobile platform. The preimplementation phase included content development, creation of a pathway for content approval within the hospital committee structure, engaging clinical leaders, and formatting the first version of the guide. Implementation involved a media campaign, staff education, and integration within the electronic medical record and hospital mobile devices. The postimplementation phase required ongoing quality improvement, revision of outdated content, and repeated staff education. The evaluation phase included a guide utilization analysis, reporting to hospital leadership, and sustainability and innovation planning. The mobile application was downloaded 3056 times and accessed 9259 times during the study period. The companion web viewer was accessed 8214 times. Successful implementation of a customizable mobile CDS tool enabled our team to expand beyond microbiological data to clinical diagnosis, treatment, and antimicrobial stewardship, broadening our influence on antimicrobial prescribing and incorporating utilization data to inspire new quality and safety initiatives. Further studies are needed to assess the impact on antimicrobial utilization, infection control measures, and patient care outcomes.

  16. Electronic clinical decision support systems attitudes and barriers to use in the oncology setting.

    LENUS (Irish Health Repository)

    Collins, I M

    2012-03-02

    BACKGROUND: There is little evidence regarding attitudes to clinical decision support systems (CDSS) in oncology. AIMS: We examined the current usage, awareness, and concerns of Irish medical oncologists and oncology pharmacists in this area. METHODS: A questionnaire was sent to 27 medical oncologists and 34 oncology pharmacists, identified through professional interest groups. Respondents ranked concerns regarding their use of a CDSS on a scale from 1 to 4, with 4 being most important. RESULTS: Overall, 67% (41\\/61) responded, 48% (13\\/27) of oncologists and 82% (28\\/34) of pharmacists surveyed. Concerns included "difficulty defining complex clinical situations with a set of rules" (mean ± SD) (3.2 ± 0.9), "ensuring evidence base is up to date and relevant" (3.2 ± 0.9) and "lack of clinically relevant suggestions" (2.9 ± 0.9). Ninety-three percent reported using a CDSS but 54% were unaware of this. CONCLUSION: While there are benefits to using a CDSS, concerns must be addressed through user education. This may be a starting point for a user-centred design approach to the development of future local systems through a consultative process.

  17. Clinical decision support system for early detection of prostate cancer from benign hyperplasia of prostate.

    Science.gov (United States)

    Ghaderzadeh, Mustafa

    2013-01-01

    There has been a growing research interest in the use of intelligent methods in medical informatics studies. Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficult medical decisions. Prostate Neoplasia problems including benign hyperplasia and cancer of prostate are very common and cause significant delay in recovery and often require costly investigations before coming to its diagnosis. The conventional approach to build medical diagnostic system requires the formulation of rules by which the input data can be analyzed. But the formulation of such rules is very difficult with large sets of input data. Realizing the difficulty, a number of quantitative mathematical and statistical models including pattern classification technique such as Artificial neural networks (ANN), rolled based system, discriminate analysis and regression analysis has been applied as an alternative to conventional clinical and medical diagnostic. Among the mathematical and statistical modeling techniques used in medical decision support, Artificial neural networks attract many attentions in recent studies and in the last decade, the use of neural networks has become widely accepted in medical applications. This is manifested by an increasing number of medical devices currently available on the market with embedded AI algorithms, together with an accelerating pace of publication in medical journals, with over 500 academic publications year featuring Artificial Neural Networks (ANNs).

  18. Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support.

    Science.gov (United States)

    Miller, Kristen; Mosby, Danielle; Capan, Muge; Kowalski, Rebecca; Ratwani, Raj; Noaiseh, Yaman; Kraft, Rachel; Schwartz, Sanford; Weintraub, William S; Arnold, Ryan

    2018-05-01

    Provider acceptance and associated patient outcomes are widely discussed in the evaluation of clinical decision support systems (CDSSs), but critical design criteria for tools have generally been overlooked. The objective of this work is to inform electronic health record alert optimization and clinical practice workflow by identifying, compiling, and reporting design recommendations for CDSS to support the efficient, effective, and timely delivery of high-quality care. A narrative review was conducted from 2000 to 2016 in PubMed and The Journal of Human Factors and Ergonomics Society to identify papers that discussed/recommended design features of CDSSs that are associated with the success of these systems. Fourteen papers were included as meeting the criteria and were found to have a total of 42 unique recommendations; 11 were classified as interface features, 10 as information features, and 21 as interaction features. Features are defined and described, providing actionable guidance that can be applied to CDSS development and policy. To our knowledge, no reviews have been completed that discuss/recommend design features of CDSS at this scale, and thus we found that this was important for the body of literature. The recommendations identified in this narrative review will help to optimize design, organization, management, presentation, and utilization of information through presentation, content, and function. The designation of 3 categories (interface, information, and interaction) should be further evaluated to determine the critical importance of the categories. Future work will determine how to prioritize them with limited resources for designers and developers in order to maximize the clinical utility of CDSS. This review will expand the field of knowledge and provide a novel organization structure to identify key recommendations for CDSS.

  19. Measuring Clinical Decision Support Influence on Evidence-Based Nursing Practice.

    Science.gov (United States)

    Cortez, Susan; Dietrich, Mary S; Wells, Nancy

    2016-07-01

    To measure the effect of clinical decision support (CDS) on oncology nurse evidence-based practice (EBP).
. Longitudinal cluster-randomized design.
. Four distinctly separate oncology clinics associated with an academic medical center.
. The study sample was comprised of randomly selected data elements from the nursing documentation software. The data elements were patient-reported symptoms and the associated nurse interventions. The total sample observations were 600, derived from a baseline, posteducation, and postintervention sample of 200 each (100 in the intervention group and 100 in the control group for each sample).
. The cluster design was used to support randomization of the study intervention at the clinic level rather than the individual participant level to reduce possible diffusion of the study intervention. An elongated data collection cycle (11 weeks) controlled for temporary increases in nurse EBP related to the education or CDS intervention.
. The dependent variable was the nurse evidence-based documentation rate, calculated from the nurse-documented interventions. The independent variable was the CDS added to the nursing documentation software.
. The average EBP rate at baseline for the control and intervention groups was 27%. After education, the average EBP rate increased to 37%, and then decreased to 26% in the postintervention sample. Mixed-model linear statistical analysis revealed no significant interaction of group by sample. The CDS intervention did not result in an increase in nurse EBP.
. EBP education increased nurse EBP documentation rates significantly but only temporarily. Nurses may have used evidence in practice but may not have documented their interventions.
. More research is needed to understand the complex relationship between CDS, nursing practice, and nursing EBP intervention documentation. CDS may have a different effect on nurse EBP, physician EBP, and other medical professional EBP.

  20. Design of a Clinical Decision Support System for Fracture Prediction Using Imbalanced Dataset

    Directory of Open Access Journals (Sweden)

    Yung-Fu Chen

    2018-01-01

    Full Text Available More than 1 billion people suffer from chronic respiratory diseases worldwide, accounting for more than 4 million deaths annually. Inhaled corticosteroid is a popular medication for treating chronic respiratory diseases. Its side effects include decreased bone mineral density and osteoporosis. The aims of this study are to investigate the association of inhaled corticosteroids and fracture and to design a clinical support system for fracture prediction. The data of patients aged 20 years and older, who had visited healthcare centers and been prescribed with inhaled corticosteroids within 2002–2010, were retrieved from the National Health Insurance Research Database (NHIRD. After excluding patients diagnosed with hip fracture or vertebrate fractures before using inhaled corticosteroid, a total of 11645 patients receiving inhaled corticosteroid therapy were included for this study. Among them, 1134 (9.7% were diagnosed with hip fracture or vertebrate fracture. The statistical results showed that demographic information, chronic respiratory diseases and comorbidities, and corticosteroid-related variables (cumulative dose, mean exposed daily dose, follow-up duration, and exposed duration were significantly different between fracture and nonfracture patients. The clinical decision support systems (CDSSs were designed with integrated genetic algorithm (GA and support vector machine (SVM by training and validating the models with balanced training sets obtained by random and cluster-based undersampling methods and testing with the imbalanced NHIRD dataset. Two different objective functions were adopted for obtaining optimal models with best predictive performance. The predictive performance of the CDSSs exhibits a sensitivity of 69.84–77.00% and an AUC of 0.7495–0.7590. It was concluded that long-term use of inhaled corticosteroids may induce osteoporosis and exhibit higher incidence of hip or vertebrate fractures. The accumulated dose of ICS and

  1. Formative Evaluation of Clinician Experience with Integrating Family History-Based Clinical Decision Support into Clinical Practice

    Directory of Open Access Journals (Sweden)

    Megan Doerr

    2014-03-01

    Full Text Available Family health history is a leading predictor of disease risk. Nonetheless, it is underutilized to guide care and, therefore, is ripe for health information technology intervention. To fill the family health history practice gap, Cleveland Clinic has developed a family health history collection and clinical decision support tool, MyFamily. This report describes the impact and process of implementing MyFamily into primary care, cancer survivorship and cancer genetics clinics. Ten providers participated in semi-structured interviews that were analyzed to identify opportunities for process improvement. Participants universally noted positive effects on patient care, including increases in quality, personalization of care and patient engagement. The impact on clinical workflow varied by practice setting, with differences observed in the ease of integration and the use of specific report elements. Tension between the length of the report and desired detail was appreciated. Barriers and facilitators to the process of implementation were noted, dominated by the theme of increased integration with the electronic medical record. These results fed real-time improvement cycles to reinforce clinician use. This model will be applied in future institutional efforts to integrate clinical genomic applications into practice and may be useful for other institutions considering the implementation of tools for personalizing medical management.

  2. An Application for Mobile Devices Focused on Clinical Decision Support: Diabetes Mellitus Case

    NARCIS (Netherlands)

    Klein, Lucas Felipe; Rigo, Sandro José; Cazella, Silvio César; Ben, Angela Jornada

    2016-01-01

    Clinical decision-making is performed by health professionals and it is currently connected to the need for manual query for these professionals for clinical guidelines, which are generally formed by large text files, which makes this process very slow and laborious. The development of

  3. Reducing duplicate testing: a comparison of two clinical decision support tools.

    Science.gov (United States)

    Procop, Gary W; Keating, Catherine; Stagno, Paul; Kottke-Marchant, Kandice; Partin, Mary; Tuttle, Robert; Wyllie, Robert

    2015-05-01

    Unnecessary duplicate laboratory testing is common and costly. Systems-based means to avert unnecessary testing should be investigated and employed. We compared the effectiveness and cost savings associated with two clinical decision support tools to stop duplicate testing. The Hard Stop required telephone contact with the laboratory and justification to have the duplicate test performed, whereas the Smart Alert allowed the provider to bypass the alert at the point of order entry without justification. The Hard Stop alert was significantly more effective than the Smart Alert (92.3% vs 42.6%, respectively; P < .0001). The cost savings realized per alert activation was $16.08/alert for the Hard Stop alert vs $3.52/alert for the Smart Alert. Structural and process changes that require laboratory contact and justification for duplicate testing are more effective than interventions that allow providers to bypass alerts without justification at point of computerized physician order entry. Copyright© by the American Society for Clinical Pathology.

  4. Critical care nurse practitioners and clinical nurse specialists interface patterns with computer-based decision support systems.

    Science.gov (United States)

    Weber, Scott

    2007-11-01

    The purposes of this review are to examine the types of clinical decision support systems in use and to identify patterns of how critical care advanced practice nurses (APNs) have integrated these systems into their nursing care patient management practices. The decision-making process itself is analyzed with a focus on how automated systems attempt to capture and reflect human decisional processes in critical care nursing, including how systems actually organize and process information to create outcome estimations based on patient clinical indicators and prognosis logarithms. Characteristics of APN clinicians and implications of these characteristics on decision system use, based on the body of decision system user research, are introduced. A review of the Medline, Ovid, CINAHL, and PubMed literature databases was conducted using "clinical decision support systems,"computerized clinical decision making," and "APNs"; an examination of components of several major clinical decision systems was also undertaken. Use patterns among APNs and other clinicians appear to vary; there is a need for original research to examine how APNs actually use these systems in their practices in critical care settings. Because APNs are increasingly responsible for admission to, and transfer from, critical care settings, more understanding is needed on how they interact with this technology and how they see automated decision systems impacting their practices. APNs who practice in critical care settings vary significantly in how they use the clinical decision systems that are in operation in their practice settings. These APNs must have an understanding of their use patterns with these systems and should critically assess whether their patient care decision making is affected by the technology.

  5. Human Cognitive Limitations. Broad, Consistent, Clinical Application of Physiological Principles Will Require Decision Support.

    Science.gov (United States)

    Morris, Alan H

    2018-02-01

    Our education system seems to fail to enable clinicians to broadly understand core physiological principles. The emphasis on reductionist science, including "omics" branches of research, has likely contributed to this decrease in understanding. Consequently, clinicians cannot be expected to consistently make clinical decisions linked to best physiological evidence. This is a large-scale problem with multiple determinants, within an even larger clinical decision problem: the failure of clinicians to consistently link their decisions to best evidence. Clinicians, like all human decision-makers, suffer from significant cognitive limitations. Detailed context-sensitive computer protocols can generate personalized medicine instructions that are well matched to individual patient needs over time and can partially resolve this problem.

  6. Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis.

    Science.gov (United States)

    Khairat, Saif; Marc, David; Crosby, William; Al Sanousi, Ali

    2018-04-18

    Clinical decision support systems (CDSSs) are an integral component of today's health information technologies. They assist with interpretation, diagnosis, and treatment. A CDSS can be embedded throughout the patient safety continuum providing reminders, recommendations, and alerts to health care providers. Although CDSSs have been shown to reduce medical errors and improve patient outcomes, they have fallen short of their full potential. User acceptance has been identified as one of the potential reasons for this shortfall. The purpose of this paper was to conduct a critical review and task analysis of CDSS research and to develop a new framework for CDSS design in order to achieve user acceptance. A critical review of CDSS papers was conducted with a focus on user acceptance. To gain a greater understanding of the problems associated with CDSS acceptance, we conducted a task analysis to identify and describe the goals, user input, system output, knowledge requirements, and constraints from two different perspectives: the machine (ie, the CDSS engine) and the user (ie, the physician). Favorability of CDSSs was based on user acceptance of clinical guidelines, reminders, alerts, and diagnostic suggestions. We propose two models: (1) the user acceptance and system adaptation design model, which includes optimizing CDSS design based on user needs/expectations, and (2) the input-process-output-engagemodel, which reveals to users the processes that govern CDSS outputs. This research demonstrates that the incorporation of the proposed models will improve user acceptance to support the beneficial effects of CDSSs adoption. Ultimately, if a user does not accept technology, this not only poses a threat to the use of the technology but can also pose a threat to the health and well-being of patients. ©Saif Khairat, David Marc, William Crosby, Ali Al Sanousi. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 18.04.2018.

  7. [Which research is needed to support clinical decision-making on integrative medicine? Can comparative effectiveness research close the gap?].

    Science.gov (United States)

    Witt, Claudia M; Huang, Wen-jing; Lao, Lixing; Berman, Brian M

    2013-08-01

    In clinical research on complementary and integrative medicine, experts and scientists have often pursued a research agenda in spite of an incomplete understanding of the needs of end users. Consequently, the majority of previous clinical trials have mainly assessed the efficacy of interventions. Scant data is available on their effectiveness. Comparative effectiveness research (CER) promises to support decision makers by generating evidence that compares the benefits and harms of best care options. This evidence, more generalizable than evidence generated by traditional randomized clinical trials (RCTs), is better suited to inform real-world care decisions. An emphasis on CER supports the development of the evidence base for clinical and policy decision-making. Whereas in most areas of complementary and integrative medicine data on CER is scarce, available acupuncture research already contributes to CER evidence. This paper will introduce CER and make suggestions for future research.

  8. Eliminating Health Care Disparities With Mandatory Clinical Decision Support: The Venous Thromboembolism (VTE) Example.

    Science.gov (United States)

    Lau, Brandyn D; Haider, Adil H; Streiff, Michael B; Lehmann, Christoph U; Kraus, Peggy S; Hobson, Deborah B; Kraenzlin, Franca S; Zeidan, Amer M; Pronovost, Peter J; Haut, Elliott R

    2015-01-01

    All hospitalized patients should be assessed for venous thromboembolism (VTE) risk factors and prescribed appropriate prophylaxis. To improve best-practice VTE prophylaxis prescription for all hospitalized patients, we implemented a mandatory computerized clinical decision support (CCDS) tool. The tool requires completion of checklists to evaluate VTE risk factors and contraindications to pharmacological prophylaxis, and then recommends the risk-appropriate VTE prophylaxis regimen. The objective of the study was to examine the effect of a quality improvement intervention on race-based and sex-based health care disparities across 2 distinct clinical services. This was a retrospective cohort study of a quality improvement intervention. The study included 1942 hospitalized medical patients and 1599 hospitalized adult trauma patients. In this study, the proportion of patients prescribed risk-appropriate, best-practice VTE prophylaxis was evaluated. Racial disparities existed in prescription of best-practice VTE prophylaxis in the preimplementation period between black and white patients on both the trauma (70.1% vs. 56.6%, P=0.025) and medicine (69.5% vs. 61.7%, P=0.015) services. After implementation of the CCDS tool, compliance improved for all patients, and disparities in best-practice prophylaxis prescription between black and white patients were eliminated on both services: trauma (84.5% vs. 85.5%, P=0.99) and medicine (91.8% vs. 88.0%, P=0.082). Similar findings were noted for sex disparities in the trauma cohort. Despite the fact that risk-appropriate prophylaxis should be prescribed equally to all hospitalized patients regardless of race and sex, practice varied widely before our quality improvement intervention. Our CCDS tool eliminated racial disparities in VTE prophylaxis prescription across 2 distinct clinical services. Health information technology approaches to care standardization are effective to eliminate health care disparities.

  9. Computerized clinical decision support systems for therapeutic drug monitoring and dosing: A decision-maker-researcher partnership systematic review

    Directory of Open Access Journals (Sweden)

    Weise-Kelly Lorraine

    2011-08-01

    Full Text Available Abstract Background Some drugs have a narrow therapeutic range and require monitoring and dose adjustments to optimize their efficacy and safety. Computerized clinical decision support systems (CCDSSs may improve the net benefit of these drugs. The objective of this review was to determine if CCDSSs improve processes of care or patient outcomes for therapeutic drug monitoring and dosing. Methods We conducted a decision-maker-researcher partnership systematic review. Studies from our previous review were included, and new studies were sought until January 2010 in MEDLINE, EMBASE, Evidence-Based Medicine Reviews, and Inspec databases. Randomized controlled trials assessing the effect of a CCDSS on process of care or patient outcomes were selected by pairs of independent reviewers. A study was considered to have a positive effect (i.e., CCDSS showed improvement if at least 50% of the relevant study outcomes were statistically significantly positive. Results Thirty-three randomized controlled trials were identified, assessing the effect of a CCDSS on management of vitamin K antagonists (14, insulin (6, theophylline/aminophylline (4, aminoglycosides (3, digoxin (2, lidocaine (1, or as part of a multifaceted approach (3. Cluster randomization was rarely used (18% and CCDSSs were usually stand-alone systems (76% primarily used by physicians (85%. Overall, 18 of 30 studies (60% showed an improvement in the process of care and 4 of 19 (21% an improvement in patient outcomes. All evaluable studies assessing insulin dosing for glycaemic control showed an improvement. In meta-analysis, CCDSSs for vitamin K antagonist dosing significantly improved time in therapeutic range. Conclusions CCDSSs have potential for improving process of care for therapeutic drug monitoring and dosing, specifically insulin and vitamin K antagonist dosing. However, studies were small and generally of modest quality, and effects on patient outcomes were uncertain, with no convincing

  10. Cost-effectiveness of clinical decision support system in improving maternal health care in Ghana.

    Directory of Open Access Journals (Sweden)

    Maxwell Ayindenaba Dalaba

    Full Text Available This paper investigated the cost-effectiveness of a computer-assisted Clinical Decision Support System (CDSS in the identification of maternal complications in Ghana.A cost-effectiveness analysis was performed in a before- and after-intervention study. Analysis was conducted from the provider's perspective. The intervention area was the Kassena- Nankana district where computer-assisted CDSS was used by midwives in maternal care in six selected health centres. Six selected health centers in the Builsa district served as the non-intervention group, where the normal Ghana Health Service activities were being carried out.Computer-assisted CDSS increased the detection of pregnancy complications during antenatal care (ANC in the intervention health centres (before-intervention = 9 /1,000 ANC attendance; after-intervention = 12/1,000 ANC attendance; P-value = 0.010. In the intervention health centres, there was a decrease in the number of complications during labour by 1.1%, though the difference was not statistically significant (before-intervention =107/1,000 labour clients; after-intervention = 96/1,000 labour clients; P-value = 0.305. Also, at the intervention health centres, the average cost per pregnancy complication detected during ANC (cost -effectiveness ratio decreased from US$17,017.58 (before-intervention to US$15,207.5 (after-intervention. Incremental cost -effectiveness ratio (ICER was estimated at US$1,142. Considering only additional costs (cost of computer-assisted CDSS, cost per pregnancy complication detected was US$285.Computer -assisted CDSS has the potential to identify complications during pregnancy and marginal reduction in labour complications. Implementing computer-assisted CDSS is more costly but more effective in the detection of pregnancy complications compared to routine maternal care, hence making the decision to implement CDSS very complex. Policy makers should however be guided by whether the additional benefit is worth

  11. Using statistical anomaly detection models to find clinical decision support malfunctions.

    Science.gov (United States)

    Ray, Soumi; McEvoy, Dustin S; Aaron, Skye; Hickman, Thu-Trang; Wright, Adam

    2018-05-11

    Malfunctions in Clinical Decision Support (CDS) systems occur due to a multitude of reasons, and often go unnoticed, leading to potentially poor outcomes. Our goal was to identify malfunctions within CDS systems. We evaluated 6 anomaly detection models: (1) Poisson Changepoint Model, (2) Autoregressive Integrated Moving Average (ARIMA) Model, (3) Hierarchical Divisive Changepoint (HDC) Model, (4) Bayesian Changepoint Model, (5) Seasonal Hybrid Extreme Studentized Deviate (SHESD) Model, and (6) E-Divisive with Median (EDM) Model and characterized their ability to find known anomalies. We analyzed 4 CDS alerts with known malfunctions from the Longitudinal Medical Record (LMR) and Epic® (Epic Systems Corporation, Madison, WI, USA) at Brigham and Women's Hospital, Boston, MA. The 4 rules recommend lead testing in children, aspirin therapy in patients with coronary artery disease, pneumococcal vaccination in immunocompromised adults and thyroid testing in patients taking amiodarone. Poisson changepoint, ARIMA, HDC, Bayesian changepoint and the SHESD model were able to detect anomalies in an alert for lead screening in children and in an alert for pneumococcal conjugate vaccine in immunocompromised adults. EDM was able to detect anomalies in an alert for monitoring thyroid function in patients on amiodarone. Malfunctions/anomalies occur frequently in CDS alert systems. It is important to be able to detect such anomalies promptly. Anomaly detection models are useful tools to aid such detections.

  12. Evaluation of real-time clinical decision support systems for platelet and cryoprecipitate orders.

    Science.gov (United States)

    Collins, Ryan A; Triulzi, Darrell J; Waters, Jonathan H; Reddy, Vivek; Yazer, Mark H

    2014-01-01

    To evaluate cryoprecipitate and platelet ordering practices after the implementation of real-time clinical decision support systems (CDSSs) in a computerized physician order entry (CPOE) system. Uniform platelet and cryoprecipitate transfusion thresholds were implemented at 11 hospitals in a regional health care system with a common CPOE system. Over 6 months, a variety of information was collected on the ordering physicians and the number of alerts generated by the CDSSs when these products were ordered outside of the institutional guidelines. There were 1,889 orders for platelets and 152 orders for cryoprecipitate placed in 6 months. Of these, 1,102 (58.3%) platelet and 74 (48.7%) cryoprecipitate orders triggered an alert. The proportion of orders canceled after an alert was generated ranged from 13.5% to 17.9% for platelets and 0% to 50.0% for cryoprecipitate orders. CDSS alerts reduce, but do not eliminate, platelet and cryoprecipitate transfusions that do not meet institutional guidelines.

  13. The Use of Clinical Decision Support in Reducing Diagnosis of and Treatment of Asymptomatic Bacteriuria.

    Science.gov (United States)

    Keller, Sara C; Feldman, Leonard; Smith, Janessa; Pahwa, Amit; Cosgrove, Sara E; Chida, Natasha

    2018-06-01

    Clinical decision support (CDS) embedded within the electronic health record (EHR) is a potential antibiotic stewardship strategy for hospitalized patients. Reduction in urine testing and treating asymptomatic bacteriuria (ASB) is an important strategy to promote antibiotic stewardship. We created an intervention focused on reducing urine testing for asymptomatic patients at a large tertiary care center. The objective of this study was to design an intervention to reduce unnecessary urinalysis and urine culture (UC) orders as well as the treatment of ASB. We performed a quasiexperimental study among adult inpatients at a single academic institution. We implemented a bundled intervention, including information broadcast in newsletters, hospitalwide screensavers, and passive CDS messages in the EHR. We investigated the impact of this strategy on urinalysis, UC orders, and on the treatment of ASB by using an interrupted time series analysis. Our intervention led to reduced UC order as well as reduced antibiotic orders in response to urinalysis orders and UC results. This easily implementable bundle may play an important role as an antibiotic stewardship strategy. © 2018 Society of Hospital Medicine.

  14. A decision support system for medical mobile devices based on clinical guidelines for tuberculosis

    NARCIS (Netherlands)

    Cazella, Silvio César; Feyh, Rafael; Ben, Angela Jornada

    2014-01-01

    The decision making process conducted by health professionals is strongly linked to the consultations of clinical guidelines, generally available in large text files, making the access to the information very laborious and time consuming. The health area is very fertile for the emergence of

  15. An ontology-driven, case-based clinical decision support model for removable partial denture design

    Science.gov (United States)

    Chen, Qingxiao; Wu, Ji; Li, Shusen; Lyu, Peijun; Wang, Yong; Li, Miao

    2016-06-01

    We present the initial work toward developing a clinical decision support model for specific design of removable partial dentures (RPDs) in dentistry. We developed an ontological paradigm to represent knowledge of a patient’s oral conditions and denture component parts. During the case-based reasoning process, a cosine similarity algorithm was applied to calculate similarity values between input patients and standard ontology cases. A group of designs from the most similar cases were output as the final results. To evaluate this model, the output designs of RPDs for 104 randomly selected patients were compared with those selected by professionals. An area under the curve of the receiver operating characteristic (AUC-ROC) was created by plotting true-positive rates against the false-positive rate at various threshold settings. The precision at position 5 of the retrieved cases was 0.67 and at the top of the curve it was 0.96, both of which are very high. The mean average of precision (MAP) was 0.61 and the normalized discounted cumulative gain (NDCG) was 0.74 both of which confirmed the efficient performance of our model. All the metrics demonstrated the efficiency of our model. This methodology merits further research development to match clinical applications for designing RPDs. This paper is organized as follows. After the introduction and description of the basis for the paper, the evaluation and results are presented in Section 2. Section 3 provides a discussion of the methodology and results. Section 4 describes the details of the ontology, similarity algorithm, and application.

  16. A fuzzy logic decision support system for assessing clinical nutritional risk

    Directory of Open Access Journals (Sweden)

    Ali Mohammad Hadianfard

    2015-04-01

    Full Text Available Introduction: Studies have indicated a global high prevalence of hospital malnutrition on admission and during hospitalization. Clinical Nutritional Risk Screen (CNRS is a way to identify malnutrition and manage nutritional interventions. Several traditional and non-computer based tools have been suggested for screening nutritional risk levels. The present study was an attempt to employ a computer based fuzzy model decision support system as a nutrition-screening tool for inpatients. Method: This is an applied modeling study. The system architecture was designed based on the fuzzy logic model including input data, inference engine, and output. A clinical nutritionist entered nineteen input variables using a windows-based graphical user interface. The inference engine was involved with knowledge obtained from literature and the construction of ‘IF-THEN’ rules. The output of the system was stratification of patients into four risk levels from ‘No’ to ‘High’ where a number was also allocated to them as a nutritional risk grade. All patients (121 people admitted during implementing the system participated in testing the model. The classification tests were used to measure the CNRS fuzzy model performance. IBM SPSS version 21 was utilized as a tool for data analysis with α = 0.05 as a significance level. Results: Results showed that sensitivity, specificity, accuracy, and precision of the fuzzy model performance were 91.67% (±4.92, 76% (±7.6, 88.43% (±5.7, and 93.62% (±4.32, respectively. Instant performance on admission and very low probability of mistake in predicting malnutrition risk level may justify using the model in hospitals. Conclusion: To conclude, the fuzzy model-screening tool is based on multiple nutritional risk factors, having the capability of classifying inpatients into several nutritional risk levels and identifying the level of required nutritional intervention.

  17. Evaluation of a clinical decision support algorithm for patient-specific childhood immunization.

    Science.gov (United States)

    Zhu, Vivienne J; Grannis, Shaun J; Tu, Wanzhu; Rosenman, Marc B; Downs, Stephen M

    2012-09-01

    To evaluate the effectiveness of a clinical decision support system (CDSS) implementing standard childhood immunization guidelines, using real-world patient data from the Regenstrief Medical Record System (RMRS). Study subjects were age 6-years or younger in 2008 and had visited the pediatric clinic on the campus of Wishard Memorial Hospital. Immunization records were retrieved from the RMRS for 135 randomly selected pediatric patients. We compared vaccine recommendations from the CDSS for both eligible and recommended timelines, based on the child's date of birth and vaccine history, to recommendations from registered nurses who routinely selected vaccines for administration in a busy inner city hospital, using the same date of birth and vaccine history. Aggregated and stratified agreement and Kappa statistics were reported. The reasons for disagreement between suggestions from the CDSS and nurses were also identified. For the 135 children, a total of 1215 vaccination suggestions were generated by nurses and were compared to the recommendations of the CDSS. The overall agreement rates were 81.3% and 90.6% for the eligible and recommended timelines, respectively. The overall Kappa values were 0.63 for the eligible timeline and 0.80 for the recommended timeline. Common reasons for disagreement between the CDSS and nurses were: (1) missed vaccination opportunities by nurses, (2) nurses sometimes suggested a vaccination before the minimal age and minimal waiting interval, (3) nurses usually did not validate patient immunization history, and (4) nurses sometimes gave an extra vaccine dose. Our childhood immunization CDSS can assist providers in delivering accurate childhood vaccinations. Copyright © 2012 Elsevier B.V. All rights reserved.

  18. Evaluation of prescriber responses to pharmacogenomics clinical decision support for thiopurine S-methyltransferase testing.

    Science.gov (United States)

    Ubanyionwu, Samuel; Formea, Christine M; Anderson, Benjamin; Wix, Kelly; Dierkhising, Ross; Caraballo, Pedro J

    2018-02-15

    Results of a study of prescribers' responses to a pharmacogenomics-based clinical decision support (CDS) alert designed to prompt thiopurine S -methyltransferase (TPMT) status testing are reported. A single-center, retrospective, chart review-based study was conducted to evaluate prescriber compliance with a pretest CDS alert that warned of potential thiopurine drug toxicity resulting from deficient TPMT activity due to TPMT gene polymorphism. The CDS alert was triggered when prescribers ordered thiopurine drugs for patients whose records did not indicate TPMT status or when historical thiopurine use was documented in the electronic health record. The alert pop-up also provided a link to online educational resources to guide thiopurine dosing calculations. During the 9-month study period, 500 CDS alerts were generated: in 101 cases (20%), TPMT phenotyping or TPMT genotyping was ordered; in 399 cases (80%), testing was not ordered. Multivariable regression analysis indicated that documentation of historical thiopurine use was the only independent predictor of test ordering. Among the 99 patients tested subsequent to CDS alerts, 70 (71%) had normal TPMT activity, 29 (29%) had intermediate activity, and none had deficient activity. The online resources provided thiopurine dosing recommendations applicable to 24 patients, but only 3 were prescribed guideline-supported doses after CDS alerts. The pretest CDS rule resulted in a large proportion of neglected alerts due to poor alerting accuracy and consequent alert fatigue. Prescriber usage of online thiopurine dosing resources was low. Copyright © 2018 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

  19. Transforming User Needs into Functional Requirements for an Antibiotic Clinical Decision Support System

    Science.gov (United States)

    Bright, T.J.

    2013-01-01

    Summary Background Many informatics studies use content analysis to generate functional requirements for system development. Explication of this translational process from qualitative data to functional requirements can strengthen the understanding and scientific rigor when applying content analysis in informatics studies. Objective To describe a user-centered approach transforming emergent themes derived from focus group data into functional requirements for informatics solutions and to illustrate these methods to the development of an antibiotic clinical decision support system (CDS). Methods The approach consisted of five steps: 1) identify unmet therapeutic planning information needs via Focus Group Study-I, 2) develop a coding framework of therapeutic planning themes to refine the domain scope to antibiotic therapeutic planning, 3) identify functional requirements of an antibiotic CDS system via Focus Group Study-II, 4) discover informatics solutions and functional requirements from coded data, and 5) determine the types of information needed to support the antibiotic CDS system and link with the identified informatics solutions and functional requirements. Results The coding framework for Focus Group Study-I revealed unmet therapeutic planning needs. Twelve subthemes emerged and were clustered into four themes; analysis indicated a need for an antibiotic CDS intervention. Focus Group Study-II included five types of information needs. Comments from the Barrier/Challenge to information access and Function/Feature themes produced three informatics solutions and 13 functional requirements of an antibiotic CDS system. Comments from the Patient, Institution, and Domain themes generated required data elements for each informatics solution. Conclusion This study presents one example explicating content analysis of focus group data and the analysis process to functional requirements from narrative data. Illustration of this 5-step method was used to develop an

  20. Free and open source enabling technologies for patient-centric, guideline-based clinical decision support: a survey.

    Science.gov (United States)

    Leong, T Y; Kaiser, K; Miksch, S

    2007-01-01

    Guideline-based clinical decision support is an emerging paradigm to help reduce error, lower cost, and improve quality in evidence-based medicine. The free and open source (FOS) approach is a promising alternative for delivering cost-effective information technology (IT) solutions in health care. In this paper, we survey the current FOS enabling technologies for patient-centric, guideline-based care, and discuss the current trends and future directions of their role in clinical decision support. We searched PubMed, major biomedical informatics websites, and the web in general for papers and links related to FOS health care IT systems. We also relied on our background and knowledge for specific subtopics. We focused on the functionalities of guideline modeling tools, and briefly examined the supporting technologies for terminology, data exchange and electronic health record (EHR) standards. To effectively support patient-centric, guideline-based care, the computerized guidelines and protocols need to be integrated with existing clinical information systems or EHRs. Technologies that enable such integration should be accessible, interoperable, and scalable. A plethora of FOS tools and techniques for supporting different knowledge management and quality assurance tasks involved are available. Many challenges, however, remain in their implementation. There are active and growing trends of deploying FOS enabling technologies for integrating clinical guidelines, protocols, and pathways into the main care processes. The continuing development and maturation of such technologies are likely to make increasingly significant contributions to patient-centric, guideline-based clinical decision support.

  1. Which research is needed to support clinical decision-making on integrative medicine?- Can comparative effectiveness research close the gap?

    Science.gov (United States)

    Witt, Claudia M; Huang, Wen-jing; Lao, Lixing; Bm, Berman

    2012-10-01

    In clinical research on complementary and integrative medicine, experts and scientists have often pursued a research agenda in spite of an incomplete understanding of the needs of end users. Consequently, the majority of previous clinical trials have mainly assessed the efficacy of interventions. Scant data is available on their effectiveness. Comparative effectiveness research (CER) promises to support decision makers by generating evidence that compares the benefits and harms of the best care options. This evidence, more generalizable than the evidence generated by traditional randomized controlled trials (RCTs), is better suited to inform real-world care decisions. An emphasis on CER supports the development of the evidence base for clinical and policy decision-making. Whereas in most areas of complementary and integrative medicine data on comparative effectiveness is scarce, available acupuncture research already contributes to CER evidence. This paper will introduce CER and make suggestions for future research.

  2. Design and implementation of a decision support system for breast cancer treatment based on clinical practice guidelines

    International Nuclear Information System (INIS)

    Skevofilakas, M.T.; Nikita, K.S.; Templaleksis, P.H.; Birbas, K.N.; Kaklamanos, I.G.; Bonatsos, G.N.

    2007-01-01

    Evidence based medicine is the clinical practice that uses medical data and proof in order to make efficient clinical decisions. Information technology (IT) can play a crucial role in exploiting the huge size of raw medical data involved. In an attempt to improve clinical efficacy, health care society nowadays also utilizes a new assistant, clinical guidelines. Our research concerns the medical domain of the breast cancer disease. Our research's focus is twofold; our primary goal is to ensure consistency in clinical practice by importing clinical guidelines in an IT driven decision support system (DSS). Furthermore, we seek to improve visualization of disease specific, clinical data, providing for it's faster and more efficient use. (orig.)

  3. An Automated System for Generating Situation-Specific Decision Support in Clinical Order Entry from Local Empirical Data

    Science.gov (United States)

    Klann, Jeffrey G.

    2011-01-01

    Clinical Decision Support is one of the only aspects of health information technology that has demonstrated decreased costs and increased quality in healthcare delivery, yet it is extremely expensive and time-consuming to create, maintain, and localize. Consequently, a majority of health care systems do not utilize it, and even when it is…

  4. Implementation of clinical decision support in young children with acute gastroenteritis: a randomized controlled trial at the emergency department

    NARCIS (Netherlands)

    D.H.F. Geurts (Dorien); E. De Vos-Kerkhof (Evelien); S. Polinder (Suzanne); E.W. Steyerberg (Ewout); J. van der Lei (Johan); H.A. Moll (Henriëtte); R. Oostenbrink (Rianne)

    2017-01-01

    textabstractAcute gastroenteritis (AGE) is one of the most frequent reasons for young children to visit emergency departments (EDs). We aimed to evaluate (1) feasibility of a nurse-guided clinical decision support system for rehydration treatment in children with AGE and (2) the impact on

  5. Decision support systems in clinical practice: The case of venous thromboembolism prevention.

    Science.gov (United States)

    Nazarenko, G I; Kleymenova, E B; Payushik, S A; Otdelenov, V A; Sychev, D A; Yashina, L P

    2015-01-01

    Today medicine is facing a "knowledge crisis" in that explosively expanding medical knowledge encounters limited abilities to disseminate new practices [1]. Clinical practice guidelines (CPGs) are intended to promote high standards of care in specific areas of medicine by summarizing best clinical practice based on careful reviews of current research. However, doctors are often short of time to study these documents and check their updates, have little motivation for strict adherence to them. A systematic review of 11 studies reporting on 29 recommendations has found that median adherence to all recommendations was 34%, suggesting that potential benefits for patients from health research may be lost [2].Clinical decision support systems (CDSS) can serve as a knowledge translation tool, mediator between clinical guidelines and physicians by providing the right information to the right person at the right time. To evaluate the effectiveness of implementation of international and national CPGs for venous thromboembolism (VTE) prevention with the help of CDSS in a general hospital. A multifunctional CDSS based on national and international guidelines on the VTE prevention was developed and implemented in the Medical Center of the Bank of Russia (MC). The system has the following functionalities: 1) it supports the decision on the VTE prevention based on individual risk assessment of thrombosis (scales of Caprini, Rogers and Khorana, Padua Prediction Score, additional risk factors) and bleeding (IMPROVE scale for non-surgical patients, major bleeding scale for surgical patients and major orthopedic surgeries, hemorrhagic complications risk in cancer patients); 2) generates the summary containing the grade of recommendations and the level of evidence, personalized recommendations on regimen and duration of preventive antithrombotic therapy, dose correction according to creatinine clearance; 3) provides an audit form for and statistical analysis of VTE cases; 3

  6. A feasibility study for a clinical decision support system prompting HIV testing.

    Science.gov (United States)

    Chadwick, D R; Hall, C; Rae, C; Rayment, Ml; Branch, M; Littlewood, J; Sullivan, A

    2017-07-01

    Levels of undiagnosed HIV infection and late presentation remain high globally despite attempts to increase testing. The objective of this study was to evaluate a risk-based prototype application to prompt HIV testing when patients undergo routine blood tests. Two computer physician order entry (CPOE) systems were modified using the application to prompt health care workers (HCWs) to add an HIV test when other tests selected suggested that the patient was at higher risk of HIV infection. The application was applied for a 3-month period in two areas, in a large London hospital and in general practices in Teesside/North Yorkshire. At the end of the evaluation period, HCWs were interviewed to assess the usability and acceptability of the prompt. Numbers of HIV tests ordered in the general practice areas were also compared before and after the prompt's introduction. The system was found to be both useable and generally acceptable to hospital doctors, general practitioners and nurse practitioners, with little evidence of prompt/alert fatigue. The issue of the prompt appearing late in the patient consultation did lead to some difficulties, particularly around discussion of the test and consent. In the general practices, around 1 in 10 prompts were accepted and there was a 6% increase in testing rates over the 3-month study period (P = 0.169). Using a CPOE-based clinical decision support application to prompt HIV testing appears both feasible and acceptable to HCWs. Refining the application to provide more accurate risk stratification is likely to make it more effective. © 2016 British HIV Association.

  7. Integrating usability testing and think-aloud protocol analysis with "near-live" clinical simulations in evaluating clinical decision support.

    Science.gov (United States)

    Li, Alice C; Kannry, Joseph L; Kushniruk, Andre; Chrimes, Dillon; McGinn, Thomas G; Edonyabo, Daniel; Mann, Devin M

    2012-11-01

    Usability evaluations can improve the usability and workflow integration of clinical decision support (CDS). Traditional usability testing using scripted scenarios with think-aloud protocol analysis provide a useful but incomplete assessment of how new CDS tools interact with users and clinical workflow. "Near-live" clinical simulations are a newer usability evaluation tool that more closely mimics clinical workflow and that allows for a complementary evaluation of CDS usability as well as impact on workflow. This study employed two phases of testing a new CDS tool that embedded clinical prediction rules (an evidence-based medicine tool) into primary care workflow within a commercial electronic health record. Phase I applied usability testing involving "think-aloud" protocol analysis of 8 primary care providers encountering several scripted clinical scenarios. Phase II used "near-live" clinical simulations of 8 providers interacting with video clips of standardized trained patient actors enacting the clinical scenario. In both phases, all sessions were audiotaped and had screen-capture software activated for onscreen recordings. Transcripts were coded using qualitative analysis methods. In Phase I, the impact of the CDS on navigation and workflow were associated with the largest volume of negative comments (accounting for over 90% of user raised issues) while the overall usability and the content of the CDS were associated with the most positive comments. However, usability had a positive-to-negative comment ratio of only 0.93 reflecting mixed perceptions about the usability of the CDS. In Phase II, the duration of encounters with simulated patients was approximately 12 min with 71% of the clinical prediction rules being activated after half of the visit had already elapsed. Upon activation, providers accepted the CDS tool pathway 82% of times offered and completed all of its elements in 53% of all simulation cases. Only 12.2% of encounter time was spent using the

  8. Physicians' perception of alternative displays of clinical research evidence for clinical decision support - A study with case vignettes.

    Science.gov (United States)

    Slager, Stacey L; Weir, Charlene R; Kim, Heejun; Mostafa, Javed; Del Fiol, Guilherme

    2017-07-01

    To design alternate information displays that present summaries of clinical trial results to clinicians to support decision-making; and to compare the displays according to efficacy and acceptability. A 6-between (information display presentation order) by 3-within (display type) factorial design. Two alternate displays were designed based on Information Foraging theory: a narrative summary that reduces the content to a few sentences; and a table format that structures the display according to the PICO (Population, Intervention, Comparison, Outcome) framework. The designs were compared with the summary display format available in PubMed. Physicians were asked to review five clinical studies retrieved for a case vignette; and were presented with the three display formats. Participants were asked to rate their experience with each of the information displays according to a Likert scale questionnaire. Twenty physicians completed the study. Overall, participants rated the table display more highly than either the text summary or PubMed's summary format (5.9vs. 5.4vs. 3.9 on a scale between 1 [strongly disagree] and 7 [strongly agree]). Usefulness ratings of seven pieces of information, i.e. patient population, patient age range, sample size, study arm, primary outcome, results of primary outcome, and conclusion, were high (average across all items=4.71 on a 1 to 5 scale, with 1=not at all useful and 5=very useful). Study arm, primary outcome, and conclusion scored the highest (4.9, 4.85, and 4.85 respectively). Participants suggested additional details such as rate of adverse effects. The table format reduced physicians' perceived cognitive effort when quickly reviewing clinical trial information and was more favorably received by physicians than the narrative summary or PubMed's summary format display. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Implementation of a Clinical Decision Support Tool for Stool Cultures and Parasitological Studies in Hospitalized Patients.

    Science.gov (United States)

    Nikolic, D; Richter, S S; Asamoto, K; Wyllie, R; Tuttle, R; Procop, G W

    2017-12-01

    There is substantial evidence that stool culture and parasitological examinations are of minimal to no value after 3 days of hospitalization. We implemented and studied the impact of a clinical decision support tool (CDST) to decrease the number of unnecessary stool cultures (STCUL), ova/parasite (O&P) examinations, and Giardia / Cryptosporidium enzyme immunoassay screens (GC-EIA) performed for patients hospitalized >3 days. We studied the frequency of stool studies ordered before or on day 3 and after day 3 of hospitalization (i.e., categorical orders/total number of orders) before and after this intervention and denoted the numbers and types of microorganisms detected within those time frames. This intervention, which corresponded to a custom-programmed hard-stop alert tool in the Epic hospital information system, allowed providers to override the intervention by calling the laboratory, if testing was deemed medically necessary. Comparative statistics were employed to determine significance, and cost savings were estimated based on our internal costs. Before the intervention, 129/670 (19.25%) O&P examinations, 47/204 (23.04%) GC-EIA, and 249/1,229 (20.26%) STCUL were ordered after 3 days of hospitalization. After the intervention, 46/521 (8.83%) O&P examinations, 27/157 (17.20%) GC-EIA, and 106/1,028 (10.31%) STCUL were ordered after 3 days of hospitalization. The proportions of reductions in the number of tests performed after 3 days and the associated P values were 54.1% for O&P examinations ( P < 0.0001), 22.58% for GC-EIA ( P = 0.2807), and 49.1% for STCUL ( P < 0.0001). This was estimated to have resulted in $8,108.84 of cost savings. The electronic CDST resulted in a substantial reduction in the number of evaluations of stool cultures and the number of parasitological examinations for patients hospitalized for more than 3 days and in a cost savings while retaining the ability of the clinician to obtain these tests if clinically indicated. Copyright © 2017

  10. Usability evaluation of a clinical decision support tool for osteoporosis disease management

    Directory of Open Access Journals (Sweden)

    Newton David

    2010-12-01

    Full Text Available Abstract Background Osteoporosis affects over 200 million people worldwide at a high cost to healthcare systems. Although guidelines are available, patients are not receiving appropriate diagnostic testing or treatment. Findings from a systematic review of osteoporosis interventions and a series of focus groups were used to develop a functional multifaceted tool that can support clinical decision-making in osteoporosis disease management at the point of care. The objective of our study was to assess how well the prototype met functional goals and usability needs. Methods We conducted a usability study for each component of the tool--the Best Practice Recommendation Prompt (BestPROMPT, the Risk Assessment Questionnaire (RAQ, and the Customised Osteoporosis Education (COPE sheet--using the framework described by Kushniruk and Patel. All studies consisted of one-on-one sessions with a moderator using a standardised worksheet. Sessions were audio- and video-taped and transcribed verbatim. Data analysis consisted of a combination of qualitative and quantitative analyses. Results In study 1, physicians liked that the BestPROMPT can provide customised recommendations based on risk factors identified from the RAQ. Barriers included lack of time to use the tool, the need to alter clinic workflow to enable point-of-care use, and that the tool may disrupt the real reason for the visit. In study 2, patients completed the RAQ in a mean of 6 minutes, 35 seconds. Of the 42 critical incidents, 60% were navigational and most occurred when the first nine participants were using the stylus pen; no critical incidents were observed with the last six participants that used the touch screen. Patients thought that the RAQ questions were easy to read and understand, but they found it difficult to initiate the questionnaire. Suggestions for improvement included improving aspects of the interface and navigation. The results of study 3 showed that most patients were able

  11. Usability of clinical decision support system as a facilitator for learning the assistive technology adaptation process.

    Science.gov (United States)

    Danial-Saad, Alexandra; Kuflik, Tsvi; Weiss, Patrice L Tamar; Schreuer, Naomi

    2016-01-01

    The aim of this study was to evaluate the usability of Ontology Supported Computerized Assistive Technology Recommender (OSCAR), a Clinical Decision Support System (CDSS) for the assistive technology adaptation process, its impact on learning the matching process, and to determine the relationship between its usability and learnability. Two groups of expert and novice clinicians (total, n = 26) took part in this study. Each group filled out system usability scale (SUS) to evaluate OSCAR's usability. The novice group completed a learning questionnaire to assess OSCAR's effect on their ability to learn the matching process. Both groups rated OSCAR's usability as "very good", (M [SUS] = 80.7, SD = 11.6, median = 83.7) by the novices, and (M [SUS] = 81.2, SD = 6.8, median = 81.2) by the experts. The Mann-Whitney results indicated that no significant differences were found between the expert and novice groups in terms of OSCAR's usability. A significant positive correlation existed between the usability of OSCAR and the ability to learn the adaptation process (rs = 0.46, p = 0.04). Usability is an important factor in the acceptance of a system. The successful application of user-centered design principles during the development of OSCAR may serve as a case study that models the significant elements to be considered, theoretically and practically in developing other systems. Implications for Rehabilitation Creating a CDSS with a focus on its usability is an important factor for its acceptance by its users. Successful usability outcomes can impact the learning process of the subject matter in general, and the AT prescription process in particular. The successful application of User-Centered Design principles during the development of OSCAR may serve as a case study that models the significant elements to be considered, theoretically and practically. The study emphasizes the importance of close collaboration between the developers and

  12. Radwaste Decision Support System

    International Nuclear Information System (INIS)

    Westrom, G.; Vance, J.N.; Gelhaus, F.E.

    1989-01-01

    The purpose of the Radwaste Decision Support System (RDSS) is to provide expert advice, analysis results and instructional material relative to the treatment, handling, transport and disposal of low-level radioactive waste produced in nuclear power plants. This functional specification addresses the following topics: Functions of the RDSS, Relationships and interfaces between the function, Development of the decisions and logic tree structures embodied in waste management, Elements of the database and the characteristics required to support the decision-making process, Specific User requirements for the RDSS, Development of the user interface, Basic software architecture, and Concepts for the RDSS usage including updating and maintenance

  13. "Many miles to go …": a systematic review of the implementation of patient decision support interventions into routine clinical practice.

    Science.gov (United States)

    Elwyn, Glyn; Scholl, Isabelle; Tietbohl, Caroline; Mann, Mala; Edwards, Adrian G K; Clay, Catharine; Légaré, France; van der Weijden, Trudy; Lewis, Carmen L; Wexler, Richard M; Frosch, Dominick L

    2013-01-01

    Two decades of research has established the positive effect of using patient-targeted decision support interventions: patients gain knowledge, greater understanding of probabilities and increased confidence in decisions. Yet, despite their efficacy, the effectiveness of these decision support interventions in routine practice has yet to be established; widespread adoption has not occurred. The aim of this review was to search for and analyze the findings of published peer-reviewed studies that investigated the success levels of strategies or methods where attempts were made to implement patient-targeted decision support interventions into routine clinical settings. An electronic search strategy was devised and adapted for the following databases: ASSIA, CINAHL, Embase, HMIC, Medline, Medline-in-process, OpenSIGLE, PsycINFO, Scopus, Social Services Abstracts, and the Web of Science. In addition, we used snowballing techniques. Studies were included after dual independent assessment. After assessment, 5322 abstracts yielded 51 articles for consideration. After examining full-texts, 17 studies were included and subjected to data extraction. The approach used in all studies was one where clinicians and their staff used a referral model, asking eligible patients to use decision support. The results point to significant challenges to the implementation of patient decision support using this model, including indifference on the part of health care professionals. This indifference stemmed from a reported lack of confidence in the content of decision support interventions and concern about disruption to established workflows, ultimately contributing to organizational inertia regarding their adoption. It seems too early to make firm recommendations about how best to implement patient decision support into routine practice because approaches that use a 'referral model' consistently report difficulties. We sense that the underlying issues that militate against the use of

  14. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: methods of a decision-maker-researcher partnership systematic review.

    Science.gov (United States)

    Haynes, R Brian; Wilczynski, Nancy L

    2010-02-05

    Computerized clinical decision support systems are information technology-based systems designed to improve clinical decision-making. As with any healthcare intervention with claims to improve process of care or patient outcomes, decision support systems should be rigorously evaluated before widespread dissemination into clinical practice. Engaging healthcare providers and managers in the review process may facilitate knowledge translation and uptake. The objective of this research was to form a partnership of healthcare providers, managers, and researchers to review randomized controlled trials assessing the effects of computerized decision support for six clinical application areas: primary preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic disease management, diagnostic test ordering and interpretation, and acute care management; and to identify study characteristics that predict benefit. The review was undertaken by the Health Information Research Unit, McMaster University, in partnership with Hamilton Health Sciences, the Hamilton, Niagara, Haldimand, and Brant Local Health Integration Network, and pertinent healthcare service teams. Following agreement on information needs and interests with decision-makers, our earlier systematic review was updated by searching Medline, EMBASE, EBM Review databases, and Inspec, and reviewing reference lists through 6 January 2010. Data extraction items were expanded according to input from decision-makers. Authors of primary studies were contacted to confirm data and to provide additional information. Eligible trials were organized according to clinical area of application. We included randomized controlled trials that evaluated the effect on practitioner performance or patient outcomes of patient care provided with a computerized clinical decision support system compared with patient care without such a system. Data will be summarized using descriptive summary measures, including proportions

  15. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: Methods of a decision-maker-researcher partnership systematic review

    Directory of Open Access Journals (Sweden)

    Wilczynski Nancy L

    2010-02-01

    Full Text Available Abstract Background Computerized clinical decision support systems are information technology-based systems designed to improve clinical decision-making. As with any healthcare intervention with claims to improve process of care or patient outcomes, decision support systems should be rigorously evaluated before widespread dissemination into clinical practice. Engaging healthcare providers and managers in the review process may facilitate knowledge translation and uptake. The objective of this research was to form a partnership of healthcare providers, managers, and researchers to review randomized controlled trials assessing the effects of computerized decision support for six clinical application areas: primary preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic disease management, diagnostic test ordering and interpretation, and acute care management; and to identify study characteristics that predict benefit. Methods The review was undertaken by the Health Information Research Unit, McMaster University, in partnership with Hamilton Health Sciences, the Hamilton, Niagara, Haldimand, and Brant Local Health Integration Network, and pertinent healthcare service teams. Following agreement on information needs and interests with decision-makers, our earlier systematic review was updated by searching Medline, EMBASE, EBM Review databases, and Inspec, and reviewing reference lists through 6 January 2010. Data extraction items were expanded according to input from decision-makers. Authors of primary studies were contacted to confirm data and to provide additional information. Eligible trials were organized according to clinical area of application. We included randomized controlled trials that evaluated the effect on practitioner performance or patient outcomes of patient care provided with a computerized clinical decision support system compared with patient care without such a system. Results Data will be summarized

  16. Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach.

    Science.gov (United States)

    Marco-Ruiz, Luis; Pedrinaci, Carlos; Maldonado, J A; Panziera, Luca; Chen, Rong; Bellika, J Gustav

    2016-08-01

    The high costs involved in the development of Clinical Decision Support Systems (CDSS) make it necessary to share their functionality across different systems and organizations. Service Oriented Architectures (SOA) have been proposed to allow reusing CDSS by encapsulating them in a Web service. However, strong barriers in sharing CDS functionality are still present as a consequence of lack of expressiveness of services' interfaces. Linked Services are the evolution of the Semantic Web Services paradigm to process Linked Data. They aim to provide semantic descriptions over SOA implementations to overcome the limitations derived from the syntactic nature of Web services technologies. To facilitate the publication, discovery and interoperability of CDS services by evolving them into Linked Services that expose their interfaces as Linked Data. We developed methods and models to enhance CDS SOA as Linked Services that define a rich semantic layer based on machine interpretable ontologies that powers their interoperability and reuse. These ontologies provided unambiguous descriptions of CDS services properties to expose them to the Web of Data. We developed models compliant with Linked Data principles to create a semantic representation of the components that compose CDS services. To evaluate our approach we implemented a set of CDS Linked Services using a Web service definition ontology. The definitions of Web services were linked to the models developed in order to attach unambiguous semantics to the service components. All models were bound to SNOMED-CT and public ontologies (e.g. Dublin Core) in order to count on a lingua franca to explore them. Discovery and analysis of CDS services based on machine interpretable models was performed reasoning over the ontologies built. Linked Services can be used effectively to expose CDS services to the Web of Data by building on current CDS standards. This allows building shared Linked Knowledge Bases to provide machine

  17. Decision support systems

    DEFF Research Database (Denmark)

    Jørgensen, L.N.; Noe, E.; Langvad, A.M.

    2007-01-01

    system Crop Protection Online is widely used by advisors and as a learning tool for students. Although the system has been validated in many field trials over the years and has shown reliable results, the number of end-users among farmers has been relatively low during the last 10 years (approximately...... 1000 farmers). A sociological investigation of farmers' decision-making styles in the area of crop protection has shown that arable farmers can be divided into three major groups: (a) system-orientated farmers, (b) experience-based farmers and (c) advisory-orientated farmers. The information required...... by these three groups to make their decisions varies and therefore different ways of using decision support systems need to be provided. Decision support systems need to be developed in close dialogue and collaboration with user groups....

  18. Depression and Anxiety During Pregnancy: Evaluating the Literature in Support of Clinical Risk-Benefit Decision-Making.

    Science.gov (United States)

    Dalke, Katharine Baratz; Wenzel, Amy; Kim, Deborah R

    2016-06-01

    Depression and anxiety during pregnancy are common, and patients and providers are faced with complex decisions regarding various treatment modalities. A structured discussion of the risks and benefits of options with the patient and her support team is recommended to facilitate the decision-making process. This clinically focused review, with emphasis on the last 3 years of published study data, evaluates the major risk categories of medication treatments, namely pregnancy loss, physical malformations, growth impairment, behavioral teratogenicity, and neonatal toxicity. Nonpharmacological treatment options, including neuromodulation and psychotherapy, are also briefly reviewed. Specific recommendations, drawn from the literature and the authors' clinical experience, are also offered to help guide the clinician in decision-making.

  19. OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records.

    Science.gov (United States)

    Chen, Jonathan H; Podchiyska, Tanya; Altman, Russ B

    2016-03-01

    To answer a "grand challenge" in clinical decision support, the authors produced a recommender system that automatically data-mines inpatient decision support from electronic medical records (EMR), analogous to Netflix or Amazon.com's product recommender. EMR data were extracted from 1 year of hospitalizations (>18K patients with >5.4M structured items including clinical orders, lab results, and diagnosis codes). Association statistics were counted for the ∼1.5K most common items to drive an order recommender. The authors assessed the recommender's ability to predict hospital admission orders and outcomes based on initial encounter data from separate validation patients. Compared to a reference benchmark of using the overall most common orders, the recommender using temporal relationships improves precision at 10 recommendations from 33% to 38% (P < 10(-10)) for hospital admission orders. Relative risk-based association methods improve inverse frequency weighted recall from 4% to 16% (P < 10(-16)). The framework yields a prediction receiver operating characteristic area under curve (c-statistic) of 0.84 for 30 day mortality, 0.84 for 1 week need for ICU life support, 0.80 for 1 week hospital discharge, and 0.68 for 30-day readmission. Recommender results quantitatively improve on reference benchmarks and qualitatively appear clinically reasonable. The method assumes that aggregate decision making converges appropriately, but ongoing evaluation is necessary to discern common behaviors from "correct" ones. Collaborative filtering recommender algorithms generate clinical decision support that is predictive of real practice patterns and clinical outcomes. Incorporating temporal relationships improves accuracy. Different evaluation metrics satisfy different goals (predicting likely events vs. "interesting" suggestions). Published by Oxford University Press on behalf of the American Medical Informatics Association 2015. This work is written by US Government

  20. Evaluation of user interface and workflow design of a bedside nursing clinical decision support system.

    Science.gov (United States)

    Yuan, Michael Juntao; Finley, George Mike; Long, Ju; Mills, Christy; Johnson, Ron Kim

    2013-01-31

    Clinical decision support systems (CDSS) are important tools to improve health care outcomes and reduce preventable medical adverse events. However, the effectiveness and success of CDSS depend on their implementation context and usability in complex health care settings. As a result, usability design and validation, especially in real world clinical settings, are crucial aspects of successful CDSS implementations. Our objective was to develop a novel CDSS to help frontline nurses better manage critical symptom changes in hospitalized patients, hence reducing preventable failure to rescue cases. A robust user interface and implementation strategy that fit into existing workflows was key for the success of the CDSS. Guided by a formal usability evaluation framework, UFuRT (user, function, representation, and task analysis), we developed a high-level specification of the product that captures key usability requirements and is flexible to implement. We interviewed users of the proposed CDSS to identify requirements, listed functions, and operations the system must perform. We then designed visual and workflow representations of the product to perform the operations. The user interface and workflow design were evaluated via heuristic and end user performance evaluation. The heuristic evaluation was done after the first prototype, and its results were incorporated into the product before the end user evaluation was conducted. First, we recruited 4 evaluators with strong domain expertise to study the initial prototype. Heuristic violations were coded and rated for severity. Second, after development of the system, we assembled a panel of nurses, consisting of 3 licensed vocational nurses and 7 registered nurses, to evaluate the user interface and workflow via simulated use cases. We recorded whether each session was successfully completed and its completion time. Each nurse was asked to use the National Aeronautics and Space Administration (NASA) Task Load Index to self

  1. Support and Assessment for Fall Emergency Referrals (SAFER 1: cluster randomised trial of computerised clinical decision support for paramedics.

    Directory of Open Access Journals (Sweden)

    Helen Anne Snooks

    Full Text Available To evaluate effectiveness, safety and cost-effectiveness of Computerised Clinical Decision Support (CCDS for paramedics attending older people who fall.Cluster trial randomised by paramedic; modelling.13 ambulance stations in two UK emergency ambulance services.42 of 409 eligible paramedics, who attended 779 older patients for a reported fall.Intervention paramedics received CCDS on Tablet computers to guide patient care. Control paramedics provided care as usual. One service had already installed electronic data capture.Effectiveness: patients referred to falls service, patient reported quality of life and satisfaction, processes of care.Further emergency contacts or death within one month.Costs and quality of life. We used findings from published Community Falls Prevention Trial to model cost-effectiveness.17 intervention paramedics used CCDS for 54 (12.4% of 436 participants. They referred 42 (9.6% to falls services, compared with 17 (5.0% of 343 participants seen by 19 control paramedics [Odds ratio (OR 2.04, 95% CI 1.12 to 3.72]. No adverse events were related to the intervention. Non-significant differences between groups included: subsequent emergency contacts (34.6% versus 29.1%; OR 1.27, 95% CI 0.93 to 1.72; quality of life (mean SF12 differences: MCS -0.74, 95% CI -2.83 to +1.28; PCS -0.13, 95% CI -1.65 to +1.39 and non-conveyance (42.0% versus 36.7%; OR 1.13, 95% CI 0.84 to 1.52. However ambulance job cycle time was 8.9 minutes longer for intervention patients (95% CI 2.3 to 15.3. Average net cost of implementing CCDS was £208 per patient with existing electronic data capture, and £308 without. Modelling estimated cost per quality-adjusted life-year at £15,000 with existing electronic data capture; and £22,200 without.Intervention paramedics referred twice as many participants to falls services with no difference in safety. CCDS is potentially cost-effective, especially with existing electronic data capture.ISRCTN Register ISRCTN

  2. Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System.

    Science.gov (United States)

    Rajamani, Sripriya; Bieringer, Aaron; Wallerius, Stephanie; Jensen, Daniel; Winden, Tamara; Muscoplat, Miriam Halstead

    2016-01-01

    Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. The study objective was to analyze the CDSi presentation by IIS in Minnesota (Minnesota Immunization Information Connection [MIIC]) through direct access by IIS interface and by access through electronic health records (EHRs) to outline similarities and differences. The immunization data presented were similar across the three systems examined, but with varying ability to integrate data across MIIC and EHR, which impacts immunization data reconciliation. Study findings will lead to better understanding of immunization data display, clinical decision support, and user functionalities with the ultimate goal of promoting IIS CDSi to improve vaccination rates.

  3. Reduced Clostridium difficile Tests and Laboratory-Identified Events With a Computerized Clinical Decision Support Tool and Financial Incentive.

    Science.gov (United States)

    Madden, Gregory R; German Mesner, Ian; Cox, Heather L; Mathers, Amy J; Lyman, Jason A; Sifri, Costi D; Enfield, Kyle B

    2018-06-01

    We hypothesized that a computerized clinical decision support tool for Clostridium difficile testing would reduce unnecessary inpatient tests, resulting in fewer laboratory-identified events. Census-adjusted interrupted time-series analyses demonstrated significant reductions of 41% fewer tests and 31% fewer hospital-onset C. difficile infection laboratory-identified events following this intervention.Infect Control Hosp Epidemiol 2018;39:737-740.

  4. Real-Time Clinical Decision Support System with Data Stream Mining

    Directory of Open Access Journals (Sweden)

    Yang Zhang

    2012-01-01

    Full Text Available This research aims to describe a new design of data stream mining system that can analyze medical data stream and make real-time prediction. The motivation of the research is due to a growing concern of combining software technology and medical functions for the development of software application that can be used in medical field of chronic disease prognosis and diagnosis, children healthcare, diabetes diagnosis, and so forth. Most of the existing software technologies are case-based data mining systems. They only can analyze finite and structured data set and can only work well in their early years and can hardly meet today's medical requirement. In this paper, we describe a clinical-support-system based data stream mining technology; the design has taken into account all the shortcomings of the existing clinical support systems.

  5. Advanced Clinical Decision Support for Transport of the Critically Ill Patient

    Science.gov (United States)

    2013-12-01

    in the sections to follow. Accomplishments according to proposed tasks: Our "team" has included all CHRCO based physician and nurse investigators...ahead of print). Winsor, G., et al., Inadequate hemodynamic management in patients undergoing interfacility transfer for suspected aortic dissection...Fields, and C.d. Forca-Tarefa, Clinical practice parameters for hemodynamic support of pediatric and neonatal patients in septic shock. J Pediatr, 2002

  6. An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription.

    Science.gov (United States)

    Shen, Ying; Yuan, Kaiqi; Chen, Daoyuan; Colloc, Joël; Yang, Min; Li, Yaliang; Lei, Kai

    2018-03-01

    The available antibiotic decision-making systems were developed from a physician's perspective. However, because infectious diseases are common, many patients desire access to knowledge via a search engine. Although the use of antibiotics should, in principle, be subject to a doctor's advice, many patients take them without authorization, and some people cannot easily or rapidly consult a doctor. In such cases, a reliable antibiotic prescription support system is needed. This study describes the construction and optimization of the sensitivity and specificity of a decision support system named IDDAP, which is based on ontologies for infectious disease diagnosis and antibiotic therapy. The ontology for this system was constructed by collecting existing ontologies associated with infectious diseases, syndromes, bacteria and drugs into the ontology's hierarchical conceptual schema. First, IDDAP identifies a potential infectious disease based on a patient's self-described disease state. Then, the system searches for and proposes an appropriate antibiotic therapy specifically adapted to the patient based on factors such as the patient's body temperature, infection sites, symptoms/signs, complications, antibacterial spectrum, contraindications, drug-drug interactions between the proposed therapy and previously prescribed medication, and the route of therapy administration. The constructed domain ontology contains 1,267,004 classes, 7,608,725 axioms, and 1,266,993 members of "SubClassOf" that pertain to infectious diseases, bacteria, syndromes, anti-bacterial drugs and other relevant components. The system includes 507 infectious diseases and their therapy methods in combination with 332 different infection sites, 936 relevant symptoms of the digestive, reproductive, neurological and other systems, 371 types of complications, 838,407 types of bacteria, 341 types of antibiotics, 1504 pairs of reaction rates (antibacterial spectrum) between antibiotics and bacteria, 431

  7. Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care.

    Science.gov (United States)

    Belard, Arnaud; Buchman, Timothy; Forsberg, Jonathan; Potter, Benjamin K; Dente, Christopher J; Kirk, Allan; Elster, Eric

    2017-04-01

    Improving diagnosis and treatment depends on clinical monitoring and computing. Clinical decision support systems (CDSS) have been in existence for over 50 years. While the literature points to positive impacts on quality and patient safety, outcomes, and the avoidance of medical errors, technical and regulatory challenges continue to retard their rate of integration into clinical care processes and thus delay the refinement of diagnoses towards personalized care. We conducted a systematic review of pertinent articles in the MEDLINE, US Department of Health and Human Services, Agency for Health Research and Quality, and US Food and Drug Administration databases, using a Boolean approach to combine terms germane to the discussion (clinical decision support, tools, systems, critical care, trauma, outcome, cost savings, NSQIP, APACHE, SOFA, ICU, and diagnostics). References were selected on the basis of both temporal and thematic relevance, and subsequently aggregated around four distinct themes: the uses of CDSS in the critical and surgical care settings, clinical insertion challenges, utilization leading to cost-savings, and regulatory concerns. Precision diagnosis is the accurate and timely explanation of each patient's health problem and further requires communication of that explanation to patients and surrogate decision-makers. Both accuracy and timeliness are essential to critical care, yet computed decision support systems (CDSS) are scarce. The limitation arises from the technical complexity associated with integrating and filtering large data sets from diverse sources. Provider mistrust and resistance coupled with the absence of clear guidance from regulatory bodies further retard acceptance of CDSS. While challenges to develop and deploy CDSS are substantial, the clinical, quality, and economic impacts warrant the effort, especially in disciplines requiring complex decision-making, such as critical and surgical care. Improving diagnosis in health care

  8. Design and Development of a Sharable Clinical Decision Support System Based on a Semantic Web Service Framework.

    Science.gov (United States)

    Zhang, Yi-Fan; Gou, Ling; Tian, Yu; Li, Tian-Chang; Zhang, Mao; Li, Jing-Song

    2016-05-01

    Clinical decision support (CDS) systems provide clinicians and other health care stakeholders with patient-specific assessments or recommendations to aid in the clinical decision-making process. Despite their demonstrated potential for improving health care quality, the widespread availability of CDS systems has been limited mainly by the difficulty and cost of sharing CDS knowledge among heterogeneous healthcare information systems. The purpose of this study was to design and develop a sharable clinical decision support (S-CDS) system that meets this challenge. The fundamental knowledge base consists of independent and reusable knowledge modules (KMs) to meet core CDS needs, wherein each KM is semantically well defined based on the standard information model, terminologies, and representation formalisms. A semantic web service framework was developed to identify, access, and leverage these KMs across diverse CDS applications and care settings. The S-CDS system has been validated in two distinct client CDS applications. Model-level evaluation results confirmed coherent knowledge representation. Application-level evaluation results reached an overall accuracy of 98.66 % and a completeness of 96.98 %. The evaluation results demonstrated the technical feasibility and application prospect of our approach. Compared with other CDS engineering efforts, our approach facilitates system development and implementation and improves system maintainability, scalability and efficiency, which contribute to the widespread adoption of effective CDS within the healthcare domain.

  9. Clinic-Based Mobile Health Decision Support to Enhance Adult Epilepsy Self-Management: An Intervention Mapping Approach.

    Science.gov (United States)

    Shegog, Ross; Begley, Charles E

    2017-01-01

    Epilepsy is a neurological disorder involving recurrent seizures. It affects approximately 5 million people in the U.S. To optimize their quality of life people with epilepsy are encouraged to engage in self-management (S-M) behaviors. These include managing their treatment (e.g., adhering to anti-seizure medication and clinical visit schedules), managing their seizures (e.g., responding to seizure episodes), managing their safety (e.g., monitoring and avoiding environmental seizure triggers), and managing their co-morbid conditions (e.g., anxiety, depression). The clinic-based Management Information Decision Support Epilepsy Tool (MINDSET) is a decision-support system founded on theory and empirical evidence. It is designed to increase awareness by adult patients (≥18 years) and their health-care provider regarding the patient's epilepsy S-M behaviors, facilitate communication during the clinic visit to prioritize S-M goals and strategies commensurate with the patient's needs, and increase the patient's self-efficacy to achieve those goals. The purpose of this paper is to describe the application of intervention mapping (IM) to develop, implement, and formatively evaluate the clinic-based MINDSET prototype and in developing implementation and evaluation plans. Deliverables comprised a logic model of the problem (IM Step 1); matrices of program objectives (IM Step 2); a program planning document comprising scope, sequence, theory-based methods, and practical strategies (IM Step 3); a functional MINDSET program prototype (IM Step 4); plans for implementation (IM Step 5); and evaluation (IM Step 6). IM provided a logical and systematic approach to developing and evaluating clinic-based decision support toward epilepsy S-M.

  10. Clinic-Based Mobile Health Decision Support to Enhance Adult Epilepsy Self-Management: An Intervention Mapping Approach

    Directory of Open Access Journals (Sweden)

    Ross Shegog

    2017-10-01

    Full Text Available IntroductionEpilepsy is a neurological disorder involving recurrent seizures. It affects approximately 5 million people in the U.S. To optimize their quality of life people with epilepsy are encouraged to engage in self-management (S-M behaviors. These include managing their treatment (e.g., adhering to anti-seizure medication and clinical visit schedules, managing their seizures (e.g., responding to seizure episodes, managing their safety (e.g., monitoring and avoiding environmental seizure triggers, and managing their co-morbid conditions (e.g., anxiety, depression. The clinic-based Management Information Decision Support Epilepsy Tool (MINDSET is a decision-support system founded on theory and empirical evidence. It is designed to increase awareness by adult patients (≥18 years and their health-care provider regarding the patient’s epilepsy S-M behaviors, facilitate communication during the clinic visit to prioritize S-M goals and strategies commensurate with the patient’s needs, and increase the patient’s self-efficacy to achieve those goals.MethodsThe purpose of this paper is to describe the application of intervention mapping (IM to develop, implement, and formatively evaluate the clinic-based MINDSET prototype and in developing implementation and evaluation plans. Deliverables comprised a logic model of the problem (IM Step 1; matrices of program objectives (IM Step 2; a program planning document comprising scope, sequence, theory-based methods, and practical strategies (IM Step 3; a functional MINDSET program prototype (IM Step 4; plans for implementation (IM Step 5; and evaluation (IM Step 6. IM provided a logical and systematic approach to developing and evaluating clinic-based decision support toward epilepsy S-M.

  11. Modeling the Construct of an Expert Evidence-Adaptive Knowledge Base for a Pressure Injury Clinical Decision Support System

    Directory of Open Access Journals (Sweden)

    Peck Chui Betty Khong

    2017-07-01

    Full Text Available The selection of appropriate wound products for the treatment of pressure injuries is paramount in promoting wound healing. However, nurses find it difficult to decide on the most optimal wound product(s due to limited live experiences in managing pressure injuries resulting from successfully implemented pressure injury prevention programs. The challenges of effective decision-making in wound treatments by nurses at the point of care are compounded by the yearly release of wide arrays of newly researched wound products into the consumer market. A clinical decision support system for pressure injury (PI-CDSS was built to facilitate effective decision-making and selection of optimal wound treatments. This paper describes the development of PI-CDSS with an expert knowledge base using an interactive development environment, Blaze Advisor. A conceptual framework using decision-making and decision theory, knowledge representation, and process modelling guided the construct of the PI-CDSS. This expert system has incorporated the practical and relevant decision knowledge of wound experts in assessment and wound treatments in its algorithm. The construct of the PI-CDSS is adaptive, with scalable capabilities for expansion to include other CDSSs and interoperability to interface with other existing clinical and administrative systems. The algorithm was formatively evaluated and tested for usability. The treatment modalities generated after using patient-specific assessment data were found to be consistent with the treatment plan(s proposed by the wound experts. The overall agreement exceeded 90% between the wound experts and the generated treatment modalities for the choice of wound products, instructions, and alerts. The PI-CDSS serves as a just-in-time wound treatment protocol with suggested clinical actions for nurses, based on the best evidence available.

  12. Decisions at hand: a decision support system on handhelds.

    Science.gov (United States)

    Zupan, B; Porenta, A; Vidmar, G; Aoki, N; Bratko, I; Beck, J R

    2001-01-01

    One of the applications of clinical information systems is decision support. Although the advantages of utilizing such aids have never been theoretically disputed, they have been rarely used in practice. The factor that probably often limits the utility of clinical decision support systems is the need for computing power at the very site of decision making--at the place where the patient is interviewed, in discussion rooms, etc. The paper reports on a possible solution to this problem. A decision-support shell LogReg is presented, which runs on a handheld computer. A general schema for handheld-based decision support is also proposed, where decision models are developed on personal computers/workstations, encoded in XML and then transferred to handhelds, where the models are used within a decision support shell. A use case where LogReg has been applied to clinical outcome prediction in crush injury is presented.

  13. Developing Mobile Clinical Decision Support for Nursing Home Staff Assessment of Urinary Tract Infection using Goal-Directed Design.

    Science.gov (United States)

    Jones, Wallace; Drake, Cynthia; Mack, David; Reeder, Blaine; Trautner, Barbara; Wald, Heidi

    2017-06-20

    Unique characteristics of nursing homes (NHs) contribute to high rates of inappropriate antibiotic use for asymptomatic bacteriuria (ASB), a benign condition. A mobile clinical decision support system (CDSS) may support NH staff in differentiating urinary tract infections (UTI) from ASB and reducing antibiotic days. We used Goal-Directed Design to: 1) Characterize information needs for UTI identification and management in NHs; 2) Develop UTI Decide, a mobile CDSS prototype informed by personas and scenarios of use constructed from Aim 1 findings; 3) Evaluate the UTI Decide prototype with NH staff. Focus groups were conducted with providers and nurses in NHs in Denver, Colorado (n= 24). Qualitative descriptive analysis was applied to focus group transcripts to identify information needs and themes related to mobile clinical decision support for UTI identification and management. Personas representing typical end users were developed; typical clinical context scenarios were constructed using information needs as goals. Usability testing was performed using cognitive walk-throughs and a think-aloud protocol. Four information needs were identified including guidance regarding resident assessment; communication with providers; care planning; and urine culture interpretation. Design of a web-based application incorporating a published decision support algorithm for evidence-based UTI diagnoses proceeded with a focus on nursing information needs during resident assessment and communication with providers. Certified nursing assistant (CNA) and registered nurse (RN) personas were constructed in 4 context scenarios with associated key path scenarios. After field testing, a high fidelity prototype of UTI Decide was completed and evaluated by potential end users. Design recommendations and content recommendations were elicited. Goal-Directed Design informed the development of a mobile CDSS supporting participant-identified information needs for UTI assessment and communication

  14. Implementation of clinical decision support in young children with acute gastroenteritis: a randomized controlled trial at the emergency department.

    Science.gov (United States)

    Geurts, Dorien; de Vos-Kerkhof, Evelien; Polinder, Suzanne; Steyerberg, Ewout; van der Lei, Johan; Moll, Henriëtte; Oostenbrink, Rianne

    2017-02-01

    Acute gastroenteritis (AGE) is one of the most frequent reasons for young children to visit emergency departments (EDs). We aimed to evaluate (1) feasibility of a nurse-guided clinical decision support system for rehydration treatment in children with AGE and (2) the impact on diagnostics, treatment, and costs compared with usual care by attending physician. A randomized controlled trial was performed in 222 children, aged 1 month to 5 years at the ED of the Erasmus MC-Sophia Children's hospital in The Netherlands ( 2010-2012). Outcome included (1) feasibility, measured by compliance of the nurses, and (2) length of stay (LOS) at the ED, the number of diagnostic tests, treatment, follow-up, and costs. Due to failure of post-ED weight measurement, we could not evaluate weight difference as measure for dehydration. Patient characteristics were comparable between the intervention (N = 113) and the usual care group (N = 109). Implementation of the clinical decision support system proved a high compliance rate. The standardized use of oral ORS (oral rehydration solution) significantly increased from 52 to 65%(RR2.2, 95%CI 1.09-4.31 p children with AGE showed high compliance and increase standardized use of ORS, without differences in other outcome measures. What is Known: • Acute gastroenteritis is one of the most frequently encountered problems in pediatric emergency departments. • Guidelines advocate standardized oral treatment in children with mild to moderate dehydration, but appear to be applied infrequently in clinical practice. What is New: • Implementation of a nurse-guided clinical decision support system on treatment of AGE in young children showed good feasibility, resulting in a more standardized ORS use in children with mild to moderate dehydration, compared to usual care. • Given the challenges to perform research in emergency care setting, the ED should be experienced and adequately equipped, especially during peak times.

  15. Clinical Decision Support Model to Predict Occlusal Force in Bruxism Patients.

    Science.gov (United States)

    Thanathornwong, Bhornsawan; Suebnukarn, Siriwan

    2017-10-01

    The aim of this study was to develop a decision support model for the prediction of occlusal force from the size and color of articulating paper markings in bruxism patients. We used the information from the datasets of 30 bruxism patients in which digital measurements of the size and color of articulating paper markings (12-µm Hanel; Coltene/Whaledent GmbH, Langenau, Germany) on canine protected hard stabilization splints were measured in pixels (P) and in red (R), green (G), and blue (B) values using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA). The occlusal force (F) was measured using T-Scan III (Tekscan Inc., South Boston, MA, USA). The multiple regression equation was applied to predict F from the P and RGB. Model evaluation was performed using the datasets from 10 new patients. The patient's occlusal force measured by T-Scan III was used as a 'gold standard' to compare with the occlusal force predicted by the multiple regression model. The results demonstrate that the correlation between the occlusal force and the pixels and RGB of the articulating paper markings was positive (F = 1.62×P + 0.07×R -0.08×G + 0.08×B + 4.74; R 2 = 0.34). There was a high degree of agreement between the occlusal force of the patient measured using T-Scan III and the occlusal force predicted by the model (kappa value = 0.82). The results obtained demonstrate that the multiple regression model can predict the occlusal force using the digital values for the size and color of the articulating paper markings in bruxism patients.

  16. Clinical Decision Support Model to Predict Occlusal Force in Bruxism Patients

    Science.gov (United States)

    Thanathornwong, Bhornsawan

    2017-01-01

    Objectives The aim of this study was to develop a decision support model for the prediction of occlusal force from the size and color of articulating paper markings in bruxism patients. Methods We used the information from the datasets of 30 bruxism patients in which digital measurements of the size and color of articulating paper markings (12-µm Hanel; Coltene/Whaledent GmbH, Langenau, Germany) on canine protected hard stabilization splints were measured in pixels (P) and in red (R), green (G), and blue (B) values using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA). The occlusal force (F) was measured using T-Scan III (Tekscan Inc., South Boston, MA, USA). The multiple regression equation was applied to predict F from the P and RGB. Model evaluation was performed using the datasets from 10 new patients. The patient's occlusal force measured by T-Scan III was used as a ‘gold standard’ to compare with the occlusal force predicted by the multiple regression model. Results The results demonstrate that the correlation between the occlusal force and the pixels and RGB of the articulating paper markings was positive (F = 1.62×P + 0.07×R –0.08×G + 0.08×B + 4.74; R2 = 0.34). There was a high degree of agreement between the occlusal force of the patient measured using T-Scan III and the occlusal force predicted by the model (kappa value = 0.82). Conclusions The results obtained demonstrate that the multiple regression model can predict the occlusal force using the digital values for the size and color of the articulating paper markings in bruxism patients. PMID:29181234

  17. Tailoring exercise interventions to comorbidities and treatment-induced adverse effects in patients with early stage breast cancer undergoing chemotherapy: a framework to support clinical decisions

    NARCIS (Netherlands)

    van der Leeden, Marike; Huijsmans, Rosalie J.; Geleijn, Edwin; de Rooij, Mariëtte; Konings, Inge R.; Buffart, Laurien M.; Dekker, Joost; Stuiver, Martijn M.

    2018-01-01

    Delivery of exercise interventions to patients with early-stage breast cancer undergoing chemotherapy requires complex clinical decisions. The purpose of this study was to develop a framework to support clinical decisions for tailoring exercise interventions to common comorbidities and cancer

  18. A clinical decision support system for integrating tuberculosis and HIV care in Kenya: a human-centered design approach.

    Science.gov (United States)

    Catalani, Caricia; Green, Eric; Owiti, Philip; Keny, Aggrey; Diero, Lameck; Yeung, Ada; Israelski, Dennis; Biondich, Paul

    2014-01-01

    With the aim of integrating HIV and tuberculosis care in rural Kenya, a team of researchers, clinicians, and technologists used the human-centered design approach to facilitate design, development, and deployment processes of new patient-specific TB clinical decision support system for medical providers. In Kenya, approximately 1.6 million people are living with HIV and have a 20-times higher risk of dying of tuberculosis. Although tuberculosis prevention and treatment medication is widely available, proven to save lives, and prioritized by the World Health Organization, ensuring that it reaches the most vulnerable communities remains challenging. Human-centered design, used in the fields of industrial design and information technology for decades, is an approach to improving the effectiveness and impact of innovations that has been scarcely used in the health field. Using this approach, our team followed a 3-step process, involving mixed methods assessment to (1) understand the situation through the collection and analysis of site observation sessions and key informant interviews; (2) develop a new clinical decision support system through iterative prototyping, end-user engagement, and usability testing; and, (3) implement and evaluate the system across 24 clinics in rural West Kenya. Through the application of this approach, we found that human-centered design facilitated the process of digital innovation in a complex and resource-constrained context.

  19. A clinical decision support system for integrating tuberculosis and HIV care in Kenya: a human-centered design approach.

    Directory of Open Access Journals (Sweden)

    Caricia Catalani

    Full Text Available With the aim of integrating HIV and tuberculosis care in rural Kenya, a team of researchers, clinicians, and technologists used the human-centered design approach to facilitate design, development, and deployment processes of new patient-specific TB clinical decision support system for medical providers. In Kenya, approximately 1.6 million people are living with HIV and have a 20-times higher risk of dying of tuberculosis. Although tuberculosis prevention and treatment medication is widely available, proven to save lives, and prioritized by the World Health Organization, ensuring that it reaches the most vulnerable communities remains challenging. Human-centered design, used in the fields of industrial design and information technology for decades, is an approach to improving the effectiveness and impact of innovations that has been scarcely used in the health field. Using this approach, our team followed a 3-step process, involving mixed methods assessment to (1 understand the situation through the collection and analysis of site observation sessions and key informant interviews; (2 develop a new clinical decision support system through iterative prototyping, end-user engagement, and usability testing; and, (3 implement and evaluate the system across 24 clinics in rural West Kenya. Through the application of this approach, we found that human-centered design facilitated the process of digital innovation in a complex and resource-constrained context.

  20. Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support.

    Science.gov (United States)

    Mohammadhassanzadeh, Hossein; Van Woensel, William; Abidi, Samina Raza; Abidi, Syed Sibte Raza

    2017-01-01

    Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians' experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning , which generalizes the commonalities among the data to induce new rules, and analogical reasoning , which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries. We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15

  1. Effectiveness of computerized clinical decision support systems for asthma and chronic obstructive pulmonary disease in primary care: a systematic review.

    Science.gov (United States)

    Fathima, Mariam; Peiris, David; Naik-Panvelkar, Pradnya; Saini, Bandana; Armour, Carol Lyn

    2014-12-02

    The use of computerized clinical decision support systems may improve the diagnosis and ongoing management of chronic diseases, which requires recurrent visits to multiple health professionals, disease and medication monitoring and modification of patient behavior. The aim of this review was to systematically review randomized controlled trials evaluating the effectiveness of computerized clinical decision systems (CCDSS) in the care of people with asthma and COPD. Randomized controlled trials published between 2003 and 2013 were searched using multiple electronic databases Medline, EMBASE, CINAHL, IPA, Informit, PsychINFO, Compendex, and Cochrane Clinical Controlled Trials Register databases. To be included, RCTs had to evaluate the role of the CCDSSs for asthma and/or COPD in primary care. Nineteen studies representing 16 RCTs met our inclusion criteria. The majority of the trials were conducted in patients with asthma. Study quality was generally high. Meta-analysis was not conducted because of methodological and clinical heterogeneity. The use of CCDSS improved asthma and COPD care in 14 of the 19 studies reviewed (74%). Nine of the nineteen studies showed statistically significant (p < 0.05) improvement in the primary outcomes measured. The majority of the studies evaluated health care process measures as their primary outcomes (10/19). Evidence supports the effectiveness of CCDSS in the care of people with asthma. However there is very little information of its use in COPD care. Although there is considerable improvement in the health care process measures and clinical outcomes through the use of CCDSSs, its effects on user workload and efficiency, safety, costs of care, provider and patient satisfaction remain understudied.

  2. The Need for Clinical Decision Support Integrated with the Electronic Health Record for the Clinical Application of Whole Genome Sequencing Information

    Directory of Open Access Journals (Sweden)

    Brandon M. Welch

    2013-12-01

    Full Text Available Whole genome sequencing (WGS is rapidly approaching widespread clinical application. Technology advancements over the past decade, since the first human genome was decoded, have made it feasible to use WGS for clinical care. Future advancements will likely drive down the price to the point wherein WGS is routinely available for care. However, were this to happen today, most of the genetic information available to guide clinical care would go unused due to the complexity of genetics, limited physician proficiency in genetics, and lack of genetics professionals in the clinical workforce. Furthermore, these limitations are unlikely to change in the future. As such, the use of clinical decision support (CDS to guide genome-guided clinical decision-making is imperative. In this manuscript, we describe the barriers to widespread clinical application of WGS information, describe how CDS can be an important tool for overcoming these barriers, and provide clinical examples of how genome-enabled CDS can be used in the clinical setting.

  3. A Clinical Decision Support System for Chronic Pain Management in Primary Care: Usability testing and its relevance.

    Science.gov (United States)

    Nair, Kalpana Maria; Malaeekeh, Raheleh; Schabort, Inge; Taenzer, Paul; Radhakrishnan, Arun; Guenter, Dale

    2015-08-13

    Clinical decision support systems (CDSSs) that are integrated into electronic medical records may be useful for encouraging practice change compliant with clinical practice guidelines. To engage end users to inform early phase CDSS development through a process of usability testing. A sequential exploratory mixed method approach was used. Interprofessional clinician participants (seven in iteration 1 and six in iteration 2) were asked to 'think aloud' while performing various tasks on the CDSS and then complete the System Usability Scale (SUS). Changes were made to the CDSS after each iteration.Results Barriers and facilitators were identified: systemic; user interface (most numerous barriers); content (most numerous facilitators) and technical. The mean SUS score was 81.1 (SD = 12.02) in iteration 1 and 70.40 (SD = 6.78) in iteration 2 (p > 0.05). Qualitative data from usability testing were valuable in the CDSS development process. SUS scores were of limited value at this development stage.

  4. Description and status update on GELLO: a proposed standardized object-oriented expression language for clinical decision support.

    Science.gov (United States)

    Sordo, Margarita; Boxwala, Aziz A; Ogunyemi, Omolola; Greenes, Robert A

    2004-01-01

    A major obstacle to sharing computable clinical knowledge is the lack of a common language for specifying expressions and criteria. Such a language could be used to specify decision criteria, formulae, and constraints on data and action. Al-though the Arden Syntax addresses this problem for clinical rules, its generalization to HL7's object-oriented data model is limited. The GELLO Expression language is an object-oriented language used for expressing logical conditions and computations in the GLIF3 (GuideLine Interchange Format, v. 3) guideline modeling language. It has been further developed under the auspices of the HL7 Clinical Decision Support Technical Committee, as a proposed HL7 standard., GELLO is based on the Object Constraint Language (OCL), because it is vendor-independent, object-oriented, and side-effect-free. GELLO expects an object-oriented data model. Although choice of model is arbitrary, standardization is facilitated by ensuring that the data model is compatible with the HL7 Reference Information Model (RIM).

  5. An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients.

    Science.gov (United States)

    Barbieri, Carlo; Molina, Manuel; Ponce, Pedro; Tothova, Monika; Cattinelli, Isabella; Ion Titapiccolo, Jasmine; Mari, Flavio; Amato, Claudia; Leipold, Frank; Wehmeyer, Wolfgang; Stuard, Stefano; Stopper, Andrea; Canaud, Bernard

    2016-08-01

    Managing anemia in hemodialysis patients can be challenging because of competing therapeutic targets and individual variability. Because therapy recommendations provided by a decision support system can benefit both patients and doctors, we evaluated the impact of an artificial intelligence decision support system, the Anemia Control Model (ACM), on anemia outcomes. Based on patient profiles, the ACM was built to recommend suitable erythropoietic-stimulating agent doses. Our retrospective study consisted of a 12-month control phase (standard anemia care), followed by a 12-month observation phase (ACM-guided care) encompassing 752 patients undergoing hemodialysis therapy in 3 NephroCare clinics located in separate countries. The percentage of hemoglobin values on target, the median darbepoetin dose, and individual hemoglobin fluctuation (estimated from the intrapatient hemoglobin standard deviation) were deemed primary outcomes. In the observation phase, median darbepoetin consumption significantly decreased from 0.63 to 0.46 μg/kg/month, whereas on-target hemoglobin values significantly increased from 70.6% to 76.6%, reaching 83.2% when the ACM suggestions were implemented. Moreover, ACM introduction led to a significant decrease in hemoglobin fluctuation (intrapatient standard deviation decreased from 0.95 g/dl to 0.83 g/dl). Thus, ACM support helped improve anemia outcomes of hemodialysis patients, minimizing erythropoietic-stimulating agent use with the potential to reduce the cost of treatment. Copyright © 2016 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

  6. The process of development of a prioritization tool for a clinical decision support build within a computerized provider order entry system: Experiences from St Luke's Health System.

    Science.gov (United States)

    Wolf, Matthew; Miller, Suzanne; DeJong, Doug; House, John A; Dirks, Carl; Beasley, Brent

    2016-09-01

    To establish a process for the development of a prioritization tool for a clinical decision support build within a computerized provider order entry system and concurrently to prioritize alerts for Saint Luke's Health System. The process of prioritizing clinical decision support alerts included (a) consensus sessions to establish a prioritization process and identify clinical decision support alerts through a modified Delphi process and (b) a clinical decision support survey to validate the results. All members of our health system's physician quality organization, Saint Luke's Care as well as clinicians, administrators, and pharmacy staff throughout Saint Luke's Health System, were invited to participate in this confidential survey. The consensus sessions yielded a prioritization process through alert contextualization and associated Likert-type scales. Utilizing this process, the clinical decision support survey polled the opinions of 850 clinicians with a 64.7 percent response rate. Three of the top rated alerts were approved for the pre-implementation build at Saint Luke's Health System: Acute Myocardial Infarction Core Measure Sets, Deep Vein Thrombosis Prophylaxis within 4 h, and Criteria for Sepsis. This study establishes a process for developing a prioritization tool for a clinical decision support build within a computerized provider order entry system that may be applicable to similar institutions. © The Author(s) 2015.

  7. Patient factors that influence clinicians' decision making in self-management support : A clinical vignette study

    NARCIS (Netherlands)

    Bos-Touwen, Irene D.; Trappenburg, Jaap C A; Van Der Wulp, Ineke; Schuurmans, Marieke J.; De Wit, Niek J.

    2017-01-01

    BACKGROUND AND AIM: Self-management support is an integral part of current chronic care guidelines. The success of self-management interventions varies between individual patients, suggesting a need for tailored self-management support. Understanding the role of patient factors in the current

  8. A web-based clinical decision tool to support treatment decision-making in psychiatry: a pilot focus group study with clinicians, patients and carers.

    Science.gov (United States)

    Henshall, Catherine; Marzano, Lisa; Smith, Katharine; Attenburrow, Mary-Jane; Puntis, Stephen; Zlodre, Jakov; Kelly, Kathleen; Broome, Matthew R; Shaw, Susan; Barrera, Alvaro; Molodynski, Andrew; Reid, Alastair; Geddes, John R; Cipriani, Andrea

    2017-07-21

    Treatment decision tools have been developed in many fields of medicine, including psychiatry, however benefits for patients have not been sustained once the support is withdrawn. We have developed a web-based computerised clinical decision support tool (CDST), which can provide patients and clinicians with continuous, up-to-date, personalised information about the efficacy and tolerability of competing interventions. To test the feasibility and acceptability of the CDST we conducted a focus group study, aimed to explore the views of clinicians, patients and carers. The CDST was developed in Oxford. To tailor treatments at an individual level, the CDST combines the best available evidence from the scientific literature with patient preferences and values, and with patient medical profile to generate personalised clinical recommendations. We conducted three focus groups comprising of three different participant types: consultant psychiatrists, participants with a mental health diagnosis and/or experience of caring for someone with a mental health diagnosis, and primary care practitioners and nurses. Each 1-h focus group started with a short visual demonstration of the CDST. To standardise the discussion during the focus groups, we used the same topic guide that covered themes relating to the acceptability and usability of the CDST. Focus groups were recorded and any identifying participant details were anonymised. Data were analysed thematically and managed using the Framework method and the constant comparative method. The focus groups took place in Oxford between October 2016 and January 2017. Overall 31 participants attended (12 consultants, 11 primary care practitioners and 8 patients or carers). The main themes that emerged related to CDST applications in clinical practice, communication, conflicting priorities, record keeping and data management. CDST was considered a useful clinical decision support, with recognised value in promoting clinician

  9. Adapting Nielsen's Design Heuristics to Dual Processing for Clinical Decision Support.

    Science.gov (United States)

    Taft, Teresa; Staes, Catherine; Slager, Stacey; Weir, Charlene

    2016-01-01

    The study objective was to improve the applicability of Nielson's standard design heuristics for evaluating electronic health record (EHR) alerts and linked ordering support by integrating them with Dual Process theory. Through initial heuristic evaluation and a user study of 7 physicians, usability problems were identified. Through independent mapping of specific usability criteria to support for each of the Dual Cognitive processes (S1 and S2) and deliberation, agreement was reached on mapping criteria. Finally, usability errors from the heuristic and user study were mapped to S1 and S2. Adding a dual process perspective to specific heuristic analysis increases the applicability and relevance of computerized health information design evaluations. This mapping enables designers to measure that their systems are tailored to support attention allocation. System 1 will be supported by improving pattern recognition and saliency, and system 2 through efficiency and control of information access.

  10. Adapting Nielsen’s Design Heuristics to Dual Processing for Clinical Decision Support

    Science.gov (United States)

    Taft, Teresa; Staes, Catherine; Slager, Stacey; Weir, Charlene

    2016-01-01

    The study objective was to improve the applicability of Nielson’s standard design heuristics for evaluating electronic health record (EHR) alerts and linked ordering support by integrating them with Dual Process theory. Through initial heuristic evaluation and a user study of 7 physicians, usability problems were identified. Through independent mapping of specific usability criteria to support for each of the Dual Cognitive processes (S1 and S2) and deliberation, agreement was reached on mapping criteria. Finally, usability errors from the heuristic and user study were mapped to S1 and S2. Adding a dual process perspective to specific heuristic analysis increases the applicability and relevance of computerized health information design evaluations. This mapping enables designers to measure that their systems are tailored to support attention allocation. System 1 will be supported by improving pattern recognition and saliency, and system 2 through efficiency and control of information access. PMID:28269915

  11. Key principles for a national clinical decision support knowledge sharing framework: synthesis of insights from leading subject matter experts.

    Science.gov (United States)

    Kawamoto, Kensaku; Hongsermeier, Tonya; Wright, Adam; Lewis, Janet; Bell, Douglas S; Middleton, Blackford

    2013-01-01

    To identify key principles for establishing a national clinical decision support (CDS) knowledge sharing framework. As part of an initiative by the US Office of the National Coordinator for Health IT (ONC) to establish a framework for national CDS knowledge sharing, key stakeholders were identified. Stakeholders' viewpoints were obtained through surveys and in-depth interviews, and findings and relevant insights were summarized. Based on these insights, key principles were formulated for establishing a national CDS knowledge sharing framework. Nineteen key stakeholders were recruited, including six executives from electronic health record system vendors, seven executives from knowledge content producers, three executives from healthcare provider organizations, and three additional experts in clinical informatics. Based on these stakeholders' insights, five key principles were identified for effectively sharing CDS knowledge nationally. These principles are (1) prioritize and support the creation and maintenance of a national CDS knowledge sharing framework; (2) facilitate the development of high-value content and tooling, preferably in an open-source manner; (3) accelerate the development or licensing of required, pragmatic standards; (4) acknowledge and address medicolegal liability concerns; and (5) establish a self-sustaining business model. Based on the principles identified, a roadmap for national CDS knowledge sharing was developed through the ONC's Advancing CDS initiative. The study findings may serve as a useful guide for ongoing activities by the ONC and others to establish a national framework for sharing CDS knowledge and improving clinical care.

  12. [Implementation of ontology-based clinical decision support system for management of interactions between antihypertensive drugs and diet].

    Science.gov (United States)

    Park, Jeong Eun; Kim, Hwa Sun; Chang, Min Jung; Hong, Hae Sook

    2014-06-01

    The influence of dietary composition on blood pressure is an important subject in healthcare. Interactions between antihypertensive drugs and diet (IBADD) is the most important factor in the management of hypertension. It is therefore essential to support healthcare providers' decision making role in active and continuous interaction control in hypertension management. The aim of this study was to implement an ontology-based clinical decision support system (CDSS) for IBADD management (IBADDM). We considered the concepts of antihypertensive drugs and foods, and focused on the interchangeability between the database and the CDSS when providing tailored information. An ontology-based CDSS for IBADDM was implemented in eight phases: (1) determining the domain and scope of ontology, (2) reviewing existing ontology, (3) extracting and defining the concepts, (4) assigning relationships between concepts, (5) creating a conceptual map with CmapTools, (6) selecting upper ontology, (7) formally representing the ontology with Protégé (ver.4.3), (8) implementing an ontology-based CDSS as a JAVA prototype application. We extracted 5,926 concepts, 15 properties, and formally represented them using Protégé. An ontology-based CDSS for IBADDM was implemented and the evaluation score was 4.60 out of 5. We endeavored to map functions of a CDSS and implement an ontology-based CDSS for IBADDM.

  13. Development of a prototype clinical decision support tool for osteoporosis disease management: a qualitative study of focus groups

    Directory of Open Access Journals (Sweden)

    Newton David

    2010-07-01

    Full Text Available Abstract Background Osteoporosis affects over 200 million people worldwide, and represents a significant cost burden. Although guidelines are available for best practice in osteoporosis, evidence indicates that patients are not receiving appropriate diagnostic testing or treatment according to guidelines. The use of clinical decision support systems (CDSSs may be one solution because they can facilitate knowledge translation by providing high-quality evidence at the point of care. Findings from a systematic review of osteoporosis interventions and consultation with clinical and human factors engineering experts were used to develop a conceptual model of an osteoporosis tool. We conducted a qualitative study of focus groups to better understand physicians' perceptions of CDSSs and to transform the conceptual osteoporosis tool into a functional prototype that can support clinical decision making in osteoporosis disease management at the point of care. Methods The conceptual design of the osteoporosis tool was tested in 4 progressive focus groups with family physicians and general internists. An iterative strategy was used to qualitatively explore the experiences of physicians with CDSSs; and to find out what features, functions, and evidence should be included in a working prototype. Focus groups were conducted using a semi-structured interview guide using an iterative process where results of the first focus group informed changes to the questions for subsequent focus groups and to the conceptual tool design. Transcripts were transcribed verbatim and analyzed using grounded theory methodology. Results Of the 3 broad categories of themes that were identified, major barriers related to the accuracy and feasibility of extracting bone mineral density test results and medications from the risk assessment questionnaire; using an electronic input device such as a Tablet PC in the waiting room; and the importance of including well-balanced information in

  14. Implementing clinical decision support for primary care professionals – the process

    DEFF Research Database (Denmark)

    Kortteisto, Tiina; Komulainen, Jorma; Kunnamo, Ilkka

    2012-01-01

    implementation of eCDS requires time and repeated supportive input. Primary care professionals need time and training for adapting eCDS in their daily routine. In addition, the eCDS content should be tailored to fulfil different professionals’ information needs in primary care practice....

  15. An RDF/OWL knowledge base for query answering and decision support in clinical pharmacogenetics.

    Science.gov (United States)

    Samwald, Matthias; Freimuth, Robert; Luciano, Joanne S; Lin, Simon; Powers, Robert L; Marshall, M Scott; Adlassnig, Klaus-Peter; Dumontier, Michel; Boyce, Richard D

    2013-01-01

    Genetic testing for personalizing pharmacotherapy is bound to become an important part of clinical routine. To address associated issues with data management and quality, we are creating a semantic knowledge base for clinical pharmacogenetics. The knowledge base is made up of three components: an expressive ontology formalized in the Web Ontology Language (OWL 2 DL), a Resource Description Framework (RDF) model for capturing detailed results of manual annotation of pharmacogenomic information in drug product labels, and an RDF conversion of relevant biomedical datasets. Our work goes beyond the state of the art in that it makes both automated reasoning as well as query answering as simple as possible, and the reasoning capabilities go beyond the capabilities of previously described ontologies.

  16. A Novel Clinical Decision Support System Using Improved Adaptive Genetic Algorithm for the Assessment of Fetal Well-Being

    Directory of Open Access Journals (Sweden)

    Sindhu Ravindran

    2015-01-01

    Full Text Available A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG dataset through an Improved Adaptive Genetic Algorithm (IAGA and Extreme Learning Machine (ELM. IAGA employs a new scaling technique (called sigma scaling to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.

  17. SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support

    Energy Technology Data Exchange (ETDEWEB)

    Thwaites, D [University of Sydney, Camperdown, Sydney (Australia); Holloway, L [Ingham Institute, Sydney, NSW (Australia); Bailey, M; Carolan, M; Miller, A [Illawarra Cancer Care Centre, Wollongong, NSW (Australia); Barakat, S; Field, M [University of Sydney, Sydney, NSW (Australia); Delaney, G; Vinod, S [Liverpool Hospital, Liverpool, NSW (Australia); Dekker, A [Maastro Clinic, Maastricht (Netherlands); Lustberg, T; Soest, J van; Walsh, S [MAASTRO Clinic, Maastricht (Netherlands)

    2015-06-15

    Purpose: Large amounts of routine radiotherapy (RT) data are available, which can potentially add clinical evidence to support better decisions. A developing collaborative Australian network, with a leading European partner, aims to validate, implement and extend European predictive models (PMs) for Australian practice and assess their impact on future patient decisions. Wider objectives include: developing multi-institutional rapid learning, using distributed learning approaches; and assessing and incorporating radiomics information into PMs. Methods: Two initial standalone pilots were conducted; one on NSCLC, the other on larynx, patient datasets in two different centres. Open-source rapid learning systems were installed, for data extraction and mining to collect relevant clinical parameters from the centres’ databases. The European DSSs were learned (“training cohort”) and validated against local data sets (“clinical cohort”). Further NSCLC studies are underway in three more centres to pilot a wider distributed learning network. Initial radiomics work is underway. Results: For the NSCLC pilot, 159/419 patient datasets were identified meeting the PM criteria, and hence eligible for inclusion in the curative clinical cohort (for the larynx pilot, 109/125). Some missing data were imputed using Bayesian methods. For both, the European PMs successfully predicted prognosis groups, but with some differences in practice reflected. For example, the PM-predicted good prognosis NSCLC group was differentiated from a combined medium/poor prognosis group (2YOS 69% vs. 27%, p<0.001). Stage was less discriminatory in identifying prognostic groups. In the good prognosis group two-year overall survival was 65% in curatively and 18% in palliatively treated patients. Conclusion: The technical infrastructure and basic European PMs support prognosis prediction for these Australian patient groups, showing promise for supporting future personalized treatment decisions

  18. SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support

    International Nuclear Information System (INIS)

    Thwaites, D; Holloway, L; Bailey, M; Carolan, M; Miller, A; Barakat, S; Field, M; Delaney, G; Vinod, S; Dekker, A; Lustberg, T; Soest, J van; Walsh, S

    2015-01-01

    Purpose: Large amounts of routine radiotherapy (RT) data are available, which can potentially add clinical evidence to support better decisions. A developing collaborative Australian network, with a leading European partner, aims to validate, implement and extend European predictive models (PMs) for Australian practice and assess their impact on future patient decisions. Wider objectives include: developing multi-institutional rapid learning, using distributed learning approaches; and assessing and incorporating radiomics information into PMs. Methods: Two initial standalone pilots were conducted; one on NSCLC, the other on larynx, patient datasets in two different centres. Open-source rapid learning systems were installed, for data extraction and mining to collect relevant clinical parameters from the centres’ databases. The European DSSs were learned (“training cohort”) and validated against local data sets (“clinical cohort”). Further NSCLC studies are underway in three more centres to pilot a wider distributed learning network. Initial radiomics work is underway. Results: For the NSCLC pilot, 159/419 patient datasets were identified meeting the PM criteria, and hence eligible for inclusion in the curative clinical cohort (for the larynx pilot, 109/125). Some missing data were imputed using Bayesian methods. For both, the European PMs successfully predicted prognosis groups, but with some differences in practice reflected. For example, the PM-predicted good prognosis NSCLC group was differentiated from a combined medium/poor prognosis group (2YOS 69% vs. 27%, p<0.001). Stage was less discriminatory in identifying prognostic groups. In the good prognosis group two-year overall survival was 65% in curatively and 18% in palliatively treated patients. Conclusion: The technical infrastructure and basic European PMs support prognosis prediction for these Australian patient groups, showing promise for supporting future personalized treatment decisions

  19. Impact of a computerized provider radiography order entry system without clinical decision support on emergency department medical imaging requests.

    Science.gov (United States)

    Claret, Pierre-Géraud; Bobbia, Xavier; Macri, Francesco; Stowell, Andrew; Motté, Antony; Landais, Paul; Beregi, Jean-Paul; de La Coussaye, Jean-Emmanuel

    2016-06-01

    The adoption of computerized physician order entry is an important cornerstone of using health information technology (HIT) in health care. The transition from paper to computer forms presents a change in physicians' practices. The main objective of this study was to investigate the impact of implementing a computer-based order entry (CPOE) system without clinical decision support on the number of radiographs ordered for patients admitted in the emergency department. This single-center pre-/post-intervention study was conducted in January, 2013 (before CPOE period) and January, 2014 (after CPOE period) at the emergency department at Nîmes University Hospital. All patients admitted in the emergency department who had undergone medical imaging were included in the study. Emergency department admissions have increased since the implementation of CPOE (5388 in the period before CPOE implementation vs. 5808 patients after CPOE implementation, p=.008). In the period before CPOE implementation, 2345 patients (44%) had undergone medical imaging; in the period after CPOE implementation, 2306 patients (40%) had undergone medical imaging (p=.008). In the period before CPOE, 2916 medical imaging procedures were ordered; in the period after CPOE, 2876 medical imaging procedures were ordered (p=.006). In the period before CPOE, 1885 radiographs were ordered; in the period after CPOE, 1776 radiographs were ordered (pmedical imaging did not vary between the two periods. Our results show a decrease in the number of radiograph requests after a CPOE system without clinical decision support was implemented in our emergency department. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  20. Impact decision support diagrams

    Science.gov (United States)

    Boslough, Mark

    2014-10-01

    One way to frame the job of planetary defense is to “find the optimal approach for finding the optimal approach” to NEO mitigation. This requires a framework for defining in advance what should be done under various circumstances. The two-dimensional action matrix from the recent NRC report “Defending Planet Earth” can be generalized to a notional “Impact Decision Support Diagram” by extending it into a third dimension. The NRC action matrix incorporated two important axes: size and time-to-impact, but probability of impact is also critical (it is part of the definitions of both the Torino and Palermo scales). Uncertainty has been neglected, but is also crucial. It can be incorporated by subsuming it into the NEO size axis by redefining size to be three standard deviations greater than the best estimate, thereby providing a built-in conservative margin. The independent variable is time-to-impact, which is known with high precision. The other two axes are both quantitative assessments of uncertainty and are both time dependent. Thus, the diagram is entirely an expression of uncertainty. The true impact probability is either one or zero, and the true size does not change. The domain contains information about the current uncertainty, which changes with time (as opposed to reality, which does not change).

  1. CAD-RADS - a new clinical decision support tool for coronary computed tomography angiography.

    Science.gov (United States)

    Foldyna, Borek; Szilveszter, Bálint; Scholtz, Jan-Erik; Banerji, Dahlia; Maurovich-Horvat, Pál; Hoffmann, Udo

    2018-04-01

    Coronary computed tomography angiography (CTA) has been established as an accurate method to non-invasively assess coronary artery disease (CAD). The proposed 'Coronary Artery Disease Reporting and Data System' (CAD-RADS) may enable standardised reporting of the broad spectrum of coronary CTA findings related to the presence, extent and composition of coronary atherosclerosis. The CAD-RADS classification is a comprehensive tool for summarising findings on a per-patient-basis dependent on the highest-grade coronary artery lesion, ranging from CAD-RADS 0 (absence of CAD) to CAD-RADS 5 (total occlusion of a coronary artery). In addition, it provides suggestions for clinical management for each classification, including further testing and therapeutic options. Despite some limitations, CAD-RADS may facilitate improved communication between imagers and patient caregivers. As such, CAD-RADS may enable a more efficient use of coronary CTA leading to more accurate utilisation of invasive coronary angiograms. Furthermore, widespread use of CAD-RADS may facilitate registry-based research of diagnostic and prognostic aspects of CTA. • CAD-RADS is a tool for standardising coronary CTA reports. • CAD-RADS includes clinical treatment recommendations based on CTA findings. • CAD-RADS has the potential to reduce variability of CTA reports.

  2. Evaluation of a Clinical Decision Support System for Dyslipidemia Treatment (HTE-DLPR by QoE questionnaire

    Directory of Open Access Journals (Sweden)

    Alberto Zamora

    2017-01-01

    Full Text Available Introduction: Clinical decision support systems (CDSS are computer systems designed to assist clinicians with patient-related decision making, such as diagnosis and treatment. CDSS have shown to improve both patient outcomes and cost of care.Methods: A multi-center observational prospective study was conducted. Ten physicians agreed to participate. Seventy-seven patients with high or very high cardiovascular risk were included. After using CDSS for dyslipidemia (HTE-DLPR for a 3 months period, participants were asked to evaluate their experience with HTE-DLPR using a quality of experience questionnaire (QoE tool for mHealth applications.Results: Total score on the QoE was 3.89 out of 5. The highest scores were received for precision, ease of use and content quality. The lowest scores were given to security, appearance and performance. Physicians were in strong agreement with the 1st HTEDLPR recommendation in 86.1% and the system’s use was described as comfortablein 85% of cases. Users positively evaluated the development of a new version of HTEDLPR in the future receiving a total score of 4.25 out of 5.Conclusions: A CDSS for dyslipidemia (HTE-DLP has been positively evaluated by physicians using QoE questionnaire.

  3. Advancing beyond the system: telemedicine nurses' clinical reasoning using a computerised decision support system for patients with COPD - an ethnographic study.

    Science.gov (United States)

    Barken, Tina Lien; Thygesen, Elin; Söderhamn, Ulrika

    2017-12-28

    Telemedicine is changing traditional nursing care, and entails nurses performing advanced and complex care within a new clinical environment, and monitoring patients at a distance. Telemedicine practice requires complex disease management, advocating that the nurses' reasoning and decision-making processes are supported. Computerised decision support systems are being used increasingly to assist reasoning and decision-making in different situations. However, little research has focused on the clinical reasoning of nurses using a computerised decision support system in a telemedicine setting. Therefore, the objective of the study is to explore the process of telemedicine nurses' clinical reasoning when using a computerised decision support system for the management of patients with chronic obstructive pulmonary disease. The factors influencing the reasoning and decision-making processes were investigated. In this ethnographic study, a combination of data collection methods, including participatory observations, the think-aloud technique, and a focus group interview was employed. Collected data were analysed using qualitative content analysis. When telemedicine nurses used a computerised decision support system for the management of patients with complex, unstable chronic obstructive pulmonary disease, two categories emerged: "the process of telemedicine nurses' reasoning to assess health change" and "the influence of the telemedicine setting on nurses' reasoning and decision-making processes". An overall theme, termed "advancing beyond the system", represented the connection between the reasoning processes and the telemedicine work and setting, where being familiar with the patient functioned as a foundation for the nurses' clinical reasoning process. In the telemedicine setting, when supported by a computerised decision support system, nurses' reasoning was enabled by the continuous flow of digital clinical data, regular video-mediated contact and shared decision

  4. Integration of Hospital Information and Clinical Decision Support Systems to Enable the Reuse of Electronic Health Record Data.

    Science.gov (United States)

    Kopanitsa, Georgy

    2017-05-18

    The efficiency and acceptance of clinical decision support systems (CDSS) can increase if they reuse medical data captured during health care delivery. High heterogeneity of the existing legacy data formats has become the main barrier for the reuse of data. Thus, we need to apply data modeling mechanisms that provide standardization, transformation, accumulation and querying medical data to allow its reuse. In this paper, we focus on the interoperability issues of the hospital information systems (HIS) and CDSS data integration. Our study is based on the approach proposed by Marcos et al. where archetypes are used as a standardized mechanism for the interaction of a CDSS with an electronic health record (EHR). We build an integration tool to enable CDSSs collect data from various institutions without a need for modifications in the implementation. The approach implies development of a conceptual level as a set of archetypes representing concepts required by a CDSS. Treatment case data from Regional Clinical Hospital in Tomsk, Russia was extracted, transformed and loaded to the archetype database of a clinical decision support system. Test records' normalization has been performed by defining transformation and aggregation rules between the EHR data and the archetypes. These mapping rules were used to automatically generate openEHR compliant data. After the transformation, archetype data instances were loaded into the CDSS archetype based data storage. The performance times showed acceptable performance for the extraction stage with a mean of 17.428 s per year (3436 case records). The transformation times were also acceptable with 136.954 s per year (0.039 s per one instance). The accuracy evaluation showed the correctness and applicability of the method for the wide range of HISes. These operations were performed without interrupting the HIS workflow to prevent the HISes from disturbing the service provision to the users. The project results have proven that

  5. Computerised clinical decision support systems to improve medication safety in long-term care homes: a systematic review.

    Science.gov (United States)

    Marasinghe, Keshini Madara

    2015-05-12

    Computerised clinical decision support systems (CCDSS) are used to improve the quality of care in various healthcare settings. This systematic review evaluated the impact of CCDSS on improving medication safety in long-term care homes (LTC). Medication safety in older populations is an important health concern as inappropriate medication use can elevate the risk of potentially severe outcomes (ie, adverse drug reactions, ADR). With an increasing ageing population, greater use of LTC by the growing ageing population and increasing number of medication-related health issues in LTC, strategies to improve medication safety are essential. Databases searched included MEDLINE, EMBASE, Scopus and Cochrane Library. Three groups of keywords were combined: those relating to LTC, medication safety and CCDSS. One reviewer undertook screening and quality assessment. Overall findings suggest that CCDSS in LTC improved the quality of prescribing decisions (ie, appropriate medication orders), detected ADR, triggered warning messages (ie, related to central nervous system side effects, drug-associated constipation, renal insufficiency) and reduced injury risk among older adults. CCDSS have received little attention in LTC, as attested by the limited published literature. With an increasing ageing population, greater use of LTC by the ageing population and increased workload for health professionals, merely relying on physicians' judgement on medication safety would not be sufficient. CCDSS to improve medication safety and enhance the quality of prescribing decisions are essential. Analysis of review findings indicates that CCDSS are beneficial, effective and have potential to improve medication safety in LTC; however, the use of CCDSS in LTC is scarce. Careful assessment on the impact of CCDSS on medication safety and further modifications to existing CCDSS are recommended for wider acceptance. Due to scant evidence in the current literature, further research on implementation and

  6. Future perspectives toward the early definition of a multivariate decision-support scheme employed in clinical decision making for senior citizens.

    Science.gov (United States)

    Frantzidis, Christos A; Gilou, Sotiria; Billis, Antonis; Karagianni, Maria; Bratsas, Charalampos D; Bamidis, Panagiotis

    2016-03-01

    Recent neuroscientific studies focused on the identification of pathological neurophysiological patterns (emotions, geriatric depression, memory impairment and sleep disturbances) through computerised clinical decision-support systems. Almost all these research attempts employed either resting-state condition (e.g. eyes-closed) or event-related potentials extracted during a cognitive task known to be affected by the disease under consideration. This Letter reviews existing data mining techniques and aims to enhance their robustness by proposing a holistic decision framework dealing with comorbidities and early symptoms' identification, while it could be applied in realistic occasions. Multivariate features are elicited and fused in order to be compared with average activities characteristic of each neuropathology group. A proposed model of the specific cognitive function which may be based on previous findings (a priori information) and/or validated by current experimental data should be then formed. So, the proposed scheme facilitates the early identification and prevention of neurodegenerative phenomena. Neurophysiological semantic annotation is hypothesised to enhance the importance of the proposed framework in facilitating the personalised healthcare of the information society and medical informatics research community.

  7. A genotypic method for determining HIV-2 coreceptor usage enables epidemiological studies and clinical decision support.

    Science.gov (United States)

    Döring, Matthias; Borrego, Pedro; Büch, Joachim; Martins, Andreia; Friedrich, Georg; Camacho, Ricardo Jorge; Eberle, Josef; Kaiser, Rolf; Lengauer, Thomas; Taveira, Nuno; Pfeifer, Nico

    2016-12-20

    CCR5-coreceptor antagonists can be used for treating HIV-2 infected individuals. Before initiating treatment with coreceptor antagonists, viral coreceptor usage should be determined to ensure that the virus can use only the CCR5 coreceptor (R5) and cannot evade the drug by using the CXCR4 coreceptor (X4-capable). However, until now, no online tool for the genotypic identification of HIV-2 coreceptor usage had been available. Furthermore, there is a lack of knowledge on the determinants of HIV-2 coreceptor usage. Therefore, we developed a data-driven web service for the prediction of HIV-2 coreceptor usage from the V3 loop of the HIV-2 glycoprotein and used the tool to identify novel discriminatory features of X4-capable variants. Using 10 runs of tenfold cross validation, we selected a linear support vector machine (SVM) as the model for geno2pheno[coreceptor-hiv2], because it outperformed the other SVMs with an area under the ROC curve (AUC) of 0.95. We found that SVMs were highly accurate in identifying HIV-2 coreceptor usage, attaining sensitivities of 73.5% and specificities of 96% during tenfold nested cross validation. The predictive performance of SVMs was not significantly different (p value 0.37) from an existing rules-based approach. Moreover, geno2pheno[coreceptor-hiv2] achieved a predictive accuracy of 100% and outperformed the existing approach on an independent data set containing nine new isolates with corresponding phenotypic measurements of coreceptor usage. geno2pheno[coreceptor-hiv2] could not only reproduce the established markers of CXCR4-usage, but also revealed novel markers: the substitutions 27K, 15G, and 8S were significantly predictive of CXCR4 usage. Furthermore, SVMs trained on the amino-acid sequences of the V1 and V2 loops were also quite accurate in predicting coreceptor usage (AUCs of 0.84 and 0.65, respectively). In this study, we developed geno2pheno[coreceptor-hiv2], the first online tool for the prediction of HIV-2 coreceptor

  8. Information Engineering and Workflow Design in a Clinical Decision Support System for Colorectal Cancer Screening in Iran.

    Science.gov (United States)

    Maserat, Elham; Seied Farajollah, Seiede Sedigheh; Safdari, Reza; Ghazisaeedi, Marjan; Aghdaei, Hamid Asadzadeh; Zali, Mohammad Reza

    2015-01-01

    Colorectal cancer is a major cause of morbidity and mortality throughout the world. Colorectal cancer screening is an optimal way for reducing of morbidity and mortality and a clinical decision support system (CDSS) plays an important role in predicting success of screening processes. DSS is a computer-based information system that improves the delivery of preventive care services. The aim of this article was to detail engineering of information requirements and work flow design of CDSS for a colorectal cancer screening program. In the first stage a screening minimum data set was determined. Developed and developing countries were analyzed for identifying this data set. Then information deficiencies and gaps were determined by check list. The second stage was a qualitative survey with a semi-structured interview as the study tool. A total of 15 users and stakeholders' perspectives about workflow of CDSS were studied. Finally workflow of DSS of control program was designed by standard clinical practice guidelines and perspectives. Screening minimum data set of national colorectal cancer screening program was defined in five sections, including colonoscopy data set, surgery, pathology, genetics and pedigree data set. Deficiencies and information gaps were analyzed. Then we designed a work process standard of screening. Finally workflow of DSS and entry stage were determined. A CDSS facilitates complex decision making for screening and has key roles in designing optimal interactions between colonoscopy, pathology and laboratory departments. Also workflow analysis is useful to identify data reconciliation strategies to address documentation gaps. Following recommendations of CDSS should improve quality of colorectal cancer screening.

  9. MINDS - Medical Information Network Decision Support System

    National Research Council Canada - National Science Library

    Armenian, H. K

    2008-01-01

    .... The increase in and complexity of medical data at various levels of resolution has increased the need for system level advancements in clinical decision support systems that provide computer-aided...

  10. Community health workers' experiences of mobile device-enabled clinical decision support systems for maternal, newborn and child health in developing countries: a qualitative systematic review protocol.

    Science.gov (United States)

    Dzabeng, Francis; Enuameh, Yeetey; Adjei, George; Manu, Grace; Asante, Kwaku Poku; Owusu-Agyei, Seth

    2016-09-01

    The objective of this review is to synthesize evidence on the experiences of community health workers (CHWs) of mobile device-enabled clinical decision support systems (CDSSs) interventions designed to support maternal newborn and child health (MNCH) in low-and middle-income countries.Specific objectives.

  11. Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support.

    Science.gov (United States)

    Fung, Kin Wah; Kapusnik-Uner, Joan; Cunningham, Jean; Higby-Baker, Stefanie; Bodenreider, Olivier

    2017-07-01

    To compare 3 commercial knowledge bases (KBs) used for detection and avoidance of potential drug-drug interactions (DDIs) in clinical practice. Drugs in the DDI tables from First DataBank (FDB), Micromedex, and Multum were mapped to RxNorm. The KBs were compared at the clinical drug, ingredient, and DDI rule levels. The KBs were evaluated against a reference list of highly significant DDIs from the Office of the National Coordinator for Health Information Technology (ONC). The KBs and the ONC list were applied to a prescription data set to simulate their use in clinical decision support. The KBs contained 1.6 million (FDB), 4.5 million (Micromedex), and 4.8 million (Multum) clinical drug pairs. Altogether, there were 8.6 million unique pairs, of which 79% were found only in 1 KB and 5% in all 3 KBs. However, there was generally more agreement than disagreement in the severity rankings, especially in the contraindicated category. The KBs covered 99.8-99.9% of the alerts of the ONC list and would have generated 25 (FDB), 145 (Micromedex), and 84 (Multum) alerts per 1000 prescriptions. The commercial KBs differ considerably in size and quantity of alerts generated. There is less variability in severity ranking of DDIs than suggested by previous studies. All KBs provide very good coverage of the ONC list. More work is needed to standardize the editorial policies and evidence for inclusion of DDIs to reduce variation among knowledge sources and improve relevance. Some DDIs considered contraindicated in all 3 KBs might be possible candidates to add to the ONC list. Published by Oxford University Press on behalf of the American Medical Informatics Association 2017. This work is written by US Government employees and is in the public domain in the United States.

  12. A health record integrated clinical decision support system to support prescriptions of pharmaceutical drugs in patients with reduced renal function: design, development and proof of concept.

    Science.gov (United States)

    Shemeikka, Tero; Bastholm-Rahmner, Pia; Elinder, Carl-Gustaf; Vég, Anikó; Törnqvist, Elisabeth; Cornelius, Birgitta; Korkmaz, Seher

    2015-06-01

    To develop and verify proof of concept for a clinical decision support system (CDSS) to support prescriptions of pharmaceutical drugs in patients with reduced renal function, integrated in an electronic health record system (EHR) used in both hospitals and primary care. A pilot study in one geriatric clinic, one internal medicine admission ward and two outpatient healthcare centers was evaluated with a questionnaire focusing on the usefulness of the CDSS. The usage of the system was followed in a log. The CDSS is considered to increase the attention on patients with impaired renal function, provides a better understanding of dosing and is time saving. The calculated glomerular filtration rate (eGFR) and the dosing recommendation classification were perceived useful while the recommendation texts and background had been used to a lesser extent. Few previous systems are used in primary care and cover this number of drugs. The global assessment of the CDSS scored high but some elements were used to a limited extent possibly due to accessibility or that texts were considered difficult to absorb. Choosing a formula for the calculation of eGFR in a CDSS may be problematic. A real-time CDSS to support kidney-related drug prescribing in both hospital and outpatient settings is valuable to the physicians. It has the potential to improve quality of drug prescribing by increasing the attention on patients with renal insufficiency and the knowledge of their drug dosing. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  13. A review of randomized controlled trials of medical record powered clinical decision support system to improve quality of diabetes care.

    Science.gov (United States)

    Ali, Syed Mustafa; Giordano, Richard; Lakhani, Saima; Walker, Dawn Marie

    2016-03-01

    A gap between current diabetes care practice and recommended diabetes care standards has consistently been reported in the literature. Many IT-based interventions have been developed to improve adherence to the quality of care standards for chronic illness like diabetes. The widespread implementation of electronic medical/health records has catalyzed clinical decision support systems (CDSS) which may improve the quality of diabetes care. Therefore, the objective of the review is to evaluate the effectiveness of CDSS in improving quality of type II diabetes care. Moreover, the review aims to highlight the key indicators of quality improvement to assist policy makers in development of future diabetes care policies through the integration of information technology and system. Setting inclusion criteria, a systematic literature search was conducted using Medline, Web of Science and Science Direct. Critical Appraisal Skills Programme (CASP) tools were used to evaluate the quality of studies. Eight randomized controlled trials (RCTs) were selected for the review. In the selected studies, seventeen clinical markers of diabetes care were discussed. Three quality of care indicators were given more importance in monitoring the progress of diabetes care, which is consistent with National Institute for Health and Care Excellence (NICE) guidelines. The presence of these indicators in the studies helped to determine which studies were selected for review. Clinical- and process-related improvements are compared between intervention group using CDSS and control group with usual care. Glycated hemoglobin (HbA1c), low density lipid cholesterol (LDL-C) and blood pressure (BP) were the quality of care indicators studied at the levels of process of care and clinical outcome. The review has found both inconsistent and variable results for quality of diabetes care measures. A significant improvement has been found in the process of care for all three measures of quality of diabetes care

  14. Implementation of a scalable, web-based, automated clinical decision support risk-prediction tool for chronic kidney disease using C-CDA and application programming interfaces.

    Science.gov (United States)

    Samal, Lipika; D'Amore, John D; Bates, David W; Wright, Adam

    2017-11-01

    Clinical decision support tools for risk prediction are readily available, but typically require workflow interruptions and manual data entry so are rarely used. Due to new data interoperability standards for electronic health records (EHRs), other options are available. As a clinical case study, we sought to build a scalable, web-based system that would automate calculation of kidney failure risk and display clinical decision support to users in primary care practices. We developed a single-page application, web server, database, and application programming interface to calculate and display kidney failure risk. Data were extracted from the EHR using the Consolidated Clinical Document Architecture interoperability standard for Continuity of Care Documents (CCDs). EHR users were presented with a noninterruptive alert on the patient's summary screen and a hyperlink to details and recommendations provided through a web application. Clinic schedules and CCDs were retrieved using existing application programming interfaces to the EHR, and we provided a clinical decision support hyperlink to the EHR as a service. We debugged a series of terminology and technical issues. The application was validated with data from 255 patients and subsequently deployed to 10 primary care clinics where, over the course of 1 year, 569 533 CCD documents were processed. We validated the use of interoperable documents and open-source components to develop a low-cost tool for automated clinical decision support. Since Consolidated Clinical Document Architecture-based data extraction extends to any certified EHR, this demonstrates a successful modular approach to clinical decision support. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  15. Agile Acceptance Test-Driven Development of Clinical Decision Support Advisories: Feasibility of Using Open Source Software.

    Science.gov (United States)

    Basit, Mujeeb A; Baldwin, Krystal L; Kannan, Vaishnavi; Flahaven, Emily L; Parks, Cassandra J; Ott, Jason M; Willett, Duwayne L

    2018-04-13

    Moving to electronic health records (EHRs) confers substantial benefits but risks unintended consequences. Modern EHRs consist of complex software code with extensive local configurability options, which can introduce defects. Defects in clinical decision support (CDS) tools are surprisingly common. Feasible approaches to prevent and detect defects in EHR configuration, including CDS tools, are needed. In complex software systems, use of test-driven development and automated regression testing promotes reliability. Test-driven development encourages modular, testable design and expanding regression test coverage. Automated regression test suites improve software quality, providing a "safety net" for future software modifications. Each automated acceptance test serves multiple purposes, as requirements (prior to build), acceptance testing (on completion of build), regression testing (once live), and "living" design documentation. Rapid-cycle development or "agile" methods are being successfully applied to CDS development. The agile practice of automated test-driven development is not widely adopted, perhaps because most EHR software code is vendor-developed. However, key CDS advisory configuration design decisions and rules stored in the EHR may prove amenable to automated testing as "executable requirements." We aimed to establish feasibility of acceptance test-driven development of clinical decision support advisories in a commonly used EHR, using an open source automated acceptance testing framework (FitNesse). Acceptance tests were initially constructed as spreadsheet tables to facilitate clinical review. Each table specified one aspect of the CDS advisory's expected behavior. Table contents were then imported into a test suite in FitNesse, which queried the EHR database to automate testing. Tests and corresponding CDS configuration were migrated together from the development environment to production, with tests becoming part of the production regression test

  16. Spatial Decision Support Systems

    Directory of Open Access Journals (Sweden)

    Silviu Ioan Bejinariu

    2015-10-01

    Full Text Available The satellite image processing is an important tool for decision making in domains like agriculture, forestry, hydrology, for normal activity tracking but also in special situations caused by natural disasters. In this paper it is proposed a method for forestry surface evaluation in terms of occupied surface and also as number of trees. The segmentation method is based on watershed transform which offers good performances in case the objects to detect have connected borders. The method is applied for automatic multi-temporal analysis of forestry areas and represents a useful instrument for decision makers.

  17. Computerized clinical decision support systems for primary preventive care: a decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes.

    Science.gov (United States)

    Souza, Nathan M; Sebaldt, Rolf J; Mackay, Jean A; Prorok, Jeanette C; Weise-Kelly, Lorraine; Navarro, Tamara; Wilczynski, Nancy L; Haynes, R Brian

    2011-08-03

    Computerized clinical decision support systems (CCDSSs) are claimed to improve processes and outcomes of primary preventive care (PPC), but their effects, safety, and acceptance must be confirmed. We updated our previous systematic reviews of CCDSSs and integrated a knowledge translation approach in the process. The objective was to review randomized controlled trials (RCTs) assessing the effects of CCDSSs for PPC on process of care, patient outcomes, harms, and costs. We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews Database, Inspec, and other databases, as well as reference lists through January 2010. We contacted authors to confirm data or provide additional information. We included RCTs that assessed the effect of a CCDSS for PPC on process of care and patient outcomes compared to care provided without a CCDSS. A study was considered to have a positive effect (i.e., CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive. We added 17 new RCTs to our 2005 review for a total of 41 studies. RCT quality improved over time. CCDSSs improved process of care in 25 of 40 (63%) RCTs. Cumulative scientifically strong evidence supports the effectiveness of CCDSSs for screening and management of dyslipidaemia in primary care. There is mixed evidence for effectiveness in screening for cancer and mental health conditions, multiple preventive care activities, vaccination, and other preventive care interventions. Fourteen (34%) trials assessed patient outcomes, and four (29%) reported improvements with the CCDSS. Most trials were not powered to evaluate patient-important outcomes. CCDSS costs and adverse events were reported in only six (15%) and two (5%) trials, respectively. Information on study duration was often missing, limiting our ability to assess sustainability of CCDSS effects. Evidence supports the effectiveness of CCDSSs for screening and

  18. Computerized clinical decision support systems for primary preventive care: A decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes

    Directory of Open Access Journals (Sweden)

    Wilczynski Nancy L

    2011-08-01

    Full Text Available Abstract Background Computerized clinical decision support systems (CCDSSs are claimed to improve processes and outcomes of primary preventive care (PPC, but their effects, safety, and acceptance must be confirmed. We updated our previous systematic reviews of CCDSSs and integrated a knowledge translation approach in the process. The objective was to review randomized controlled trials (RCTs assessing the effects of CCDSSs for PPC on process of care, patient outcomes, harms, and costs. Methods We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews Database, Inspec, and other databases, as well as reference lists through January 2010. We contacted authors to confirm data or provide additional information. We included RCTs that assessed the effect of a CCDSS for PPC on process of care and patient outcomes compared to care provided without a CCDSS. A study was considered to have a positive effect (i.e., CCDSS showed improvement if at least 50% of the relevant study outcomes were statistically significantly positive. Results We added 17 new RCTs to our 2005 review for a total of 41 studies. RCT quality improved over time. CCDSSs improved process of care in 25 of 40 (63% RCTs. Cumulative scientifically strong evidence supports the effectiveness of CCDSSs for screening and management of dyslipidaemia in primary care. There is mixed evidence for effectiveness in screening for cancer and mental health conditions, multiple preventive care activities, vaccination, and other preventive care interventions. Fourteen (34% trials assessed patient outcomes, and four (29% reported improvements with the CCDSS. Most trials were not powered to evaluate patient-important outcomes. CCDSS costs and adverse events were reported in only six (15% and two (5% trials, respectively. Information on study duration was often missing, limiting our ability to assess sustainability of CCDSS effects. Conclusions

  19. Development and Implementation of the Clinical Decision Support System for Patients With Cancer and Nurses' Experiences Regarding the System.

    Science.gov (United States)

    Yılmaz, Arzu Akman; Ozdemir, Leyla

    2017-01-01

    The purpose of this study was to develop and implement the clinical decision support system (CDSS) for oncology nurses in the care of patients with cancer and to explore the nurses' experiences about the system. The study was conducted using a mixed-methods research design with 14 nurses working at a gynecological oncology clinic at a university hospital in Turkey. The nurses stated that they did not experience any problems during the implementation of the CDSS, and its usage facilitated the assessment of patients' needs and care management. The results indicated that the CDSS supported the nurses' decision-making process about patients' needs and preparation of individual care plans. The CDSS should be developed and implemented by the nurses working with patients with cancer. AMAÇ: Amaç kanser hastalarının bakımına yönelik klinik karar destek sistemi oluşturmak, uygulamak (KKDS) ve sistemi kullanan hemşirelerin deneyimlerini incelemektir. YÖNTEM: Çalışma kalitatif ve kantitatif araştırma yöntemleri kullanılarak Türkiyede'ki bir üniversite hastanesinin jinekolojik onkoloji servisinde çalışan 14 hemşire ile yürütülmüştür. Hemşireler KKDS'ni kullanırken herhangi bir sorun yaşamadıklarını ve sistemin hasta gereksinimlerini değerlendirmeyi ve bakım yönetimini kolaylaştırdığını belirtmişlerdir. SONUÇ: Bulgular hastanın gereksinimlerine karar verme sürecinde ve bireysel bakım planları hazırlamada KKDS'nin hemşireleri desteklediğini göstermektedir. HEMŞIRELIK UYGULAMALARI IÇIN ÖNERILER: Kanserli hastaların bakımına yönelik KKDS geliştirilebilir ve hemşireler tarafından klinikte kullanılabilir. © 2015 NANDA International, Inc.

  20. Continuous Decision Support

    Science.gov (United States)

    2015-12-24

    typically either a corporate finance perspective such as net present value, an operations research perspective that treats the issue as a knapsack...117 6.6 +Resources Reactive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 6.7 ++Resources Reactive with Pearson -Tukey...applying financial methods deals with a class of techniques known as real options methods. [11, 80] In a real options framework, “any corporate decision

  1. SMARTHealth India: Development and Field Evaluation of a Mobile Clinical Decision Support System for Cardiovascular Diseases in Rural India.

    Science.gov (United States)

    Praveen, Devarsetty; Patel, Anushka; Raghu, Arvind; Clifford, Gari D; Maulik, Pallab K; Mohammad Abdul, Ameer; Mogulluru, Kishor; Tarassenko, Lionel; MacMahon, Stephen; Peiris, David

    2014-12-08

    Cardiovascular disease (CVD) is the major cause of premature death and disability in India and yet few people at risk of CVD are able to access best practice health care. Mobile health (mHealth) is a promising solution, but very few mHealth interventions have been subjected to robust evaluation in India. The objectives were to develop a multifaceted, mobile clinical decision support system (CDSS) for CVD management and evaluate it for use by public nonphysician health care workers (NPHWs) and physicians in a rural Indian setting. Plain language clinical rules were developed based on standard guidelines and programmed into a computer tablet app. The algorithm was validated and field-tested in 11 villages in Andhra Pradesh, involving 11 NPHWs and 3 primary health center (PHC) physicians. A mixed method evaluation was conducted comprising clinical and survey data and in-depth patient and staff interviews to understand barriers and enablers to the use of the system. Then this was thematically analyzed using NVivo 10. During validation of the algorithm, there was an initial agreement for 70% of the 42 calculated variables between the CDSS and SPSS software outputs. Discrepancies were identified and amendments were made until perfect agreement was achieved. During field testing, NPHWs and PHC physicians used the CDSS to screen 227 and 65 adults, respectively. The NPHWs identified 39% (88/227) of patients for referral with 78% (69/88) of these having a definite indication for blood pressure (BP)-lowering medication. However, only 35% (24/69) attended a clinic within 1 month of referral, with 42% (10/24) of these reporting continuing medications at 3-month follow-up. Physicians identified and recommended 17% (11/65) of patients for BP-lowering medications. Qualitative interviews identified 3 interrelated interview themes: (1) the CDSS had potential to change prevailing health care models, (2) task-shifting to NPHWs was the central driver of change, and (3) despite high

  2. Usability evaluation of pharmacogenomics clinical decision support aids and clinical knowledge resources in a computerized provider order entry system: a mixed methods approach.

    Science.gov (United States)

    Devine, Emily Beth; Lee, Chia-Ju; Overby, Casey L; Abernethy, Neil; McCune, Jeannine; Smith, Joe W; Tarczy-Hornoch, Peter

    2014-07-01

    Pharmacogenomics (PGx) is positioned to have a widespread impact on the practice of medicine, yet physician acceptance is low. The presentation of context-specific PGx information, in the form of clinical decision support (CDS) alerts embedded in a computerized provider order entry (CPOE) system, can aid uptake. Usability evaluations can inform optimal design, which, in turn, can spur adoption. The study objectives were to: (1) evaluate an early prototype, commercial CPOE system with PGx-CDS alerts in a simulated environment, (2) identify potential improvements to the system user interface, and (3) understand the contexts under which PGx knowledge embedded in an electronic health record is useful to prescribers. Using a mixed methods approach, we presented seven cardiologists and three oncologists with five hypothetical clinical case scenarios. Each scenario featured a drug for which a gene encoding drug metabolizing enzyme required consideration of dosage adjustment. We used Morae(®) to capture comments and on-screen movements as participants prescribed each drug. In addition to PGx-CDS alerts, 'Infobutton(®)' and 'Evidence' icons provided participants with clinical knowledge resources to aid decision-making. Nine themes emerged. Five suggested minor improvements to the CPOE user interface; two suggested presenting PGx information through PGx-CDS alerts using an 'Infobutton' or 'Evidence' icon. The remaining themes were strong recommendations to provide succinct, relevant guidelines and dosing recommendations of phenotypic information from credible and trustworthy sources; any more information was overwhelming. Participants' median rating of PGx-CDS system usability was 2 on a Likert scale ranging from 1 (strongly agree) to 7 (strongly disagree). Usability evaluation results suggest that participants considered PGx information important for improving prescribing decisions; and that they would incorporate PGx-CDS when information is presented in relevant and

  3. The limitations of using the existing TAM in adoption of clinical decision support system in hospitals: An emprical study in Malaysia

    Directory of Open Access Journals (Sweden)

    Pouyan Emaeilzadeh

    2016-01-01

    Physician adoption of clinical information technology is important for its successful implementation. Therefore, the purpose of this study is to gain a better insight about factors affecting physicians’ acceptance of clinical decision support systems (CDSS in a hospital setting. The results reflect the importance of perceived threat to professional autonomy, perceived interactivity with clinical IT, perceived usefulness and perceived ease of use in determining physicians’ intention to use CDSS.

  4. An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

    Directory of Open Access Journals (Sweden)

    Panagiotis Bountris

    2014-01-01

    Full Text Available Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV, including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS, composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%, high specificity (97.1%, high positive predictive value (89.4%, and high negative predictive value (97.1%, for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+. In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.

  5. Impact of four training conditions on physician use of a web-based clinical decision support system.

    Science.gov (United States)

    Kealey, Edith; Leckman-Westin, Emily; Finnerty, Molly T

    2013-09-01

    Training has been identified as an important barrier to implementation of clinical decision support systems (CDSSs), but little is known about the effectiveness of different training approaches. Using an observational retrospective cohort design, we examined the impact of four training conditions on physician use of a CDSS: (1) computer lab training with individualized follow-up (CL-FU) (n=40), (2) computer lab training without follow-up (CL) (n=177), (3) lecture demonstration (LD) (n=16), or (4) no training (NT) (n=134). Odds ratios of any use and ongoing use under training conditions were compared to no training over a 2-year follow-up period. CL-FU was associated with the highest percent of active users and odds for any use (90.0%, odds ratio (OR)=10.2, 95% confidence interval (CI): 3.2-32.9) and ongoing use (60.0%, OR=6.1 95% CI: 2.6-13.7), followed by CL (any use=81.4%, OR=5.3, CI: 2.9-9.6; ongoing use=28.8%, OR=1.7, 95% CI: 1.0-3.0). LD was not superior to no training (any use=47%, ongoing use=22.4%). Training format may have differential effects on initial and long-term follow-up of CDSSs use by physicians. Copyright © 2013 Elsevier B.V. All rights reserved.

  6. An intelligent clinical decision support system for patient-specific predictions to improve cervical intraepithelial neoplasia detection.

    Science.gov (United States)

    Bountris, Panagiotis; Haritou, Maria; Pouliakis, Abraham; Margari, Niki; Kyrgiou, Maria; Spathis, Aris; Pappas, Asimakis; Panayiotides, Ioannis; Paraskevaidis, Evangelos A; Karakitsos, Petros; Koutsouris, Dimitrios-Dionyssios

    2014-01-01

    Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.

  7. Architecture Design of Healthcare Software-as-a-Service Platform for Cloud-Based Clinical Decision Support Service

    OpenAIRE

    Oh, Sungyoung; Cha, Jieun; Ji, Myungkyu; Kang, Hyekyung; Kim, Seok; Heo, Eunyoung; Han, Jong Soo; Kang, Hyunggoo; Chae, Hoseok; Hwang, Hee; Yoo, Sooyoung

    2015-01-01

    Objectives To design a cloud computing-based Healthcare Software-as-a-Service (SaaS) Platform (HSP) for delivering healthcare information services with low cost, high clinical value, and high usability. Methods We analyzed the architecture requirements of an HSP, including the interface, business services, cloud SaaS, quality attributes, privacy and security, and multi-lingual capacity. For cloud-based SaaS services, we focused on Clinical Decision Service (CDS) content services, basic functi...

  8. Social, Organizational, and Contextual Characteristics of Clinical Decision Support Systems for Intensive Insulin Therapy: A Literature Review and Case Study

    Science.gov (United States)

    Campion, Thomas R.; Waitman, Lemuel R.; May, Addison K.; Ozdas, Asli; Lorenzi, Nancy M.; Gadd, Cynthia S.

    2009-01-01

    Introduction: Evaluations of computerized clinical decision support systems (CDSS) typically focus on clinical performance changes and do not include social, organizational, and contextual characteristics explaining use and effectiveness. Studies of CDSS for intensive insulin therapy (IIT) are no exception, and the literature lacks an understanding of effective computer-based IIT implementation and operation. Results: This paper presents (1) a literature review of computer-based IIT evaluations through the lens of institutional theory, a discipline from sociology and organization studies, to demonstrate the inconsistent reporting of workflow and care process execution and (2) a single-site case study to illustrate how computer-based IIT requires substantial organizational change and creates additional complexity with unintended consequences including error. Discussion: Computer-based IIT requires organizational commitment and attention to site-specific technology, workflow, and care processes to achieve intensive insulin therapy goals. The complex interaction between clinicians, blood glucose testing devices, and CDSS may contribute to workflow inefficiency and error. Evaluations rarely focus on the perspective of nurses, the primary users of computer-based IIT whose knowledge can potentially lead to process and care improvements. Conclusion: This paper addresses a gap in the literature concerning the social, organizational, and contextual characteristics of CDSS in general and for intensive insulin therapy specifically. Additionally, this paper identifies areas for future research to define optimal computer-based IIT process execution: the frequency and effect of manual data entry error of blood glucose values, the frequency and effect of nurse overrides of CDSS insulin dosing recommendations, and comprehensive ethnographic study of CDSS for IIT. PMID:19815452

  9. Social, organizational, and contextual characteristics of clinical decision support systems for intensive insulin therapy: a literature review and case study.

    Science.gov (United States)

    Campion, Thomas R; Waitman, Lemuel R; May, Addison K; Ozdas, Asli; Lorenzi, Nancy M; Gadd, Cynthia S

    2010-01-01

    Evaluations of computerized clinical decision support systems (CDSS) typically focus on clinical performance changes and do not include social, organizational, and contextual characteristics explaining use and effectiveness. Studies of CDSS for intensive insulin therapy (IIT) are no exception, and the literature lacks an understanding of effective computer-based IIT implementation and operation. This paper presents (1) a literature review of computer-based IIT evaluations through the lens of institutional theory, a discipline from sociology and organization studies, to demonstrate the inconsistent reporting of workflow and care process execution and (2) a single-site case study to illustrate how computer-based IIT requires substantial organizational change and creates additional complexity with unintended consequences including error. Computer-based IIT requires organizational commitment and attention to site-specific technology, workflow, and care processes to achieve intensive insulin therapy goals. The complex interaction between clinicians, blood glucose testing devices, and CDSS may contribute to workflow inefficiency and error. Evaluations rarely focus on the perspective of nurses, the primary users of computer-based IIT whose knowledge can potentially lead to process and care improvements. This paper addresses a gap in the literature concerning the social, organizational, and contextual characteristics of CDSS in general and for intensive insulin therapy specifically. Additionally, this paper identifies areas for future research to define optimal computer-based IIT process execution: the frequency and effect of manual data entry error of blood glucose values, the frequency and effect of nurse overrides of CDSS insulin dosing recommendations, and comprehensive ethnographic study of CDSS for IIT. Copyright (c) 2009. Published by Elsevier Ireland Ltd.

  10. Exploring a clinically friendly web-based approach to clinical decision support linked to the electronic health record: design philosophy, prototype implementation, and framework for assessment.

    Science.gov (United States)

    Miller, Perry; Phipps, Michael; Chatterjee, Sharmila; Rajeevan, Nallakkandi; Levin, Forrest; Frawley, Sandra; Tokuno, Hajime

    2014-07-01

    Computer-based clinical decision support (CDS) is an important component of the electronic health record (EHR). As an increasing amount of CDS is implemented, it will be important that this be accomplished in a fashion that assists in clinical decision making without imposing unacceptable demands and burdens upon the provider's practice. The objective of our study was to explore an approach that allows CDS to be clinician-friendly from a variety of perspectives, to build a prototype implementation that illustrates features of the approach, and to gain experience with a pilot framework for assessment. The paper first discusses the project's design philosophy and goals. It then describes a prototype implementation (Neuropath/CDS) that explores the approach in the domain of neuropathic pain and in the context of the US Veterans Administration EHR. Finally, the paper discusses a framework for assessing the approach, illustrated by a pilot assessment of Neuropath/CDS. The paper describes the operation and technical design of Neuropath/CDS, as well as the results of the pilot assessment, which emphasize the four areas of focus, scope, content, and presentation. The work to date has allowed us to explore various design and implementation issues relating to the approach illustrated in Neuropath/CDS, as well as the development and pilot application of a framework for assessment.

  11. Transforming user needs into functional requirements for an antibiotic clinical decision support system: explicating content analysis for system design.

    Science.gov (United States)

    Bright, T J

    2013-01-01

    Many informatics studies use content analysis to generate functional requirements for system development. Explication of this translational process from qualitative data to functional requirements can strengthen the understanding and scientific rigor when applying content analysis in informatics studies. To describe a user-centered approach transforming emergent themes derived from focus group data into functional requirements for informatics solutions and to illustrate these methods to the development of an antibiotic clinical decision support system (CDS). THE APPROACH CONSISTED OF FIVE STEPS: 1) identify unmet therapeutic planning information needs via Focus Group Study-I, 2) develop a coding framework of therapeutic planning themes to refine the domain scope to antibiotic therapeutic planning, 3) identify functional requirements of an antibiotic CDS system via Focus Group Study-II, 4) discover informatics solutions and functional requirements from coded data, and 5) determine the types of information needed to support the antibiotic CDS system and link with the identified informatics solutions and functional requirements. The coding framework for Focus Group Study-I revealed unmet therapeutic planning needs. Twelve subthemes emerged and were clustered into four themes; analysis indicated a need for an antibiotic CDS intervention. Focus Group Study-II included five types of information needs. Comments from the Barrier/Challenge to information access and Function/Feature themes produced three informatics solutions and 13 functional requirements of an antibiotic CDS system. Comments from the Patient, Institution, and Domain themes generated required data elements for each informatics solution. This study presents one example explicating content analysis of focus group data and the analysis process to functional requirements from narrative data. Illustration of this 5-step method was used to develop an antibiotic CDS system, resolving unmet antibiotic prescribing

  12. Effect of a clinical decision support system on adherence to a lower tidal volume mechanical ventilation strategy

    NARCIS (Netherlands)

    Eslami, Saeid; de Keizer, Nicolette F.; Abu-Hanna, Ameen; de Jonge, Evert; Schultz, Marcus J.

    2009-01-01

    PURPOSE: The purpose of the study was to measure the effect of a computerized decision support system (CDSS) on adherence to tidal volume (V(T)) recommendations. MATERIALS AND METHODS: We performed a prospective before-after evaluation study on applied V(T) to examine the impact of a CDSS on

  13. Decision support for emergency management

    International Nuclear Information System (INIS)

    Andersen, V.

    1989-05-01

    A short introduction will be given to the Nordic project ''NKA/INF: Information Technology for Accident and Emergency Management'', which is now in its final phase. To perform evaluation of the project, special scenarious have been developed, and experiments based on these will be fulfilled and compared with experiments without use of the decision support system. Furthermore, the succeeding European project, ''IT Support for Emergency Management - ISEM'', with the purpose of developing a decision support system for complex and distributed decision making in emergency management in full scale, will be described and the preliminary conceptual model for the system will be presented. (author)

  14. Feasibility study of a clinical decision support system for the management of multimorbid seniors in primary care: study protocol.

    Science.gov (United States)

    Weltermann, Birgitta; Kersting, Christine

    2016-01-01

    Care for seniors is complex because patients often have more than one disease, one medication, and one physician. It is a key challenge for primary care physicians to structure the various aspects of each patient's care, to integrate each patient's preferences, and to maintain a long-term overview. This article describes the design for the development and feasibility testing of the clinical decision support system (CDSS) eCare*Seniors© which is electronic health record (EHR)-based allowing for a long-term, comprehensive, evidence-based, and patient preference-oriented management of multimorbid seniors. This mixed-methods study is designed in three steps. First, focus groups and practice observations will be conducted to develop criteria for software design from a physicians' and practice assistants' perspective. Second, based on these criteria, a CDSS prototype will be developed. Third, the prototype's feasibility will be tested by five primary care practices in the care of 30 multimorbid seniors. Primary outcome is the usability of the software measured by the validated system usability scale (SUS) after 3 months. Secondary outcomes are the (a) willingness to routinely use the CDSS, (b) degree of utilization of the CDSS, (c) acceptance of the CDSS, (d) willingness of the physicians to purchase the CDSS, and (e) willingness of the practice assistants to use the CDSS in the long term. These outcomes will be measured using semi-structured interviews and software usage data. If the SUS score reaches ≥70 %, feasibility testing will be judged successful. Otherwise, the CDSS prototype will be refined according to the users' needs and retested by the physicians and practice assistants until it is fully adapted to their requirements and reaches a usability score ≥70 %. The study will support the development of a CDSS which is primary care-defined, user-friendly, easy-to-comprehend, workflow-oriented, and comprehensive. The software will assist physicians and

  15. Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sang Youn; Moon, Sung Kyoung; Hwang, Sung Il; Sung, Chang Kyu; Cho, Jeong Yeon; Kim, Seung Hyup; Lee, Hak Jong [Seoul National University College of Medicine, Seoul (Korea, Republic of); Jung, Dae Chul [National Cancer Center, Ilsan (Korea, Republic of); Lee, Ji Won [Kangwon National University College of Medicine, Chuncheon (Korea, Republic of)

    2011-10-15

    The purpose of the current study was to develop support vector machine (SVM) and artificial neural network (ANN) models for the pre-operative prediction of advanced prostate cancer by using the parameters acquired from transrectal ultrasound (TRUS)-guided prostate biopsies, and to compare the accuracies between the two models. Five hundred thirty-two consecutive patients who underwent prostate biopsies and prostatectomies for prostate cancer were divided into the training and test groups (n = 300 versus n 232). From the data in the training group, two clinical decision support systems (CDSSs-[SVM and ANN]) were constructed with input (age, prostate specific antigen level, digital rectal examination, and five biopsy parameters) and output data (the probability for advanced prostate cancer [> pT3a]). From the data of the test group, the accuracy of output data was evaluated. The areas under the receiver operating characteristic (ROC) curve (AUC) were calculated to summarize the overall performances, and a comparison of the ROC curves was performed (p < 0.05). The AUC of SVM and ANN is 0.805 and 0.719, respectively (p = 0.020), in the pre-operative prediction of advanced prostate cancer. Te performance of SVM is superior to ANN in the pre-operative prediction of advanced prostate cancer.

  16. An Organizational Informatics Analysis of Colorectal, Breast, and Cervical Cancer Screening Clinical Decision Support and Information Systems within Community Health Centers

    Science.gov (United States)

    Carney, Timothy Jay

    2012-01-01

    A study design has been developed that employs a dual modeling approach to identify factors associated with facility-level cancer screening improvement and how this is mediated by the use of clinical decision support. This dual modeling approach combines principles of (1) Health Informatics, (2) Cancer Prevention and Control, (3) Health Services…

  17. A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: A two-armed randomized controlled trial

    NARCIS (Netherlands)

    Beeckman, D.; Clays, E.; Hecke, A. Van; Vanderwee, K.; Schoonhoven, L.; Verhaeghe, S.

    2013-01-01

    BACKGROUND: Frail older people admitted to nursing homes are at risk of a range of adverse outcomes, including pressure ulcers. Clinical decision support systems are believed to have the potential to improve care and to change the behaviour of healthcare professionals. OBJECTIVES: To determine

  18. Impact on process results of clinical decision support systems (CDSSs) applied to medication use: overview of systematic reviews.

    Science.gov (United States)

    Reis, Wálleri C; Bonetti, Aline F; Bottacin, Wallace E; Reis, Alcindo S; Souza, Thaís T; Pontarolo, Roberto; Correr, Cassyano J; Fernandez-Llimos, Fernando

    2017-01-01

    The purpose of this overview (systematic review of systematic reviews) is to evaluate the impact of clinical decision support systems (CDSS) applied to medication use in the care process. A search for systematic reviews that address CDSS was performed on Medline following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Cochrane recommendations. Terms related to CDSS and systematic reviews were used in combination with Boolean operators and search field tags to build the electronic search strategy. There was no limitation of date or language for inclusion. We included revisions that investigated, as a main or secondary objective, changes in process outcomes. The Revised Assessment of Multiple Systematic Reviews (R-AMSTAR) score was used to evaluate the quality of the studies. The search retrieved 954 articles. Five articles were added through manual search, totaling an initial sample of 959 articles. After screening and reading in full, 44 systematic reviews met the inclusion criteria. In the medication-use processes where CDSS was used, the most common stages were prescribing (n=38 (86.36%) and administering (n=12 (27.27%)). Most of the systematic reviews demonstrated improvement in the health care process (30/44 - 68.2%). The main positive results were related to improvement of the quality of prescription by the physicians (14/30 - 46.6%) and reduction of errors in prescribing (5/30 - 16.6%). However, the quality of the studies was poor, according to the score used. CDSSs represent a promising technology to optimize the medication-use process, especially related to improvement in the quality of prescriptions and reduction of prescribing errors, although higher quality studies are needed to establish the predictors of success in these systems.

  19. Cost and economic benefit of clinical decision support systems for cardiovascular disease prevention: a community guide systematic review.

    Science.gov (United States)

    Jacob, Verughese; Thota, Anilkrishna B; Chattopadhyay, Sajal K; Njie, Gibril J; Proia, Krista K; Hopkins, David P; Ross, Murray N; Pronk, Nicolaas P; Clymer, John M

    2017-05-01

    This review evaluates costs and benefits associated with acquiring, implementing, and operating clinical decision support systems (CDSSs) to prevent cardiovascular disease (CVD). Methods developed for the Community Guide were used to review CDSS literature covering the period from January 1976 to October 2015. Twenty-one studies were identified for inclusion. It was difficult to draw a meaningful estimate for the cost of acquiring and operating CDSSs to prevent CVD from the available studies ( n  = 12) due to considerable heterogeneity. Several studies ( n  = 11) indicated that health care costs were averted by using CDSSs but many were partial assessments that did not consider all components of health care. Four cost-benefit studies reached conflicting conclusions about the net benefit of CDSSs based on incomplete assessments of costs and benefits. Three cost-utility studies indicated inconsistent conclusions regarding cost-effectiveness based on a conservative $50,000 threshold. Intervention costs were not negligible, but specific estimates were not derived because of the heterogeneity of implementation and reporting metrics. Expected economic benefits from averted health care cost could not be determined with confidence because many studies did not fully account for all components of health care. We were unable to conclude whether CDSSs for CVD prevention is either cost-beneficial or cost-effective. Several evidence gaps are identified, most prominently a lack of information about major drivers of cost and benefit, a lack of standard metrics for the cost of CDSSs, and not allowing for useful life of a CDSS that generally extends beyond one accounting period. Published by Oxford University Press on behalf of the American Medical Informatics Association 2017. This work is written by US Government employees and is in the public domain in the US.

  20. Evaluation of a clinical decision support tool for osteoporosis disease management: protocol for an interrupted time series design.

    Science.gov (United States)

    Kastner, Monika; Sawka, Anna; Thorpe, Kevin; Chignel, Mark; Marquez, Christine; Newton, David; Straus, Sharon E

    2011-07-22

    Osteoporosis affects over 200 million people worldwide at a high cost to healthcare systems. Although guidelines on assessing and managing osteoporosis are available, many patients are not receiving appropriate diagnostic testing or treatment. Findings from a systematic review of osteoporosis interventions, a series of mixed-methods studies, and advice from experts in osteoporosis and human-factors engineering were used collectively to develop a multicomponent tool (targeted to family physicians and patients at risk for osteoporosis) that may support clinical decision making in osteoporosis disease management at the point of care. A three-phased approach will be used to evaluate the osteoporosis tool. In phase 1, the tool will be implemented in three family practices. It will involve ensuring optimal functioning of the tool while minimizing disruption to usual practice. In phase 2, the tool will be pilot tested in a quasi-experimental interrupted time series (ITS) design to determine if it can improve osteoporosis disease management at the point of care. Phase 3 will involve conducting a qualitative postintervention follow-up study to better understand participants' experiences and perceived utility of the tool and readiness to adopt the tool at the point of care. The osteoporosis tool has the potential to make several contributions to the development and evaluation of complex, chronic disease interventions, such as the inclusion of an implementation strategy prior to conducting an evaluation study. Anticipated benefits of the tool may be to increase awareness for patients about osteoporosis and its associated risks and provide an opportunity to discuss a management plan with their physician, which may all facilitate patient self-management.

  1. Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support

    OpenAIRE

    Mohammadhassanzadeh, Hossein; Van Woensel, William; Abidi, Samina Raza; Abidi, Syed Sibte Raza

    2017-01-01

    Background Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians? experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mech...

  2. A clinical decision support system algorithm for intravenous to oral antibiotic switch therapy: validity, clinical relevance and usefulness in a three-step evaluation study.

    Science.gov (United States)

    Akhloufi, H; Hulscher, M; van der Hoeven, C P; Prins, J M; van der Sijs, H; Melles, D C; Verbon, A

    2018-04-26

    To evaluate a clinical decision support system (CDSS) based on consensus-based intravenous to oral switch criteria, which identifies intravenous to oral switch candidates. A three-step evaluation study of a stand-alone CDSS with electronic health record interoperability was performed at the Erasmus University Medical Centre in the Netherlands. During the first step, we performed a technical validation. During the second step, we determined the sensitivity, specificity, negative predictive value and positive predictive value in a retrospective cohort of all hospitalized adult patients starting at least one therapeutic antibacterial drug between 1 and 16 May 2013. ICU, paediatric and psychiatric wards were excluded. During the last step the clinical relevance and usefulness was prospectively assessed by reports to infectious disease specialists. An alert was considered clinically relevant if antibiotics could be discontinued or switched to oral therapy at the time of the alert. During the first step, one technical error was found. The second step yielded a positive predictive value of 76.6% and a negative predictive value of 99.1%. The third step showed that alerts were clinically relevant in 53.5% of patients. For 43.4% it had already been decided to discontinue or switch the intravenous antibiotics by the treating physician. In 10.1%, the alert resulted in advice to change antibiotic policy and was considered useful. This prospective cohort study shows that the alerts were clinically relevant in >50% (n = 449) and useful in 10% (n = 85). The CDSS needs to be evaluated in hospitals with varying activity of infectious disease consultancy services as this probably influences usefulness.

  3. A collaborative framework for contributing DICOM RT PHI (Protected Health Information) to augment data mining in clinical decision support

    Science.gov (United States)

    Deshpande, Ruchi; Thuptimdang, Wanwara; DeMarco, John; Liu, Brent J.

    2014-03-01

    We have built a decision support system that provides recommendations for customizing radiation therapy treatment plans, based on patient models generated from a database of retrospective planning data. This database consists of relevant metadata and information derived from the following DICOM objects - CT images, RT Structure Set, RT Dose and RT Plan. The usefulness and accuracy of such patient models partly depends on the sample size of the learning data set. Our current goal is to increase this sample size by expanding our decision support system into a collaborative framework to include contributions from multiple collaborators. Potential collaborators are often reluctant to upload even anonymized patient files to repositories outside their local organizational network in order to avoid any conflicts with HIPAA Privacy and Security Rules. We have circumvented this problem by developing a tool that can parse DICOM files on the client's side and extract de-identified numeric and text data from DICOM RT headers for uploading to a centralized system. As a result, the DICOM files containing PHI remain local to the client side. This is a novel workflow that results in adding only relevant yet valuable data from DICOM files to the centralized decision support knowledge base in such a way that the DICOM files never leave the contributor's local workstation in a cloud-based environment. Such a workflow serves to encourage clinicians to contribute data for research endeavors by ensuring protection of electronic patient data.

  4. Development of a real-time clinical decision support system upon the web mvc-based architecture for prostate cancer treatment

    Directory of Open Access Journals (Sweden)

    Liang Wen-Miin

    2011-03-01

    Full Text Available Abstract Background A real-time clinical decision support system (RTCDSS with interactive diagrams enables clinicians to instantly and efficiently track patients' clinical records (PCRs and improve their quality of clinical care. We propose a RTCDSS to process online clinical informatics from multiple databases for clinical decision making in the treatment of prostate cancer based on Web Model-View-Controller (MVC architecture, by which the system can easily be adapted to different diseases and applications. Methods We designed a framework upon the Web MVC-based architecture in which the reusable and extractable models can be conveniently adapted to other hospital information systems and which allows for efficient database integration. Then, we determined the clinical variables of the prostate cancer treatment based on participating clinicians' opinions and developed a computational model to determine the pretreatment parameters. Furthermore, the components of the RTCDSS integrated PCRs and decision factors for real-time analysis to provide evidence-based diagrams upon the clinician-oriented interface for visualization of treatment guidance and health risk assessment. Results The resulting system can improve quality of clinical treatment by allowing clinicians to concurrently analyze and evaluate the clinical markers of prostate cancer patients with instantaneous clinical data and evidence-based diagrams which can automatically identify pretreatment parameters. Moreover, the proposed RTCDSS can aid interactions between patients and clinicians. Conclusions Our proposed framework supports online clinical informatics, evaluates treatment risks, offers interactive guidance, and provides real-time reference for decision making in the treatment of prostate cancer. The developed clinician-oriented interface can assist clinicians in conveniently presenting evidence-based information to patients and can be readily adapted to an existing hospital

  5. Development of a real-time clinical decision support system upon the Web MVC-based architecture for prostate cancer treatment.

    Science.gov (United States)

    Lin, Hsueh-Chun; Wu, Hsi-Chin; Chang, Chih-Hung; Li, Tsai-Chung; Liang, Wen-Miin; Wang, Jong-Yi Wang

    2011-03-08

    A real-time clinical decision support system (RTCDSS) with interactive diagrams enables clinicians to instantly and efficiently track patients' clinical records (PCRs) and improve their quality of clinical care. We propose a RTCDSS to process online clinical informatics from multiple databases for clinical decision making in the treatment of prostate cancer based on Web Model-View-Controller (MVC) architecture, by which the system can easily be adapted to different diseases and applications. We designed a framework upon the Web MVC-based architecture in which the reusable and extractable models can be conveniently adapted to other hospital information systems and which allows for efficient database integration. Then, we determined the clinical variables of the prostate cancer treatment based on participating clinicians' opinions and developed a computational model to determine the pretreatment parameters. Furthermore, the components of the RTCDSS integrated PCRs and decision factors for real-time analysis to provide evidence-based diagrams upon the clinician-oriented interface for visualization of treatment guidance and health risk assessment. The resulting system can improve quality of clinical treatment by allowing clinicians to concurrently analyze and evaluate the clinical markers of prostate cancer patients with instantaneous clinical data and evidence-based diagrams which can automatically identify pretreatment parameters. Moreover, the proposed RTCDSS can aid interactions between patients and clinicians. Our proposed framework supports online clinical informatics, evaluates treatment risks, offers interactive guidance, and provides real-time reference for decision making in the treatment of prostate cancer. The developed clinician-oriented interface can assist clinicians in conveniently presenting evidence-based information to patients and can be readily adapted to an existing hospital information system and be easily applied in other chronic diseases.

  6. Informing the design of clinical decision support services for evaluation of children with minor blunt head trauma in the emergency department: a sociotechnical analysis.

    Science.gov (United States)

    Sheehan, Barbara; Nigrovic, Lise E; Dayan, Peter S; Kuppermann, Nathan; Ballard, Dustin W; Alessandrini, Evaline; Bajaj, Lalit; Goldberg, Howard; Hoffman, Jeffrey; Offerman, Steven R; Mark, Dustin G; Swietlik, Marguerite; Tham, Eric; Tzimenatos, Leah; Vinson, David R; Jones, Grant S; Bakken, Suzanne

    2013-10-01

    Integration of clinical decision support services (CDSS) into electronic health records (EHRs) may be integral to widespread dissemination and use of clinical prediction rules in the emergency department (ED). However, the best way to design such services to maximize their usefulness in such a complex setting is poorly understood. We conducted a multi-site cross-sectional qualitative study whose aim was to describe the sociotechnical environment in the ED to inform the design of a CDSS intervention to implement the Pediatric Emergency Care Applied Research Network (PECARN) clinical prediction rules for children with minor blunt head trauma. Informed by a sociotechnical model consisting of eight dimensions, we conducted focus groups, individual interviews and workflow observations in 11 EDs, of which 5 were located in academic medical centers and 6 were in community hospitals. A total of 126 ED clinicians, information technology specialists, and administrators participated. We clustered data into 19 categories of sociotechnical factors through a process of thematic analysis and subsequently organized the categories into a sociotechnical matrix consisting of three high-level sociotechnical dimensions (workflow and communication, organizational factors, human factors) and three themes (interdisciplinary assessment processes, clinical practices related to prediction rules, EHR as a decision support tool). Design challenges that emerged from the analysis included the need to use structured data fields to support data capture and re-use while maintaining efficient care processes, supporting interdisciplinary communication, and facilitating family-clinician interaction for decision-making. Copyright © 2013 Elsevier Inc. All rights reserved.

  7. Decision Strategy Research: Policy Support

    International Nuclear Information System (INIS)

    Hardeman, F.

    2000-01-01

    The objective of SCK-CEN's R and D programme on decision strategy research are (1) to support and advise the Belgian authorities on specific problems concerning existing and potential hazards from exposure to ionising radiation, both in normal and emergency situations; (2) to perform research on relevant topics that might have an important impact on decision making related to nuclear applications, including social and economic sciences. Main achievements in this area in 1999 are described

  8. Clinical impact of pharmacogenetic profiling with a clinical decision support tool in polypharmacy home health patients: A prospective pilot randomized controlled trial.

    Directory of Open Access Journals (Sweden)

    Lindsay S Elliott

    Full Text Available In polypharmacy patients under home health management, pharmacogenetic testing coupled with guidance from a clinical decision support tool (CDST on reducing drug, gene, and cumulative interaction risk may provide valuable insights in prescription drug treatment, reducing re-hospitalization and emergency department (ED visits. We assessed the clinical impact of pharmacogenetic profiling integrating binary and cumulative drug and gene interaction warnings on home health polypharmacy patients.This prospective, open-label, randomized controlled trial was conducted at one hospital-based home health agency between February 2015 and February 2016. Recruitment came from patient referrals to home health at hospital discharge. Eligible patients were aged 50 years and older and taking or initiating treatment with medications with potential or significant drug-gene-based interactions. Subjects (n = 110 were randomized to pharmacogenetic profiling (n = 57. The study pharmacist reviewed drug-drug, drug-gene, and cumulative drug and/or gene interactions using the YouScript® CDST to provide drug therapy recommendations to clinicians. The control group (n = 53 received treatment as usual including pharmacist guided medication management using a standard drug information resource. The primary outcome measure was the number of re-hospitalizations and ED visits at 30 and 60 days after discharge from the hospital. The mean number of re-hospitalizations per patient in the tested vs. untested group was 0.25 vs. 0.38 at 30 days (relative risk (RR, 0.65; 95% confidence interval (CI, 0.32-1.28; P = 0.21 and 0.33 vs. 0.70 at 60 days following enrollment (RR, 0.48; 95% CI, 0.27-0.82; P = 0.007. The mean number of ED visits per patient in the tested vs. untested group was 0.25 vs. 0.40 at 30 days (RR, 0.62; 95% CI, 0.31-1.21; P = 0.16 and 0.39 vs. 0.66 at 60 days (RR, 0.58; 95% CI, 0.34-0.99; P = 0.045. Differences in composite outcomes at 60 days (exploratory endpoints

  9. Decision support using nonparametric statistics

    CERN Document Server

    Beatty, Warren

    2018-01-01

    This concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective. While many degree programs require a course in parametric statistics, these methods are often inadequate for real-world decision making in business environments. Much of the data collected today by business executives (for example, customer satisfaction opinions) requires nonparametric statistics for valid analysis, and this book provides the reader with a set of tools that can be used to validly analyze all data, regardless of type. Through numerous examples and exercises, this book explains why nonparametric statistics will lead to better decisions and how they are used to reach a decision, with a wide array of business applications. Online resources include exercise data, spreadsheets, and solutions.

  10. The design and implementation of an Interactive Computerised Decision Support Framework (ICDSF) as a strategy to improve nursing students' clinical reasoning skills.

    Science.gov (United States)

    Hoffman, Kerry; Dempsey, Jennifer; Levett-Jones, Tracy; Noble, Danielle; Hickey, Noelene; Jeong, Sarah; Hunter, Sharyn; Norton, Carol

    2011-08-01

    This paper describes the conceptual design and testing of an Interactive Computerised Decision Support Framework (ICDSF) which was constructed to enable student nurses to "think like a nurse." The ICDSF was based on a model of clinical reasoning. Teaching student nurses to reason clinically is important as poor clinical reasoning skills can lead to "failure-to rescue" of deteriorating patients. The framework of the ICDSF was based on nursing concepts to encourage deep learning and transferability of knowledge. The principles of active student participation, situated cognition to solve problems, authenticity, and cognitive rehearsal were used to develop the ICDSF. The ICDSF was designed in such a way that students moved through it in a step-wise fashion and were required to achieve competency at each step before proceeding to the next. The quality of the ICDSF was evaluated using a questionairre survey, students' written comments and student assessment measures on a pilot and the ICDSF. Overall students were highly satisfied with the clinical scenarios of the ICDSF and believed they were an interesting and useful way to engage in authentic clinical learning. They also believed the ICDSF was useful in developing cognitive skills such as clinical reasoning, problem-solving and decision-making. Some reported issues were the need for good technical support and the lack of face to face contact when using e-learning. Some students also believed the ICDSF was less useful than actual clinical placements. Copyright © 2010 Elsevier Ltd. All rights reserved.

  11. Analysis of the process of representing clinical statements for decision-support applications: a comparison of openEHR archetypes and HL7 virtual medical record.

    Science.gov (United States)

    González-Ferrer, A; Peleg, M; Marcos, M; Maldonado, J A

    2016-07-01

    Delivering patient-specific decision-support based on computer-interpretable guidelines (CIGs) requires mapping CIG clinical statements (data items, clinical recommendations) into patients' data. This is most effectively done via intermediate data schemas, which enable querying the data according to the semantics of a shared standard intermediate schema. This study aims to evaluate the use of HL7 virtual medical record (vMR) and openEHR archetypes as intermediate schemas for capturing clinical statements from CIGs that are mappable to electronic health records (EHRs) containing patient data and patient-specific recommendations. Using qualitative research methods, we analyzed the encoding of ten representative clinical statements taken from two CIGs used in real decision-support systems into two health information models (openEHR archetypes and HL7 vMR instances) by four experienced informaticians. Discussion among the modelers about each case study example greatly increased our understanding of the capabilities of these standards, which we share in this educational paper. Differing in content and structure, the openEHR archetypes were found to contain a greater level of representational detail and structure while the vMR representations took fewer steps to complete. The use of openEHR in the encoding of CIG clinical statements could potentially facilitate applications other than decision-support, including intelligent data analysis and integration of additional properties of data items from existing EHRs. On the other hand, due to their smaller size and fewer details, the use of vMR potentially supports quicker mapping of EHR data into clinical statements.

  12. Workflow barriers out of hours: optimising critical care outreach to support clinical decision making in medical and surgical care settings

    OpenAIRE

    Brady, Anne-Marie; Ennis, Shauna; Prendergast, Maebh; Quirke, Mary; Bhangu, Jas; Lynch, Aine; Byrne, Gobnait

    2017-01-01

    Introduction: The out-of-hours period is associated with less favourable patient health outcomes as well as unpredictable workloads and reduced support structures for clinical activity. In particular, appropriate skill mix, staff numbers, resources, communication structures and access to diagnostic services can influence patient safety and risk. As part of continued efforts to improve patient care and hospital management, one major academic hospital is in Ireland has been engaged in work re-d...

  13. A scalable architecture for incremental specification and maintenance of procedural and declarative clinical decision-support knowledge.

    Science.gov (United States)

    Hatsek, Avner; Shahar, Yuval; Taieb-Maimon, Meirav; Shalom, Erez; Klimov, Denis; Lunenfeld, Eitan

    2010-01-01

    Clinical guidelines have been shown to improve the quality of medical care and to reduce its costs. However, most guidelines exist in a free-text representation and, without automation, are not sufficiently accessible to clinicians at the point of care. A prerequisite for automated guideline application is a machine-comprehensible representation of the guidelines. In this study, we designed and implemented a scalable architecture to support medical experts and knowledge engineers in specifying and maintaining the procedural and declarative aspects of clinical guideline knowledge, resulting in a machine comprehensible representation. The new framework significantly extends our previous work on the Digital electronic Guidelines Library (DeGeL) The current study designed and implemented a graphical framework for specification of declarative and procedural clinical knowledge, Gesher. We performed three different experiments to evaluate the functionality and usability of the major aspects of the new framework: Specification of procedural clinical knowledge, specification of declarative clinical knowledge, and exploration of a given clinical guideline. The subjects included clinicians and knowledge engineers (overall, 27 participants). The evaluations indicated high levels of completeness and correctness of the guideline specification process by both the clinicians and the knowledge engineers, although the best results, in the case of declarative-knowledge specification, were achieved by teams including a clinician and a knowledge engineer. The usability scores were high as well, although the clinicians' assessment was significantly lower than the assessment of the knowledge engineers.

  14. Architecture Design of Healthcare Software-as-a-Service Platform for Cloud-Based Clinical Decision Support Service.

    Science.gov (United States)

    Oh, Sungyoung; Cha, Jieun; Ji, Myungkyu; Kang, Hyekyung; Kim, Seok; Heo, Eunyoung; Han, Jong Soo; Kang, Hyunggoo; Chae, Hoseok; Hwang, Hee; Yoo, Sooyoung

    2015-04-01

    To design a cloud computing-based Healthcare Software-as-a-Service (SaaS) Platform (HSP) for delivering healthcare information services with low cost, high clinical value, and high usability. We analyzed the architecture requirements of an HSP, including the interface, business services, cloud SaaS, quality attributes, privacy and security, and multi-lingual capacity. For cloud-based SaaS services, we focused on Clinical Decision Service (CDS) content services, basic functional services, and mobile services. Microsoft's Azure cloud computing for Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) was used. The functional and software views of an HSP were designed in a layered architecture. External systems can be interfaced with the HSP using SOAP and REST/JSON. The multi-tenancy model of the HSP was designed as a shared database, with a separate schema for each tenant through a single application, although healthcare data can be physically located on a cloud or in a hospital, depending on regulations. The CDS services were categorized into rule-based services for medications, alert registration services, and knowledge services. We expect that cloud-based HSPs will allow small and mid-sized hospitals, in addition to large-sized hospitals, to adopt information infrastructures and health information technology with low system operation and maintenance costs.

  15. Architecture Design of Healthcare Software-as-a-Service Platform for Cloud-Based Clinical Decision Support Service

    Science.gov (United States)

    Oh, Sungyoung; Cha, Jieun; Ji, Myungkyu; Kang, Hyekyung; Kim, Seok; Heo, Eunyoung; Han, Jong Soo; Kang, Hyunggoo; Chae, Hoseok; Hwang, Hee

    2015-01-01

    Objectives To design a cloud computing-based Healthcare Software-as-a-Service (SaaS) Platform (HSP) for delivering healthcare information services with low cost, high clinical value, and high usability. Methods We analyzed the architecture requirements of an HSP, including the interface, business services, cloud SaaS, quality attributes, privacy and security, and multi-lingual capacity. For cloud-based SaaS services, we focused on Clinical Decision Service (CDS) content services, basic functional services, and mobile services. Microsoft's Azure cloud computing for Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) was used. Results The functional and software views of an HSP were designed in a layered architecture. External systems can be interfaced with the HSP using SOAP and REST/JSON. The multi-tenancy model of the HSP was designed as a shared database, with a separate schema for each tenant through a single application, although healthcare data can be physically located on a cloud or in a hospital, depending on regulations. The CDS services were categorized into rule-based services for medications, alert registration services, and knowledge services. Conclusions We expect that cloud-based HSPs will allow small and mid-sized hospitals, in addition to large-sized hospitals, to adopt information infrastructures and health information technology with low system operation and maintenance costs. PMID:25995962

  16. Supporting shared decision making using an Option Grid for osteoarthritis of the knee in an interface musculoskeletal clinic: A stepped wedge trial.

    Science.gov (United States)

    Elwyn, Glyn; Pickles, Tim; Edwards, Adrian; Kinsey, Katharine; Brain, Kate; Newcombe, Robert G; Firth, Jill; Marrin, Katy; Nye, Alan; Wood, Fiona

    2016-04-01

    To evaluate whether introducing tools, specifically designed for use in clinical encounters, namely Option Grids, into a clinical practice setting leads to higher levels of shared decision making. A stepped wedge trial design where 6 physiotherapists at an interface clinic in Oldham, UK, were sequentially instructed in how to use an Option Grid for osteoarthritis of the knee. Patients with suspected or confirmed osteoarthritis of the knee were recruited, six per clinician prior to instruction, and six per clinician afterwards. We measured shared decision making, patient knowledge, and readiness to decide. A total of 72 patients were recruited; 36 were allocated to the intervention group. There was an 8.4 point (95% CI 4.4 to 12.2) increase in the Observer OPTION score (range 0-100) in the intervention group. The mean gain in knowledge was 0.9 points (score range 0-5, 95% CI, 0.3 to 1.5). There was no increase in encounter duration. Shared decision making increased when clinicians used the knee osteoarthritis Option Grid. Tools designed to support collaboration and deliberation about treatment options lead to increased levels of shared decision making. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  17. A Clinical Decision Support System Using Ultrasound Textures and Radiologic Features to Distinguish Metastasis From Tumor-Free Cervical Lymph Nodes in Patients With Papillary Thyroid Carcinoma.

    Science.gov (United States)

    Abbasian Ardakani, Ali; Reiazi, Reza; Mohammadi, Afshin

    2018-03-30

    This study investigated the potential of a clinical decision support approach for the classification of metastatic and tumor-free cervical lymph nodes (LNs) in papillary thyroid carcinoma on the basis of radiologic and textural analysis through ultrasound (US) imaging. In this research, 170 metastatic and 170 tumor-free LNs were examined by the proposed clinical decision support method. To discover the difference between the groups, US imaging was used for the extraction of radiologic and textural features. The radiologic features in the B-mode scans included the echogenicity, margin, shape, and presence of microcalcification. To extract the textural features, a wavelet transform was applied. A support vector machine classifier was used to classify the LNs. In the training set data, a combination of radiologic and textural features represented the best performance with sensitivity, specificity, accuracy, and area under the curve (AUC) values of 97.14%, 98.57%, 97.86%, and 0.994, respectively, whereas the classification based on radiologic and textural features alone yielded lower performance, with AUCs of 0.964 and 0.922. On testing the data set, the proposed model could classify the tumor-free and metastatic LNs with an AUC of 0.952, which corresponded to sensitivity, specificity, and accuracy of 93.33%, 96.66%, and 95.00%. The clinical decision support method based on textural and radiologic features has the potential to characterize LNs via 2-dimensional US. Therefore, it can be used as a supplementary technique in daily clinical practice to improve radiologists' understanding of conventional US imaging for characterizing LNs. © 2018 by the American Institute of Ultrasound in Medicine.

  18. Clinical Usefulness of Tools to Support Decision-making for Palliative Treatment of Metastatic Colorectal Cancer: A Systematic Review

    NARCIS (Netherlands)

    Engelhardt, Ellen G.; Révész, Dóra; Tamminga, Hans J.; Punt, Cornelis J. A.; Koopman, Mirjam; Onwuteaka-Philipsen, Bregje D.; Steyerberg, Ewout W.; Jansma, Ilse P.; de Vet, Henrica C. W.; Coupé, Veerle M. H.

    2018-01-01

    Decision-making regarding palliative treatment for patients with metastatic colorectal cancer (mCRC) is complex and comprises numerous decisions. Decision-making should be guided by the premise of maintaining and/or improving patients' quality of life, by patient preference, and by the trade-off

  19. Implications of Decision Making Research for Decision Support and Displays

    OpenAIRE

    Morrison, Jeffrey G.; Kelly, Richard T.; Moore, Ronald A.; Hutchins, Susan G.

    1998-01-01

    To appear in J. A. Cannon-Bowers & E. Salas (Eds.), Decision Making Under Stress: Implications for Training and Simulation. A prototype decision support system (DSS) was developed to enhance Navy tactical decision making based on naturalistic decision processes. Displays were developed to support critical decision making tasks through recognition-primed and explanation-based reasoning processes, and cognitive analysis was conducted of the decision making problems faced by Navy ...

  20. Scalable software architectures for decision support.

    Science.gov (United States)

    Musen, M A

    1999-12-01

    Interest in decision-support programs for clinical medicine soared in the 1970s. Since that time, workers in medical informatics have been particularly attracted to rule-based systems as a means of providing clinical decision support. Although developers have built many successful applications using production rules, they also have discovered that creation and maintenance of large rule bases is quite problematic. In the 1980s, several groups of investigators began to explore alternative programming abstractions that can be used to build decision-support systems. As a result, the notions of "generic tasks" and of reusable problem-solving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) problem-solving methods--domain-independent algorithms for automating stereotypical tasks--and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper highlights how developers can construct large, maintainable decision-support systems using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community.

  1. Checklist and Decision Support in Nutritional Care for Burned Patients

    Science.gov (United States)

    2016-10-01

    able to construct a checklist of a clinical and physiologic model and then a computerised decision support system that will perform two functions: the...the provision of nutritional therapy, and assessment of use by nursing and physician staff KEYWORDS Nutrition, severe burn, decision support... clinical testing. Checklist and Decision Support in Nutritional Care for Burned Patients Proposal Number: 12340011 W81XWH-12-2-0074 PI: Steven E

  2. Developing public health clinical decision support systems (CDSS for the outpatient community in New York City: our experience

    Directory of Open Access Journals (Sweden)

    Singer Jesse

    2011-09-01

    Full Text Available Abstract Background Developing a clinically relevant set of quality measures that can be effectively used by an electronic health record (EHR is difficult. Whether it is achieving internal consensus on relevant priority quality measures, communicating to EHR vendors' whose programmers generally lack clinical contextual knowledge, or encouraging implementation of EHR that meaningfully impacts health outcomes, the path is challenging. However, greater transparency of population health, better accountability, and ultimately improved health outcomes is the goal and EHRs afford us a realistic chance of reaching it in a scalable way. Method In this article, we summarize our experience as a public health government agency with developing measures for a public health oriented EHR in New York City in partnership with a commercial EHR vendor. Results From our experience, there are six key lessons that we share in this article that we believe will dramatically increase the chance of success. First, define the scope and build consensus. Second, get support from executive leadership. Third, find an enthusiastic and competent software partner. Fourth, implement a transparent operational strategy. Fifth, create and test the EHR system with real life scenarios. Last, seek help when you need it. Conclusions Despite the challenges, we encourage public health agencies looking to build a similarly focused public health EHR to create one both for improved individual patient as well as the larger population health.

  3. Telepsychiatry clinical decision support system used by non-psychiatrists in remote areas: Validity & reliability of diagnostic module

    Science.gov (United States)

    Malhotra, Savita; Chakrabarti, Subho; Shah, Ruchita; Sharma, Minali; Sharma, Kanu Priya; Malhotra, Akanksha; Upadhyaya, Suneet K.; Margoob, Mushtaq A.; Maqbool, Dar; Jassal, Gopal D.

    2017-01-01

    Background & objectives: A knowledge-based, logically-linked online telepsychiatric decision support system for diagnosis and treatment of mental disorders was developed and validated. We evaluated diagnostic accuracy and reliability of the application at remote sites when used by non-psychiatrists who underwent a brief training in its use through video-conferencing. Methods: The study was conducted at a nodal telepsychiatry centre, and three geographically remote peripheral centres. The diagnostic tool of application had a screening followed by detailed criteria-wise diagnostic modules for 18 psychiatric disorders. A total of 100 consecutive consenting adult outpatients attending remote telepsychiatry centres were included. To assess inter-rater reliability, patients were interviewed face to face by non-specialists at remote sites using the application (active interviewer) and simultaneously on online application via video-conferencing by a passive assessor at nodal centre. Another interviewer at the nodal centre rated the patient using Mini-International Neuropsychiatric Interview (MINI) for diagnostic validation. Results: Screening sub-module had high sensitivity (80-100%), low positive predictive values (PPV) (0.10-0.71) but high negative predictive value (NPV) (0.97-1) for most disorders. For the diagnostic sub-modules, Cohen's kappa was >0.4 for all disorders, with kappa of 0.7-1.0 for most disorders. PPV and NPV were high for most disorders. Inter-rater agreement analysis revealed kappa >0.6 for all disorders. Interpretation & conclusions: Diagnostic tool showed acceptable to good validity and reliability when used by non-specialists at remote sites. Our findings show that diagnostic tool of the telepsychiatry application has potential to empower non-psychiatrist doctors and paramedics to diagnose psychiatric disorders accurately and reliably in remote sites. PMID:29265020

  4. Real-time use of the iPad by third-year medical students for clinical decision support and learning: a mixed methods study

    Science.gov (United States)

    Nuss, Michelle A.; Hill, Janette R.; Cervero, Ronald M.; Gaines, Julie K.; Middendorf, Bruce F.

    2014-01-01

    Purpose Despite widespread use of mobile technology in medical education, medical students’ use of mobile technology for clinical decision support and learning is not well understood. Three key questions were explored in this extensive mixed methods study: 1) how medical students used mobile technology in the care of patients, 2) the mobile applications (apps) used and 3) how expertise and time spent changed overtime. Methods This year-long (July 2012–June 2013) mixed methods study explored the use of the iPad, using four data collection instruments: 1) beginning and end-of-year questionnaires, 2) iPad usage logs, 3) weekly rounding observations, and 4) weekly medical student interviews. Descriptive statistics were generated for the questionnaires and apps reported in the usage logs. The iPad usage logs, observation logs, and weekly interviews were analyzed via inductive thematic analysis. Results Students predominantly used mobile technology to obtain real-time patient data via the electronic health record (EHR), to access medical knowledge resources for learning, and to inform patient care. The top four apps used were Epocrates®, PDF Expert®, VisualDx®, and Micromedex®. The majority of students indicated that their use (71%) and expertise (75%) using mobile technology grew overtime. Conclusions This mixed methods study provides substantial evidence that medical students used mobile technology for clinical decision support and learning. Integrating its use into the medical student's daily workflow was essential for achieving these outcomes. Developing expertise in using mobile technology and various apps was critical for effective and efficient support of real-time clinical decisions. PMID:25317266

  5. Real-time use of the iPad by third-year medical students for clinical decision support and learning: a mixed methods study.

    Science.gov (United States)

    Nuss, Michelle A; Hill, Janette R; Cervero, Ronald M; Gaines, Julie K; Middendorf, Bruce F

    2014-01-01

    Despite widespread use of mobile technology in medical education, medical students' use of mobile technology for clinical decision support and learning is not well understood. Three key questions were explored in this extensive mixed methods study: 1) how medical students used mobile technology in the care of patients, 2) the mobile applications (apps) used and 3) how expertise and time spent changed overtime. This year-long (July 2012-June 2013) mixed methods study explored the use of the iPad, using four data collection instruments: 1) beginning and end-of-year questionnaires, 2) iPad usage logs, 3) weekly rounding observations, and 4) weekly medical student interviews. Descriptive statistics were generated for the questionnaires and apps reported in the usage logs. The iPad usage logs, observation logs, and weekly interviews were analyzed via inductive thematic analysis. Students predominantly used mobile technology to obtain real-time patient data via the electronic health record (EHR), to access medical knowledge resources for learning, and to inform patient care. The top four apps used were Epocrates(®), PDF Expert(®), VisualDx(®), and Micromedex(®). The majority of students indicated that their use (71%) and expertise (75%) using mobile technology grew overtime. This mixed methods study provides substantial evidence that medical students used mobile technology for clinical decision support and learning. Integrating its use into the medical student's daily workflow was essential for achieving these outcomes. Developing expertise in using mobile technology and various apps was critical for effective and efficient support of real-time clinical decisions.

  6. INTELLIGENT DECISION SUPPORT ON FOREX

    Directory of Open Access Journals (Sweden)

    V. A. Rybak

    2014-01-01

    Full Text Available A new technology of intelligent decision support on Forex, including forming algorithms of trading signals, rules for the training sample based on technical indicators, which have the highest correlation with the price, the method of reducing the number of losing trades, is proposed. The last is based on an analysis of the wave structure of the market, while the beginning of the cycle (the wave number one is offered to be identified using Bill Williams Oscillator (Awesome oscillator. The process chain of constructing neuro-fuzzy model using software package MatLab is described.

  7. Future of electronic health records: implications for decision support.

    Science.gov (United States)

    Rothman, Brian; Leonard, Joan C; Vigoda, Michael M

    2012-01-01

    The potential benefits of the electronic health record over traditional paper are many, including cost containment, reductions in errors, and improved compliance by utilizing real-time data. The highest functional level of the electronic health record (EHR) is clinical decision support (CDS) and process automation, which are expected to enhance patient health and healthcare. The authors provide an overview of the progress in using patient data more efficiently and effectively through clinical decision support to improve health care delivery, how decision support impacts anesthesia practice, and how some are leading the way using these systems to solve need-specific issues. Clinical decision support uses passive or active decision support to modify clinician behavior through recommendations of specific actions. Recommendations may reduce medication errors, which would result in considerable savings by avoiding adverse drug events. In selected studies, clinical decision support has been shown to decrease the time to follow-up actions, and prediction has proved useful in forecasting patient outcomes, avoiding costs, and correctly prompting treatment plan modifications by clinicians before engaging in decision-making. Clinical documentation accuracy and completeness is improved by an electronic health record and greater relevance of care data is delivered. Clinical decision support may increase clinician adherence to clinical guidelines, but educational workshops may be equally effective. Unintentional consequences of clinical decision support, such as alert desensitization, can decrease the effectiveness of a system. Current anesthesia clinical decision support use includes antibiotic administration timing, improved documentation, more timely billing, and postoperative nausea and vomiting prophylaxis. Electronic health record implementation offers data-mining opportunities to improve operational, financial, and clinical processes. Using electronic health record data

  8. Tactical decision making under stress (TADMUS) decision support system

    OpenAIRE

    Morrison, Jeffrey G.; Kelly, Richard T.; Moore, Ronald A.; Hutchins, Susan G.

    1996-01-01

    A prototype decision support system (DSS) was developed to enhance Navy tactical decision making based on naturalistic decision processes. Displays were developed to support critical decision making tasks through recognition-primed and explanation-based reasoning processes and cognitive analysis of the decision making problems faced by Navy tactical officers in a shipboard Combat Information Center. Baseline testing in high intensity, peace keeping, littoral scenarios indicated...

  9. Using the Situated Clinical Decision-Making framework to guide analysis of nurses' clinical decision-making.

    Science.gov (United States)

    Gillespie, Mary

    2010-11-01

    Nurses' clinical decision-making is a complex process that holds potential to influence the quality of care provided and patient outcomes. The evolution of nurses' decision-making that occurs with experience has been well documented. In addition, literature includes numerous strategies and approaches purported to support development of nurses' clinical decision-making. There has been, however, significantly less attention given to the process of assessing nurses' clinical decision-making and novice clinical educators are often challenged with knowing how to best support nurses and nursing students in developing their clinical decision-making capacity. The Situated Clinical Decision-Making framework is presented for use by clinical educators: it provides a structured approach to analyzing nursing students' and novice nurses' decision-making in clinical nursing practice, assists educators in identifying specific issues within nurses' clinical decision-making, and guides selection of relevant strategies to support development of clinical decision-making. A series of questions is offered as a guide for clinical educators when assessing nurses' clinical decision-making. The discussion presents key considerations related to analysis of various decision-making components, including common sources of challenge and errors that may occur within nurses' clinical decision-making. An exemplar illustrates use of the framework and guiding questions. Implications of this approach for selection of strategies that support development of clinical decision-making are highlighted. Copyright © 2010 Elsevier Ltd. All rights reserved.

  10. Barriers to implementation of a computerized decision support system for depression: an observational report on lessons learned in "real world" clinical settings

    Directory of Open Access Journals (Sweden)

    Sunderajan Prabha

    2009-01-01

    Full Text Available Abstract Background Despite wide promotion, clinical practice guidelines have had limited effect in changing physician behavior. Effective implementation strategies to date have included: multifaceted interventions involving audit and feedback, local consensus processes, marketing; reminder systems, either manual or computerized; and interactive educational meetings. In addition, there is now growing evidence that contextual factors affecting implementation must be addressed such as organizational support (leadership procedures and resources for the change and strategies to implement and maintain new systems. Methods To examine the feasibility and effectiveness of implementation of a computerized decision support system for depression (CDSS-D in routine public mental health care in Texas, fifteen study clinicians (thirteen physicians and two advanced nurse practitioners participated across five sites, accruing over 300 outpatient visits on 168 patients. Results Issues regarding computer literacy and hardware/software requirements were identified as initial barriers. Clinicians also reported concerns about negative impact on workflow and the potential need for duplication during the transition from paper to electronic systems of medical record keeping. Conclusion The following narrative report based on observations obtained during the initial testing and use of a CDSS-D in clinical settings further emphasizes the importance of taking into account organizational factors when planning implementation of evidence-based guidelines or decision support within a system.

  11. Toward a More Robust and Efficient Usability Testing Method of Clinical Decision Support for Nurses Derived From Nursing Electronic Health Record Data.

    Science.gov (United States)

    Lopez, Karen Dunn; Febretti, Alessandro; Stifter, Janet; Johnson, Andrew; Wilkie, Diana J; Keenan, Gail

    2017-10-01

    To develop methods for rapid and simultaneous design, testing, and management of multiple clinical decision support (CDS) features to aid nurse decision-making. We used quota sampling, think-aloud and cognitive interviews, and deductive and inductive coding of synchronized audio video data and archival libraries. Our methods and organizational tools allowed us to rapidly improve the usability, understandability, and usefulness of CDS in a generalizable sample of practicing nurses. The method outlined allows the rapid integration of nursing terminology based electronic health record data into routine workflow and holds strong potential for improving patient outcomes. The methods and organizational tools for development of multiple CDS system features can be used to translate knowledge into practice. © 2016 NANDA International, Inc.

  12. Development of EBM-CDSS (Evidence-Based Clinical Decision Support System) to AIG Prognostication in Terminally Ill Patients

    Science.gov (United States)

    2016-03-01

    indicates that, when making their choices, the people tend to be regret averse: they anticipate regret to avoid post-decisional regret . In the...visual analogue scales for elicitation of regret , elicitation of acceptable regret , incorporation of treatment effects in the decision making...calculations. The details of the CDSS-EBM are published in a peer-reviewed journal manuscript (See appendix: Extensions to Regret -based Decision Curve Analysis

  13. Decision support for patient care: implementing cybernetics.

    Science.gov (United States)

    Ozbolt, Judy; Ozdas, Asli; Waitman, Lemuel R; Smith, Janis B; Brennan, Grace V; Miller, Randolph A

    2004-01-01

    The application of principles and methods of cybernetics permits clinicians and managers to use feedback about care effectiveness and resource expenditure to improve quality and to control costs. Keys to the process are the specification of therapeutic goals and the creation of an organizational culture that supports the use of feedback to improve care. Daily feedback on the achievement of each patient's therapeutic goals provides tactical decision support, enabling clinicians to adjust care as needed. Monthly or quarterly feedback on aggregated goal achievement for all patients on a clinical pathway provides strategic decision support, enabling clinicians and managers to identify problems with supposed "best practices" and to test hypotheses about solutions. Work is underway at Vanderbilt University Medical Center to implement feedback loops in care and management processes and to evaluate the effects.

  14. "Quality of prenatal and maternal care: bridging the know-do gap" (QUALMAT study): an electronic clinical decision support system for rural Sub-Saharan Africa.

    Science.gov (United States)

    Blank, Antje; Prytherch, Helen; Kaltschmidt, Jens; Krings, Andreas; Sukums, Felix; Mensah, Nathan; Zakane, Alphonse; Loukanova, Svetla; Gustafsson, Lars L; Sauerborn, Rainer; Haefeli, Walter E

    2013-04-10

    Despite strong efforts to improve maternal care, its quality remains deficient in many countries of Sub-Saharan Africa as persistently high maternal mortality rates testify. The QUALMAT study seeks to improve the performance and motivation of rural health workers and ultimately quality of primary maternal health care services in three African countries Burkina Faso, Ghana, and Tanzania. One major intervention is the introduction of a computerized Clinical Decision Support System (CDSS) for rural primary health care centers to be used by health care workers of different educational levels. A stand-alone, java-based software, able to run on any standard hardware, was developed based on assessment of the health care situation in the involved countries. The software scope was defined and the final software was programmed under consideration of test experiences. Knowledge for the decision support derived from the World Health Organization (WHO) guideline "Pregnancy, Childbirth, Postpartum and Newborn Care; A Guide for Essential Practice". The QUALMAT CDSS provides computerized guidance and clinical decision support for antenatal care, and care during delivery and up to 24 hours post delivery. The decision support is based on WHO guidelines and designed using three principles: (1) Guidance through routine actions in maternal and perinatal care, (2) integration of clinical data to detect situations of concern by algorithms, and (3) electronic tracking of peri- and postnatal activities. In addition, the tool facilitates patient management and is a source of training material. The implementation of the software, which is embedded in a set of interventions comprising the QUALMAT study, is subject to various research projects assessing and quantifying the impact of the CDSS on quality of care, the motivation of health care staff (users) and its health economic aspects. The software will also be assessed for its usability and acceptance, as well as for its influence on

  15. Advanced decision support for winter road maintenance

    Science.gov (United States)

    2008-01-01

    This document provides an overview of the Federal Highway Administration's winter Maintenance Decision Support System (MDSS). The MDSS is a decision support tool that has the ability to provide weather predictions focused toward the road surface. The...

  16. Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN): evolution of a content management system for point-of-care clinical decision support.

    Science.gov (United States)

    Barwise, Amelia; Garcia-Arguello, Lisbeth; Dong, Yue; Hulyalkar, Manasi; Vukoja, Marija; Schultz, Marcus J; Adhikari, Neill K J; Bonneton, Benjamin; Kilickaya, Oguz; Kashyap, Rahul; Gajic, Ognjen; Schmickl, Christopher N

    2016-10-03

    The Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN) is an international collaborative project with the overall objective of standardizing the approach to the evaluation and treatment of critically ill patients world-wide, in accordance with best-practice principles. One of CERTAIN's key features is clinical decision support providing point-of-care information about common acute illness syndromes, procedures, and medications in an index card format. This paper describes 1) the process of developing and validating the content for point-of-care decision support, and 2) the content management system that facilitates frequent peer-review and allows rapid updates of content across different platforms (CERTAIN software, mobile apps, pdf-booklet) and different languages. Content was created based on survey results of acute care providers and validated using an open peer-review process. Over a 3 year period, CERTAIN content expanded to include 67 syndrome cards, 30 procedure cards, and 117 medication cards. 127 (59 %) cards have been peer-reviewed so far. Initially MS Word® and Dropbox® were used to create, store, and share content for peer-review. Recently Google Docs® was used to make the peer-review process more efficient. However, neither of these approaches met our security requirements nor has the capacity to instantly update the different CERTAIN platforms. Although we were able to successfully develop and validate a large inventory of clinical decision support cards in a short period of time, commercially available software solutions for content management are suboptimal. Novel custom solutions are necessary for efficient global point of care content system management.

  17. General practitioners' attitudes and preparedness towards Clinical Decision Support in e-Prescribing (CDS-eP adoption in the West of Ireland: a cross sectional study

    Directory of Open Access Journals (Sweden)

    O'Brien Timothy

    2010-01-01

    Full Text Available Abstract Background Electronic clinical decision support (CDS is increasingly establishing its role in evidence-based clinical practice. Considerable evidence supports its enhancement of efficiency in e-Prescribing, but some controversy remains. This study evaluated the practicality and identified the perceived benefits of, and barriers to, its future adoption in the West of Ireland. Methods This cross sectional study was carried out by means of a 27-part questionnaire sent to 262 registered general practitioners in Counties Galway, Mayo and Roscommon. The survey domains encompassed general information of individual's practice, current use of CDS and the practitioner's attitudes towards adoption of CDS-eP. Descriptive and inferential analyses were performed to analyse the data collected. Results The overall response rate was 37%. Nearly 92% of respondents employed electronic medical records in their practice. The majority acknowledged the value of electronic CDS in improving prescribing quality (71% and reducing prescribing errors (84%. Despite a high degree of unfamiliarity (73%, the practitioners were open to the use of CDS-eP (94% and willing to invest greater resources for its implementation (62%. Lack of a strategic implementation plan (78% is the main perceived barrier to the incorporation of CDS-eP into clinical practice, followed by i lack of financial incentives (70%, ii lack of standardized product software (61%, iii high sensitivity of drug-drug interaction or medication allergy markers (46%, iv concern about overriding physicians' prescribing decisions(44% and v lack of convincing evidence on the systems' effectiveness (22%. Conclusions Despite favourable attitudes towards the adoption of CDS-eP, multiple perceived barriers impede its incorporation into clinical practice. These merit further exploration, taking into consideration the structure of the Irish primary health care system, before CDS-eP can be recommended for routine

  18. Visual Decision Support Tool for Supporting Asset ...

    Science.gov (United States)

    Abstract:Managing urban water infrastructures faces the challenge of jointly dealing with assets of diverse types, useful life, cost, ages and condition. Service quality and sustainability require sound long-term planning, well aligned with tactical and operational planning and management. In summary, the objective of an integrated approach to infrastructure asset management is to assist utilities answer the following questions:•Who are we at present?•What service do we deliver?•What do we own?•Where do we want to be in the long-term?•How do we get there?The AWARE-P approach (www.aware-p.org) offers a coherent methodological framework and a valuable portfolio of software tools. It is designed to assist water supply and wastewater utility decision-makers in their analyses and planning processes. It is based on a Plan-Do-Check-Act process and is in accordance with the key principles of the International Standards Organization (ISO) 55000 standards on asset management. It is compatible with, and complementary to WERF’s SIMPLE framework. The software assists in strategic, tactical, and operational planning, through a non-intrusive, web-based, collaborative environment where objectives and metrics drive IAM planning. It is aimed at industry professionals and managers, as well as at the consultants and technical experts that support them. It is easy to use and maximizes the value of information from multiple existing data sources, both in da

  19. Clinical decision support systems for primary care: the identification of promising application areas and an initial design of a CDSS for lower back pain

    NARCIS (Netherlands)

    Oude Nijeweme-d'Hollosy, W.; Velsen, L. van; Swinkels, I.C.S.; Hermens, H.

    2015-01-01

    Decision support technology has the potential to change the way professionals treat patients for the better. We questioned thirty-three healthcare professionals on their view about the usage of eHealth technology within their daily practice, and areas in which decision support can play a role, to

  20. A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial.

    Science.gov (United States)

    Beeckman, Dimitri; Clays, Els; Van Hecke, Ann; Vanderwee, Katrien; Schoonhoven, Lisette; Verhaeghe, Sofie

    2013-04-01

    Frail older people admitted to nursing homes are at risk of a range of adverse outcomes, including pressure ulcers. Clinical decision support systems are believed to have the potential to improve care and to change the behaviour of healthcare professionals. To determine whether a multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention improves adherence to recommendations for pressure ulcer prevention in nursing homes. Two-armed randomized controlled trial in a nursing home setting in Belgium. The trial consisted of a 16-week implementation intervention between February and June 2010, including one baseline, four intermediate, and one post-testing measurement. Primary outcome was the adherence to guideline-based care recommendations (in terms of allocating adequate pressure ulcer prevention in residents at risk). Secondary outcomes were the change in resident outcomes (pressure ulcer prevalence) and intermediate outcomes (knowledge and attitudes of healthcare professionals). Random sample of 11 wards (6 experimental; 5 control) in a convenience sample of 4 nursing homes in Belgium. In total, 464 nursing home residents and 118 healthcare professionals participated. The experimental arm was involved in a multi-faceted tailored implementation intervention of a clinical decision support system, including interactive education, reminders, monitoring, feedback and leadership. The control arm received a hard-copy of the pressure ulcer prevention protocol, supported by standardized 30 min group lecture. Patients in the intervention arm were significantly more likely to receive fully adequate pressure ulcer prevention when seated in a chair (F=16.4, P=0.003). No significant improvement was observed on pressure ulcer prevalence and knowledge of the professionals. While baseline attitude scores were comparable between both groups [exp. 74.3% vs. contr. 74.5% (P=0.92)], the mean score after the intervention was

  1. Working at the intersection of context, culture, and technology: Provider perspectives on antimicrobial stewardship in the emergency department using electronic health record clinical decision support.

    Science.gov (United States)

    Chung, Phillip; Scandlyn, Jean; Dayan, Peter S; Mistry, Rakesh D

    2017-11-01

    Antibiotic stewardship programs (ASPs) have not been fully developed for the emergency department (ED), in part the result of the barriers characteristic of this setting. Electronic health record-based clinical decision support (EHR CDS) represents a promising strategy to implement ASPs in the ED. We aimed to determine the cultural beliefs and structural barriers and facilitators to implementation of antimicrobial stewardship in the pediatric ED using EHR CDS. Interviews and focus groups were conducted with hospital and ED leadership, attending ED physicians, nurse practitioners, physician assistants, and residents at a single health system in Colorado. We reviewed and coded the data using constant comparative analysis and framework analysis until a final set of themes emerged. Two dominant perceptions shaped providers' perspectives on ASPs in the ED and EHR CDS: (1) maintaining workflow efficiency and (2) constrained decision-making autonomy. Clinicians identified structural barriers to ASPs, such as pace of the ED, and various beliefs that shaped patterns of practice, including accommodating the prescribing decisions of other providers and managing parental expectations. Recommendations to enhance uptake focused on designing a simple yet flexible user interface, providing clinicians with performance data, and on-boarding clinicians to enhance buy-in. Developing a successful ED-based ASP using EHR CDS should attend to technologic needs, the institutional context, and the cultural beliefs of practice associated with providers' antibiotic prescribing. Copyright © 2017 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

  2. Decision support, analytics, and business intelligence

    CERN Document Server

    Power, Daniel J

    2013-01-01

    Competition is becoming more intense and decision makers are encountering increasing complexity, rapid change, and higher levels of risk. In many situations, the solution is more and better computerized decision support, especially analytics and business intelligence. Today managers need to learn about and understand computerized decision support. If a business is to succeed, managers must know much more about information technology solutions. This second edition of a powerful introductory book is targeted at busy managers and MBA students who need to grasp the basics of computerized decision support, including the following: What are analytics? What is a decision support system? How can managers identify opportunities to create innovative computerized support? Inside, the author addresses these questions and some 60 more fundamental questions that are key to understanding the rapidly changing realm of computerized decision support. In a short period of time, you'll "get up to speed" on decision support, anal...

  3. Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes

    Directory of Open Access Journals (Sweden)

    Igor Vyacheslavovich Buzaev

    2016-09-01

    Full Text Available Objective: The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. Method: aLYNX system includes: registry with significant factors, decisions and results; machine learning process based on this registry data; the use of the machine learning results as the adviser. We show a possibility to build a computer adviser with a neural network model for making a choice between coronary aortic bypass surgery (CABG and percutaneous coronary intervention (PCI in order to achieve a higher 5-year survival rate in patients with angina based on the experience of 5107 patients. Results: The neural network was trained by 4679 patients who achieved 5-year survival. Among them, 2390 patients underwent PCI and 2289 CABG. After training, the correlation coefficient (r of the network was 0.74 for training, 0.67 for validation, 0.71 for test and 0.73 for total. Simulation of the neural network function has been performed after training in the two groups of patients with known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between neural network model and heart team [16.8% (787/4679 vs. 20.3% (87/428, P = 0.065]. Conclusion: The study shows the possibility to build a computer adviser with a neural network model for making a choice between CABG and PCI in order to achieve a higher 5-year survival rate in patients with angina. Keywords: Coronary artery bypass grafting, Percutaneous coronary intervention, Artificial intelligence, Decision making

  4. TeenBP: Development and Piloting of an EHR-Linked Clinical Decision Support System to Improve Recognition of Hypertension in Adolescents.

    Science.gov (United States)

    Kharbanda, Elyse O; Nordin, James D; Sinaiko, Alan R; Ekstrom, Heidi L; Stultz, Jerry M; Sherwood, Nancy E; Fontaine, Patricia L; Asche, Steve E; Dehmer, Steven P; Amundson, Jerry H; Appana, Deepika X; Bergdall, Anna R; Hayes, Marcia G; O'Connor, Patrick J

    2015-01-01

    Blood pressure (BP) is routinely measured in children and adolescents during primary care visits. However, elevated BP or hypertension is frequently not diagnosed or evaluated further by primary care providers. Barriers to recognition include lack of clinician buy-in, competing priorities, and complexity of the standard BP tables. We have developed and piloted TeenBP- a web-based, electronic health record (EHR) linked system designed to improve recognition of prehypertension and hypertension in adolescents during primary care visits. Important steps in developing TeenBP included the following: review of national BP guidelines, consideration of clinic workflow, engagement of clinical leaders, and evaluation of the impact on clinical sites. Use of a web-based platform has facilitated updates to the TeenBP algorithm and to the message content. In addition, the web-based platform has allowed for development of a sophisticated display of patient-specific information at the point of care. In the TeenBP pilot, conducted at a single pediatric and family practice site with six clinicians, over a five-month period, more than half of BPs in the hypertensive range were clinically recognized. Furthermore, in this small pilot the TeenBP clinical decision support (CDS) was accepted by providers and clinical staff. Effectiveness of the TeenBP CDS will be determined in a two-year cluster-randomized clinical trial, currently underway at 20 primary care sites. Use of technology to extract and display clinically relevant data stored within the EHR may be a useful tool for improving recognition of adolescent hypertension during busy primary care visits. In the future, the methods developed specifically for TeenBP are likely to be translatable to a wide range of acute and chronic issues affecting children and adolescents.

  5. A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

    Science.gov (United States)

    Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

    2017-08-01

    The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

  6. SMARTool: A tool for clinical decision support for the management of patients with coronary artery disease based on modeling of atherosclerotic plaque process.

    Science.gov (United States)

    Sakellarios, Antonis I; Rigas, George; Kigka, Vassiliki; Siogkas, Panagiotis; Tsompou, Panagiota; Karanasiou, Georgia; Exarchos, Themis; Andrikos, Ioannis; Tachos, Nikolaos; Pelosi, Gualtriero; Parodi, Oberdan; Fotiaids, Dimitrios I

    2017-07-01

    SMARTool aims to the development of a clinical decision support system (CDSS) for the management and stratification of patients with coronary artery disease (CAD). This will be achieved by performing computational modeling of the main processes of atherosclerotic plaque growth. More specifically, computed tomography coronary angiography (CTCA) is acquired and 3-dimensional (3D) reconstruction is performed for the arterial trees. Then, blood flow and plaque growth modeling is employed simulating the major processes of atherosclerosis, such as the estimation of endothelial shear stress (ESS), the lipids transportation, low density lipoprotein (LDL) oxidation, macrophages migration and plaque development. The plaque growth model integrates information from genetic and biological data of the patients. The SMARTool system enables also the calculation of the virtual functional assessment index (vFAI), an index equivalent to the invasively measured fractional flow reserve (FFR), to provide decision support for patients with stenosed arteries. Finally, it integrates modeling of stent deployment. In this work preliminary results are presented. More specifically, the reconstruction methodology has mean value of Dice Coefficient and Hausdorff Distance is 0.749 and 1.746, respectively, while low ESS and high LDL concentration can predict plaque progression.

  7. Flexible Decision Support in Dynamic Interorganizational Networks

    NARCIS (Netherlands)

    J. Collins (John); W. Ketter (Wolfgang); M. Gini (Maria)

    2008-01-01

    textabstractAn effective Decision Support System (DSS) should help its users improve decision-making in complex, information-rich, environments. We present a feature gap analysis that shows that current decision support technologies lack important qualities for a new generation of agile business

  8. Use of Mobile Clinical Decision Support Software by Junior Doctors at a UK Teaching Hospital: Identification and Evaluation of Barriers to Engagement.

    Science.gov (United States)

    Patel, Rakesh; Green, William; Shahzad, Muhammad Waseem; Larkin, Chris

    2015-08-13

    Clinical decision support (CDS) tools improve clinical diagnostic decision making and patient safety. The availability of CDS to health care professionals has grown in line with the increased prevalence of apps and smart mobile devices. Despite these benefits, patients may have safety concerns about the use of mobile devices around medical equipment. This research explored the engagement of junior doctors (JDs) with CDS and the perceptions of patients about their use. There were three objectives for this research: (1) to measure the actual usage of CDS tools on mobile devices (mCDS) by JDs, (2) to explore the perceptions of JDs about the drivers and barriers to using mCDS, and (3) to explore the perceptions of patients about the use of mCDS. This study used a mixed-methods approach to study the engagement of JDs with CDS accessed through mobile devices. Usage data were collected on the number of interactions by JDs with mCDS. The perceived drivers and barriers for JDs to using CDS were then explored by interviews. Finally, these findings were contrasted with the perception of patients about the use of mCDS by JDs. Nine of the 16 JDs made a total of 142 recorded interactions with the mCDS over a 4-month period. Only 27 of the 114 interactions (24%) that could be categorized as on-shift or off-shift occurred on-shift. Eight individual, institutional, and cultural barriers to engagement emerged from interviews with the user group. In contrast to reported cautions and concerns about the impact of clinicians' use of mobile phone on patient health and safety, patients had positive perceptions about the use of mCDS. Patients reported positive perceptions toward mCDS. The usage of mCDS to support clinical decision making was considered to be positive as part of everyday clinical practice. The degree of engagement was found to be limited due to a number of individual, institutional, and cultural barriers. The majority of mCDS engagement occurred outside of the workplace

  9. Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes.

    Science.gov (United States)

    Buzaev, Igor Vyacheslavovich; Plechev, Vladimir Vyacheslavovich; Nikolaeva, Irina Evgenievna; Galimova, Rezida Maratovna

    2016-09-01

    The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. aLYNX system includes: registry with significant factors, decisions and results; machine learning process based on this registry data; the use of the machine learning results as the adviser. We show a possibility to build a computer adviser with a neural network model for making a choice between coronary aortic bypass surgery (CABG) and percutaneous coronary intervention (PCI) in order to achieve a higher 5-year survival rate in patients with angina based on the experience of 5107 patients. The neural network was trained by 4679 patients who achieved 5-year survival. Among them, 2390 patients underwent PCI and 2289 CABG. After training, the correlation coefficient ( r ) of the network was 0.74 for training, 0.67 for validation, 0.71 for test and 0.73 for total. Simulation of the neural network function has been performed after training in the two groups of patients with known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between neural network model and heart team [16.8% (787/4679) vs. 20.3% (87/428), P  = 0.065)]. The study shows the possibility to build a computer adviser with a neural network model for making a choice between CABG and PCI in order to achieve a higher 5-year survival rate in patients with angina.

  10. Analysis of appropriateness of outpatient CT and MRI referred from primary care clinics at an academic medical center: how critical is the need for improved decision support?

    Science.gov (United States)

    Lehnert, Bruce E; Bree, Robert L

    2010-03-01

    The aim of this study was to retrospectively analyze a large group of CT and MRI examinations for appropriateness using evidence-based guidelines. The authors reviewed medical records from 459 elective outpatient CT and MR examinations from primary care physicians. Evidence-based appropriateness criteria from a radiology benefit management company were used to determine if the examination would have met criteria for approval. Submitted clinical history at the time of interpretation and clinic notes and laboratory results preceding the date of the imaging study were examined to simulate a real-time consultation with the referring provider. The radiology reports and subsequent clinic visits were analyzed for outcomes. Of the 459 examinations reviewed, 284 (62%) were CT and 175 (38%) were MRI. Three hundred forty-one (74%) were considered appropriate, and 118 (26%) were not considered appropriate. Examples of inappropriate examinations included brain CT for chronic headache, lumbar spine MR for acute back pain, knee or shoulder MRI in patients with osteoarthritis, and CT for hematuria during a urinary tract infection. Fifty-eight percent of the appropriate studies had positive results and affected subsequent management, whereas only thirteen percent [corrected] of inappropriate studies had positive results and affected management. A high percentage of examinations not meeting appropriateness criteria and subsequently yielding negative results suggests a need for tools to help primary care physicians improve the quality of their imaging decision requests. In the current environment, which stresses cost containment and comparative effectiveness, traditional radiology benefit management tools are being challenged by clinical decision support, with an emphasis on provider education coupled with electronic order entry systems.

  11. The limitations of using the existing TAM in adoption of clinical decision support system in hospitals: An empirical study in Malaysia

    Directory of Open Access Journals (Sweden)

    Pouyan Esmaeilzadeh

    2014-04-01

    Full Text Available The technology acceptance model (TAM has been widely used to study user acceptance of new computer technologies. Previous studies claimed that future technology acceptance research should explore other additional explanatory variables, which may affect the originally proposed constructs of the TAM. The use of information technology in the health care sector and especially in hospitals offers great potential for improving the performance of physicians, increasing the quality of services and also reducing the organizational expenses. However, the main challenge that arises according to the literature is whether healthcare professionals are willing to adopt and use clinical information technology while performing their tasks. Although adoption of various information technologies has been studied using the technology acceptance model (TAM, the study of technology acceptance for professional groups (such as physicians has been limited. Physician adoption of clinical information technology is important for its successful implementation. Therefore, the purpose of this study is to gain a better insight about factors affecting physicians’ acceptance of clinical decision support systems (CDSS in a hospital setting. The results reflect the importance of perceived threat to professional autonomy, perceived interactivity with clinical IT, perceived usefulness and perceived ease of use in determining physicians’ intention to use CDSS.

  12. A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems.

    Science.gov (United States)

    Simpao, Allan F; Tan, Jonathan M; Lingappan, Arul M; Gálvez, Jorge A; Morgan, Sherry E; Krall, Michael A

    2017-10-01

    Anesthesia information management systems (AIMS) are sophisticated hardware and software technology solutions that can provide electronic feedback to anesthesia providers. This feedback can be tailored to provide clinical decision support (CDS) to aid clinicians with patient care processes, documentation compliance, and resource utilization. We conducted a systematic review of peer-reviewed articles on near real-time and point-of-care CDS within AIMS using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. Studies were identified by searches of the electronic databases Medline and EMBASE. Two reviewers screened studies based on title, abstract, and full text. Studies that were similar in intervention and desired outcome were grouped into CDS categories. Three reviewers graded the evidence within each category. The final analysis included 25 articles on CDS as implemented within AIMS. CDS categories included perioperative antibiotic prophylaxis, post-operative nausea and vomiting prophylaxis, vital sign monitors and alarms, glucose management, blood pressure management, ventilator management, clinical documentation, and resource utilization. Of these categories, the reviewers graded perioperative antibiotic prophylaxis and clinical documentation as having strong evidence per the peer reviewed literature. There is strong evidence for the inclusion of near real-time and point-of-care CDS in AIMS to enhance compliance with perioperative antibiotic prophylaxis and clinical documentation. Additional research is needed in many other areas of AIMS-based CDS.

  13. Decision support in vaccination policies.

    Science.gov (United States)

    Piso, B; Wild, C

    2009-10-09

    Looking across boarders reveals that the national immunization programs of various countries differ in their vaccination schedules and decisions regarding the implementation and funding of new vaccines. The aim of this review is to identify decision aids and crucial criteria for a rational decision-making process on vaccine introduction and to develop a theoretical framework for decision-making based on available literature. Systematic literature search supplemented by hand-search. We identified five published decision aids for vaccine introduction and program planning in industrialized countries. Their comparison revealed an overall similarity with some differences in the approach as well as criteria. Burden of disease and vaccine characteristics play a key role in all decision aids, but authors vary in their views on the significance of cost-effectiveness analyses. Other relevant factors that should be considered before vaccine introduction are discussed to highly differing extents. These factors include the immunization program itself as well as its conformity with other programs, its feasibility, acceptability, and equity, as well as ethical, legal and political considerations. Assuming that the most comprehensive framework possible will not provide a feasible tool for decision-makers, we suggest a stepwise procedure. Though even the best rational approach and most comprehensive evaluation is limited by remaining uncertainties, frameworks provide at least a structured approach to evaluate the various aspects of vaccine implementation decision-making. This process is essential in making consistently sound decisions and will facilitate the public's confidence in the decision and its realization.

  14. Intention to adopt clinical decision support systems in a developing country: effect of Physician’s perceived professional autonomy, involvement and belief: a cross-sectional study

    Directory of Open Access Journals (Sweden)

    Sambasivan Murali

    2012-12-01

    Full Text Available Abstract Background Computer-based clinical decision support systems (CDSS are regarded as a key element to enhance decision-making in a healthcare environment to improve the quality of medical care delivery. The concern of having new CDSS unused is still one of the biggest issues in developing countries for the developers and implementers of clinical IT systems. The main objectives of this study are to determine whether (1 the physician’s perceived professional autonomy, (2 involvement in the decision to implement CDSS and (3 the belief that CDSS will improve job performance increase the intention to adopt CDSS. Four hypotheses were formulated and tested. Methods A questionnaire-based survey conducted between July 2010 and December 2010. The study was conducted in seven public and five private hospitals in Kuala Lumpur, Malaysia. Before contacting the hospitals, necessary permission was obtained from the Ministry of Health, Malaysia and the questionnaire was vetted by the ethics committee of the ministry. Physicians working in 12 hospitals from 10 different specialties participated in the study. The sampling method used was stratified random sampling and the physicians were stratified based on the specialty. A total of 450 physicians were selected using a random number generator. Each of these physicians was given a questionnaire and out of 450 questionnaires, 335 (response rate – 74% were returned and 309 (69% were deemed usable. Results The hypotheses were tested using Structural Equation Modeling (SEM. Salient results are: (1 Physicians’ perceived threat to professional autonomy lowers the intention to use CDSS (p Conclusion The proposed model with the three main constructs (physician’s professional characteristic, involvement and belief explains 47% of the variance in the intention to use CDSS. This is significantly higher than the models addressed so far. The results will have a major impact in implementing CDSS in developing

  15. KISTI at TREC 2014 Clinical Decision Support Track: Concept-based Document Re-ranking to Biomedical Information Retrieval

    Science.gov (United States)

    2014-11-01

    sematic type. Injury or Poisoning inpo T037 Anatomical Abnormality anab T190 Given a document D, a concept vector = {1, 2, … , ...integrating biomedical terminology . Nucleic acids research 32, Database issue (2004), 267–270. 5. Chapman, W.W., Hillert, D., Velupillai, S., et...Conference (TREC), (2011). 9. Koopman, B. and Zuccon, G. Understanding negation and family history to improve clinical information retrieval. Proceedings

  16. Uncertainty modeling and decision support

    International Nuclear Information System (INIS)

    Yager, Ronald R.

    2004-01-01

    We first formulate the problem of decision making under uncertainty. The importance of the representation of our knowledge about the uncertainty in formulating a decision process is pointed out. We begin with a brief discussion of the case of probabilistic uncertainty. Next, in considerable detail, we discuss the case of decision making under ignorance. For this case the fundamental role of the attitude of the decision maker is noted and its subjective nature is emphasized. Next the case in which a Dempster-Shafer belief structure is used to model our knowledge of the uncertainty is considered. Here we also emphasize the subjective choices the decision maker must make in formulating a decision function. The case in which the uncertainty is represented by a fuzzy measure (monotonic set function) is then investigated. We then return to the Dempster-Shafer belief structure and show its relationship to the fuzzy measure. This relationship allows us to get a deeper understanding of the formulation the decision function used Dempster- Shafer framework. We discuss how this deeper understanding allows a decision analyst to better make the subjective choices needed in the formulation of the decision function

  17. Customer Decision Support Systems: Resources for Student Decision Making

    Directory of Open Access Journals (Sweden)

    Cara Okleshen Peters, Ph.D.

    2005-07-01

    Full Text Available This paper highlights the potential of customer decision support systems (CDSS to assist students in education-related decision making. Faculty can use these resources to more effectively advise students on various elements of college life, while students can employ them to more actively participate in their own learning and improve their academic experience. This conceptual paper summarizes consumer decision support systems (CDSS concepts and presents exemplar websites students could utilize to support their education-related decision making. Finally, the authors discuss the potential benefits and drawbacks such resources engender from a student perspective and conclude with directions for future research.

  18. Promising adoption of an electronic clinical decision support system for antenatal and intrapartum care in rural primary healthcare facilities in sub-Saharan Africa: The QUALMAT experience.

    Science.gov (United States)

    Sukums, Felix; Mensah, Nathan; Mpembeni, Rose; Massawe, Siriel; Duysburgh, Els; Williams, Afua; Kaltschmidt, Jens; Loukanova, Svetla; Haefeli, Walter E; Blank, Antje

    2015-09-01

    The QUALMAT project has successfully implemented an electronic clinical decision support system (eCDSS) for antenatal and intrapartum care in two sub-Saharan African countries. The system was introduced to facilitate adherence to clinical practice guidelines and to support decision making during client encounter to bridge the know-do gap of health workers. This study aimed to describe health workers' acceptance and use of the eCDSS for maternal care in rural primary health care (PHC) facilities of Ghana and Tanzania and to identify factors affecting successful adoption of such a system. This longitudinal study was conducted in Lindi rural district in Tanzania and Kassena-Nankana district in Ghana between October 2011 and December 2013 employing mixed methods. The study population included healthcare workers who were involved in the provision of maternal care in six rural PHC facilities from one district in each country where the eCDSS was implemented. All eCDSS users participated in the study with 61 and 56 participants at the midterm and final assessment, respectively. After several rounds of user training and support the eCDSS has been successfully adopted and constantly used during patient care in antenatal clinics and maternity wards. The eCDSS was used in 71% (2703/3798) and 59% (14,189/24,204) of all ANC clients in Tanzania and Ghana respectively, while it was also used in 83% (1185/1427) and 67% (1435/2144) of all deliveries in Tanzania and in Ghana, respectively. Several barriers reported to hinder eCDSS use were related to individual users, tasks, technology, and organization attributes. Implementation of an eCDSS in resource-constrained PHC facilities in sub-Saharan Africa was successful and the health workers accepted and continuously used the system for maternal care. Facilitators for eCDSS use included sufficient training and regular support whereas the challenges to sustained use were unreliable power supply and perceived high workload. However our

  19. Guidelines for maternal and neonatal "point of care": needs of and attitudes towards a computerized clinical decision support system in rural Burkina Faso.

    Science.gov (United States)

    Zakane, S Alphonse; Gustafsson, Lars L; Tomson, Göran; Loukanova, Svetla; Sié, Ali; Nasiell, Josefine; Bastholm-Rahmner, Pia

    2014-06-01

    In 2010, 245,000 women died due to pregnancy-related causes in sub-Saharan Africa and southern Asia. Our study is nested into the QUALMAT project and seeks to improve the quality of maternal care services through the introduction of a computerized clinical decision support system (CDSS) to help healthcare workers in rural areas. Healthcare information technology applications in low-income countries may improve healthcare provision but recent studies demonstrate unintended consequences with underuse or resistance to CDSS and that the fit between the system and the clinical needs does present challenges. To explore and describe perceived needs and attitudes among healthcare workers to access WHO guidelines using CDSS in maternal and neonatal care in rural Burkina Faso. Data were collected with semi-structured interviews in two rural districts in Burkina Faso with 45 informants. Descriptive statistics were used for the analysis of the quantitative part of the interview corresponding to informants' background. Qualitative data were analyzed using manifest content analysis. Four main findings emerged: (a) an appreciable willingness among healthcare workers for and a great interest to adapt and use modern technologies like computers to learn more in the workplace, (b) a positive attitude to easy access of guidelines and implementation of decision-support using computers in the workplace, (c) a fear that the CDSS would require more working time and lead to double-work, and (d) that the CDSS is complicated and requires substantial computer training and extensive instructions to fully implement. The findings can be divided into aspects of motivators and barriers in relation to how the CDSS is perceived and to be used. These aspects are closely connected to each other as the motivating aspects can easily be turned into barriers if not taken care of properly in the final design, during implementation and maintenance of the CDSS at point of care. Copyright © 2014 Elsevier

  20. The effect of pharmacogenetic profiling with a clinical decision support tool on healthcare resource utilization and estimated costs in the elderly exposed to polypharmacy.

    Science.gov (United States)

    Brixner, D; Biltaji, E; Bress, A; Unni, S; Ye, X; Mamiya, T; Ashcraft, K; Biskupiak, J

    2016-01-01

    To compare healthcare resource utilization (HRU) and clinical decision-making for elderly patients based on cytochrome P450 (CYP) pharmacogenetic testing and the use of a comprehensive medication management clinical decision support tool (CDST), to a cohort of similar non-tested patients. An observational study compared a prospective cohort of patients ≥65 years subjected to pharmacogenetic testing to a propensity score (PS) matched historical cohort of untested patients in a claims database. Patients had a prescribed medication or dose change of at least one of 61 oral drugs or combinations of ≥3 drugs at enrollment. Four-month HRU outcomes examined included hospitalizations, emergency department (ED) and outpatient visits and provider acceptance of test recommendations. Costs were estimated using national data sources. There were 205 tested patients PS matched to 820 untested patients. Hospitalization rate was 9.8% in the tested group vs. 16.1% in the untested group (RR = 0.61, 95% CI = 0.39-0.95, p = 0.027), ED visit rate was 4.4% in the tested group vs. 15.4% in the untested group (RR = 0.29, 95% CI = 0.15-0.55, p = 0.0002) and outpatient visit rate was 71.7% in the tested group vs. 36.5% in the untested group (RR = 1.97, 95% CI = 1.74-2.23, p provider majority (95%) considered the test helpful and 46% followed CDST provided recommendations. Patients CYP DNA tested and treated according to the personalized prescribing system had a significant decrease in hospitalizations and emergency department visits, resulting in potential cost savings. Providers had a high satisfaction rate with the clinical utility of the system and followed recommendations when appropriate.

  1. A Clinical Decision Support Engine Based on a National Medication Repository for the Detection of Potential Duplicate Medications: Design and Evaluation.

    Science.gov (United States)

    Yang, Cheng-Yi; Lo, Yu-Sheng; Chen, Ray-Jade; Liu, Chien-Tsai

    2018-01-19

    A computerized physician order entry (CPOE) system combined with a clinical decision support system can reduce duplication of medications and thus adverse drug reactions. However, without infrastructure that supports patients' integrated medication history across health care facilities nationwide, duplication of medication can still occur. In Taiwan, the National Health Insurance Administration has implemented a national medication repository and Web-based query system known as the PharmaCloud, which allows physicians to access their patients' medication records prescribed by different health care facilities across Taiwan. This study aimed to develop a scalable, flexible, and thematic design-based clinical decision support (CDS) engine, which integrates a national medication repository to support CPOE systems in the detection of potential duplication of medication across health care facilities, as well as to analyze its impact on clinical encounters. A CDS engine was developed that can download patients' up-to-date medication history from the PharmaCloud and support a CPOE system in the detection of potential duplicate medications. When prescribing a medication order using the CPOE system, a physician receives an alert if there is a potential duplicate medication. To investigate the impact of the CDS engine on clinical encounters in outpatient services, a clinical encounter log was created to collect information about time, prescribed drugs, and physicians' responses to handling the alerts for each encounter. The CDS engine was installed in a teaching affiliate hospital, and the clinical encounter log collected information for 3 months, during which a total of 178,300 prescriptions were prescribed in the outpatient departments. In all, 43,844/178,300 (24.59%) patients signed the PharmaCloud consent form allowing their physicians to access their medication history in the PharmaCloud. The rate of duplicate medication was 5.83% (1843/31,614) of prescriptions. When

  2. Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

    Directory of Open Access Journals (Sweden)

    Simon Fong

    2013-01-01

    Full Text Available Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

  3. A mobile and web-based clinical decision support and monitoring system for diabetes mellitus patients in primary care: a study protocol for a randomized controlled trial.

    Science.gov (United States)

    Kart, Özge; Mevsim, Vildan; Kut, Alp; Yürek, İsmail; Altın, Ayşe Özge; Yılmaz, Oğuz

    2017-11-29

    Physicians' guideline use rates for diagnosis, treatment and monitoring of diabetes mellitus (DM) is very low. Time constraints, patient overpopulation, and complex guidelines require alternative solutions for real time patient monitoring. Rapidly evolving e-health technology combined with clinical decision support and monitoring systems (CDSMS) provides an effective solution to these problems. The purpose of the study is to develop a user-friendly, comprehensive, fully integrated web and mobile-based Clinical Decision Support and Monitoring System (CDSMS) for the screening, diagnosis, treatment, and monitoring of DM diseases which is used by physicians and patients in primary care and to determine the effectiveness of the system. The CDSMS will be based on evidence-based guidelines for DM disease. A web and mobile-based application will be developed in which the physician will remotely monitor patient data through mobile applications in real time. The developed CDSMS will be tested in two stages. In the first stage, the usability, understandability, and adequacy of the application will be determined. Five primary care physicians will use the developed application for at least 16 DM patients. Necessary improvements will be made according to physician feedback. In the second phase, a parallel, single-blind, randomized controlled trial will be implemented. DM diagnosed patients will be recruited for the CDSMS trial by their primary care physicians. Ten physicians and their 439 patients will be involved in the study. Eligible participants will be assigned to intervention and control groups with simple randomization. The significance level will be accepted as p system will make recommendations on patient monitoring, diagnosis, and treatment. These recommendations will be implemented at the physician's discretion. Patients in the control group will be treated by physicians according to current DM treatment standards. Patients in both groups will be monitored for 6

  4. C-A4-01: Computerized Clinical Decision Support During Drug Ordering for Long-term Care Residents With Renal Insufficiency

    Science.gov (United States)

    Field, Terry S; Rochon, Paula; Lee, Monica; Gavendo, Linda; Baril, Joann L; Gurwitz, Jerry H

    2010-01-01

    Objective: To determine whether a computerized clinical decision support system (CDSS) providing patient specific recommendations in real- time improves the quality of prescribing for long-term care residents with renal insufficiency. Design: A randomized trial within the long-stay units of a large long-term care facility. Randomization was within blocks by unit type. Alerts related to medication prescribing for residents with renal insufficiency were displayed to prescribers in the intervention units and hidden but tracked in control units. Measurement: The proportions of final drug orders that were appropriate were compared between intervention and control units within alert categories: recommended medication doses; recommended administration frequencies; recommendations to avoid the drug; 4) warnings of missing information. Results: The rates of alerts were nearly equal in the intervention and control units: 2.5 per 1000 resident days in the intervention units and 2.4 in the control units. The proportions of dose alerts for which the final drug orders were appropriate were similar between the intervention and control units (relative risk 0.95, 95% confidence interval 0.83, 1.1). For the remaining alert categories significantly higher proportions of final drug orders were appropriate in the intervention units: relative risk 2.4 for maximum frequency (1.4, 4.4); 2.6 for drugs that should be avoided (1.4, 5.0); and 1.8 for alerts to acquire missing information (1.1, 3.4). Overall, final drug orders were appropriate significantly more often than a relative risk 1.2 (1.0, 1.4). By tracking personnel time and expenditures, we estimated the cost of developing the CDSS as $48,668.57. Drug costs saved during the 12 months of the trial are estimated at $2,137. Conclusion: Clinical decision support for physicians prescribing medications for long-term care residents with renal insufficiency can improve the quality of prescribing decisions. However, patient well-being and

  5. Toward the Modularization of Decision Support Systems

    Science.gov (United States)

    Raskin, R. G.

    2009-12-01

    Decision support systems are typically developed entirely from scratch without the use of modular components. This “stovepiped” approach is inefficient and costly because it prevents a developer from leveraging the data, models, tools, and services of other developers. Even when a decision support component is made available, it is difficult to know what problem it solves, how it relates to other components, or even that the component exists, The Spatial Decision Support (SDS) Consortium was formed in 2008 to organize the body of knowledge in SDS within a common portal. The portal identifies the canonical steps in the decision process and enables decision support components to be registered, categorized, and searched. This presentation describes how a decision support system can be assembled from modular models, data, tools and services, based on the needs of the Earth science application.

  6. Assessment of readiness for clinical decision support to aid laboratory monitoring of immunosuppressive care at U.S. liver transplant centers.

    Science.gov (United States)

    Jacobs, J; Weir, C; Evans, R S; Staes, C

    2014-01-01

    Following liver transplantation, patients require lifelong immunosuppressive care and monitoring. Computerized clinical decision support (CDS) has been shown to improve post-transplant immunosuppressive care processes and outcomes. The readiness of transplant information systems to implement computerized CDS to support post-transplant care is unknown. a) Describe the current clinical information system functionality and manual and automated processes for laboratory monitoring of immunosuppressive care, b) describe the use of guidelines that may be used to produce computable logic and the use of computerized alerts to support guideline adherence, and c) explore barriers to implementation of CDS in U.S. liver transplant centers. We developed a web-based survey using cognitive interviewing techniques. We surveyed 119 U.S. transplant programs that performed at least five liver transplantations per year during 2010-2012. Responses were summarized using descriptive analyses; barriers were identified using qualitative methods. Respondents from 80 programs (67% response rate) completed the survey. While 98% of programs reported having an electronic health record (EHR), all programs used paper-based manual processes to receive or track immunosuppressive laboratory results. Most programs (85%) reported that 30% or more of their patients used external laboratories for routine testing. Few programs (19%) received most external laboratory results as discrete data via electronic interfaces while most (80%) manually entered laboratory results into the EHR; less than half (42%) could integrate internal and external laboratory results. Nearly all programs had guidelines regarding pre-specified target ranges (92%) or testing schedules (97%) for managing immunosuppressive care. Few programs used computerized alerting to notify transplant coordinators of out-of-range (27%) or overdue laboratory results (20%). Use of EHRs is common, yet all liver transplant programs were largely

  7. Costs associated with implementation of computer-assisted clinical decision support system for antenatal and delivery care: case study of Kassena-Nankana district of northern Ghana.

    Science.gov (United States)

    Dalaba, Maxwell Ayindenaba; Akweongo, Patricia; Williams, John; Saronga, Happiness Pius; Tonchev, Pencho; Sauerborn, Rainer; Mensah, Nathan; Blank, Antje; Kaltschmidt, Jens; Loukanova, Svetla

    2014-01-01

    This study analyzed cost of implementing computer-assisted Clinical Decision Support System (CDSS) in selected health care centres in Ghana. A descriptive cross sectional study was conducted in the Kassena-Nankana district (KND). CDSS was deployed in selected health centres in KND as an intervention to manage patients attending antenatal clinics and the labour ward. The CDSS users were mainly nurses who were trained. Activities and associated costs involved in the implementation of CDSS (pre-intervention and intervention) were collected for the period between 2009-2013 from the provider perspective. The ingredients approach was used for the cost analysis. Costs were grouped into personnel, trainings, overheads (recurrent costs) and equipment costs (capital cost). We calculated cost without annualizing capital cost to represent financial cost and cost with annualizing capital costs to represent economic cost. Twenty-two trained CDSS users (at least 2 users per health centre) participated in the study. Between April 2012 and March 2013, users managed 5,595 antenatal clients and 872 labour clients using the CDSS. We observed a decrease in the proportion of complications during delivery (pre-intervention 10.74% versus post-intervention 9.64%) and a reduction in the number of maternal deaths (pre-intervention 4 deaths versus post-intervention 1 death). The overall financial cost of CDSS implementation was US$23,316, approximately US$1,060 per CDSS user trained. Of the total cost of implementation, 48% (US$11,272) was pre-intervention cost and intervention cost was 52% (US$12,044). Equipment costs accounted for the largest proportion of financial cost: 34% (US$7,917). When economic cost was considered, total cost of implementation was US$17,128-lower than the financial cost by 26.5%. The study provides useful information in the implementation of CDSS at health facilities to enhance health workers' adherence to practice guidelines and taking accurate decisions to improve

  8. Risk-based emergency decision support

    International Nuclear Information System (INIS)

    Koerte, Jens

    2003-01-01

    In the present paper we discuss how to assist critical decisions taken under complex, contingent circumstances, with a high degree of uncertainty and short time frames. In such sharp-end decision regimes, standard rule-based decision support systems do not capture the complexity of the situation. At the same time, traditional risk analysis is of little use due to variability in the specific circumstances. How then, can an organisation provide assistance to, e.g. pilots in dealing with such emergencies? A method called 'contingent risk and decision analysis' is presented, to provide decision support for decisions under variable circumstances and short available time scales. The method consists of nine steps of definition, modelling, analysis and criteria definition to be performed 'off-line' by analysts, and procedure generation to transform the analysis result into an operational decision aid. Examples of pilots' decisions in response to sudden vibration in offshore helicopter transport method are used to illustrate the approach

  9. Physicians’ use of computerized clinical decision supports to improve medication management in the elderly – the Seniors Medication Alert and Review Technology intervention

    Directory of Open Access Journals (Sweden)

    Alagiakrishnan K

    2016-01-01

    Full Text Available Kannayiram Alagiakrishnan,1 Patricia Wilson,2 Cheryl A Sadowski,3 Darryl Rolfson,1 Mark Ballermann,4,5 Allen Ausford,6,7 Karla Vermeer,7 Kunal Mohindra,8 Jacques Romney,9 Robert S Hayward10 1Department of Medicine, Division of Geriatric Medicine, 2Department of Medicine, 3Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, 4Chief Medical Information Office, Alberta Health Services, 5Division of Critical Care, Department of Medicine, University of Alberta, 6Department of Family Medicine, University of Alberta, 7Lynwood Family Physician, 8eClinician EMR, Alberta Health Services-Information Systems, 9Department of Medicine, Division of Endocrinology, 10Division of General Internal Medicine, University of Alberta, Edmonton, AB, Canada Background: Elderly people (aged 65 years or more are at increased risk of polypharmacy (five or more medications, inappropriate medication use, and associated increased health care costs. The use of clinical decision support (CDS within an electronic medical record (EMR could improve medication safety.Methods: Participatory action research methods were applied to preproduction design and development and postproduction optimization of an EMR-embedded CDS implementation of the Beers’ Criteria for medication management and the Cockcroft–Gault formula for estimating glomerular filtration rates (GFR. The “Seniors Medication Alert and Review Technologies” (SMART intervention was used in primary care and geriatrics specialty clinics. Passive (chart messages and active (order-entry alerts prompts exposed potentially inappropriate medications, decreased GFR, and the possible need for medication adjustments. Physician reactions were assessed using surveys, EMR simulations, focus groups, and semi-structured interviews. EMR audit data were used to identify eligible patient encounters, the frequency of CDS events, how alerts were managed, and when evidence links were followed.Results: Analysis of

  10. Decision support for customers in electronic environments

    Directory of Open Access Journals (Sweden)

    František Dařena

    2011-01-01

    Full Text Available Due to the rapid spread of computer technologies into day-to-day lives many purchases or purchase-related decisions are made in the electronic environment of the Web. In order to handle information overload that is the result of the availability of many web-based stores, products and services, consumers use decision support aids that help with need recognition, information retrieval, filtering, comparisons and choice making. Decision support systems (DSS discipline spreads about 40 years back and was mostly focused on assisting managers. However, online environments and decision support in such environments bring new opportunities also to the customers. The focus on decision support for consumers is also not investigated to the large extent and not documented in the literature. Providing customers with well designed decision aids can lead to lower cognitive decision effort associated with the purchase decision which results in significant increase of consumer’s confidence, satisfaction, and cost savings. During decision making process the subjects can chose from several methods (optimizing, reasoning, analogizing, and creating, DSS types (data-, model-, communication-, document-driven, and knowledge-based and benefit from different modern technologies. The paper investigates popular customer decision making aids, such as search, filtering, comparison, ­e-negotiations and auctions, recommendation systems, social network systems, product design applications, communication support etc. which are frequently related to e-commerce applications. Results include the overview of such decision supporting tools, specific examples, classification according the way how the decisions are supported, and possibilities of applications of progressive technologies. The paper thus contributes to the process of development of the interface between companies and the customers where customer decisions take place.

  11. DECISIONS, METHODS AND TECHNIQUES RELATED TO DECISION SUPPORT SYSTEMS (DSS

    Directory of Open Access Journals (Sweden)

    Boghean Florin

    2015-07-01

    Full Text Available Generalised uncertainty, a phenomenon that today’s managers are facing as part of their professional experience, makes it impossible to anticipate the way the business environment will evolve or what will be the consequences of the decisions they plan to implement. Any decision making process within the company entails the simultaneous presence of a number of economic, technical, juridical, human and managerial variables. The development and the approval of a decision is the result of decision making activities developed by the decision maker and sometimes by a decision support team or/and a decision support system (DSS. These aspects related to specific applications of decision support systems in risk management will be approached in this research paper. Decisions in general and management decisions in particular are associated with numerous risks, due to their complexity and increasing contextual orientation. In each business entity, there are concerns with the implementation of risk management in order to improve the likelihood of meeting objectives, the trust of the parties involved, increase the operational safety and security as well as the protection of the environment, minimise losses, improve organisational resilience in order to diminish the negative impact on the organisation and provide a solid foundation for decision making. Since any business entity is considered to be a wealth generator, the analysis of their performance should not be restricted to financial efficiency alone, but will also encompass their economic efficiency as well. The type of research developed in this paper entails different dimensions: conceptual, methodological, as well as empirical testing. Subsequently, the conducted research entails a methodological side, since the conducted activities have resulted in the presentation of a simulation model that is useful in decision making processes on the capital market. The research conducted in the present paper

  12. Decision support system for Wamakersvallei Winery

    CSIR Research Space (South Africa)

    Van Der Merwe, A

    2007-09-01

    Full Text Available The goal of the study is to lend decision support to management a a wine cellar in three areas of expertise, with Wamakersvallei Winery serving as a special case study. This decision support system is to be delivered in the form of Excel spreadsheet...

  13. Fertility and contraceptive decision-making and support for HIV infected individuals: client and provider experiences and perceptions at two HIV clinics in Uganda

    Directory of Open Access Journals (Sweden)

    Wanyenze Rhoda K

    2013-02-01

    Full Text Available Abstract Background Some people living with HIV/AIDS (PLHIV want to have children while others want to prevent pregnancies; this calls for comprehensive services to address both needs. This study explored decisions to have or not to have children and contraceptive preferences among PLHIV at two clinics in Uganda. Methods This was a qualitative cross-sectional study. We conducted seventeen focus group discussions and 14 in-depth interviews with sexually active adult men and women and adolescent girls and boys, and eight key informant interviews with providers. Overall, 106 individuals participated in the interviews; including 84 clients through focus group discussions. Qualitative latent content analysis technique was used, guided by key study questions and objectives. A coding system was developed before the transcripts were examined. Codes were grouped into categories and then themes and subthemes further identified. Results In terms of contraceptive preferences, clients had a wide range of preferences; whereas some did not like condoms, pills and injectables, others preferred these methods. Fears of complications were raised mainly about pills and injectables while cost of the methods was a major issue for the injectables, implants and intrauterine devices. Other than HIV sero-discordance and ill health (which was cited as transient, the decision to have children or not was largely influenced by socio-cultural factors. All adult men, women and adolescents noted the need to have children, preferably more than one. The major reasons for wanting more children for those who already had some were; the sex of the children (wanting to have both girls and boys and especially boys, desire for large families, pressure from family, and getting new partners. Providers were supportive of the decision to have children, especially for those who did not have any child at all, but some clients cited negative experiences with providers and information gaps for

  14. Barriers to and facilitators of implementing shared decision making and decision support in a paediatric hospital: A descriptive study.

    Science.gov (United States)

    Boland, Laura; McIsaac, Daniel I; Lawson, Margaret L

    2016-04-01

    To explore multiple stakeholders' perceived barriers to and facilitators of implementing shared decision making and decision support in a tertiary paediatric hospital. An interpretive descriptive qualitative study was conducted using focus groups and interviews to examine senior hospital administrators', clinicians', parents' and youths' perceived barriers to and facilitators of shared decision making and decision support implementation. Data were analyzed using inductive thematic analysis. Fifty-seven stakeholders participated. Six barrier and facilitator themes emerged. The main barrier was gaps in stakeholders' knowledge of shared decision making and decision support. Facilitators included compatibility between shared decision making and the hospital's culture and ideal practices, perceptions of positive patient and family outcomes associated with shared decision making, and positive attitudes regarding shared decision making and decision support. However, youth attitudes regarding the necessity and usefulness of a decision support program were a barrier. Two themes were both a barrier and a facilitator. First, stakeholder groups were uncertain which clinical situations are suitable for shared decision making (eg, new diagnoses, chronic illnesses, complex decisions or urgent decisions). Second, the clinical process may be hindered if shared decision making and decision support decrease efficiency and workflow; however, shared decision making may reduce repeat visits and save time over the long term. Specific knowledge translation strategies that improve shared decision making knowledge and match specific barriers identified by each stakeholder group may be required to promote successful shared decision making and decision support implementation in the authors' paediatric hospital.

  15. "Send & Hold" Clinical Decision Support Rules improvement to reduce unnecessary testing of vitamins A, E, K, B1, B2, B3, B6 and C.

    Science.gov (United States)

    Rodriguez-Borja, Enrique; Corchon-Peyrallo, Africa; Barba-Serrano, Esther; Villalba Martínez, Celia; Carratala Calvo, Arturo

    2018-02-03

    We assessed the impact of several "send & hold" clinical decision support rules (CDSRs) within the electronical request system for vitamins A, E, K, B1, B2, B3, B6 and C for all outpatients at a large health department. When ordered through electronical request, providers (except for all our primary care physicians who worked as a non-intervention control group) were always asked to answer several compulsory questions regarding main indication, symptomatology, suspected diagnosis, vitamin active treatments, etc., for each vitamin test using a drop-down list format. After samples arrival, tests were later put on hold internally by our laboratory information system (LIS) until review for their appropriateness was made by two staff pathologists according to the provided answers and LIS records (i.e. "send & hold"). The number of tests for each analyte was compared between the 10-month period before and after CDSRs implementation in both groups. After implementation, vitamins test volumes decreased by 40% for vitamin A, 29% for vitamin E, 42% for vitamin K, 37% for vitamin B1, 85% for vitamin B2, 68% for vitamin B3, 65% for vitamin B6 and 59% for vitamin C (all p values 0.03 or lower except for vitamin B3), whereas in control group, the majority increased or remained stable. In patients with rejected vitamins, no new requests and/or adverse clinical outcome comments due to this fact were identified. "Send & hold" CDSRs are a promising informatics tool that can support in utilization management and enhance the pathologist's leadership role as tests specialist.

  16. Decision support in supervisory control

    International Nuclear Information System (INIS)

    Rasmussen, J.; Goodstein, L.P.

    1985-08-01

    It is argued that the supervisory control of complex industrial processes having a potential for serious consequences in case of accidents requires careful consideration of the allocation of decision making between the three main agents of control; namely the designer, the operator and the automatic control system. In particular, it is advocated that instead of continuing their efforts to make their preplanning of responses and countermeasures more and more complete and restricting the operator's initiative, designers should take advantage of modern information technology to make available to the operators their conceptual models and their processing resources so as to allow the operators to function as their extended arm in coping with the plant. Such an interactive decision making activity would thus benefit from this simultaneous availability of the design basis, up-to-date knowledge of plant status and accumualted operational experience. (author)

  17. A pragmatic study exploring the prevention of delirium among hospitalized older hip fracture patients: Applying evidence to routine clinical practice using clinical decision support

    Directory of Open Access Journals (Sweden)

    Schmaltz Heidi N

    2010-10-01

    Full Text Available Abstract Delirium occurs in up to 65% of older hip fracture patients. Developing delirium in hospital has been associated with a variety of adverse outcomes. Trials have shown that multi-component preventive interventions can lower delirium rates. The objective of this study was to implement and evaluate the effectiveness of an evidence-based electronic care pathway, which incorporates multi-component delirium strategies, among older hip fracture patients. We conducted a pragmatic study using an interrupted time series design in order to evaluate the use and impact of the intervention. The target population was all consenting patients aged 65 years or older admitted with an acute hip fracture to the orthopedic units at two Calgary, Alberta hospitals. The primary outcome was delirium rates. Secondary outcomes included length of hospital stay, in-hospital falls, in-hospital mortality, new discharges to long-term care, and readmissions. A Durbin Watson test was conducted to test for serial correlation and, because no correlation was found, Chi-square statistics, Wilcoxon test and logistic regression analyses were conducted as appropriate. At study completion, focus groups were conducted at each hospital to explore issues around the use of the order set. During the 40-week study period, 134 patients were enrolled. The intervention had no effect on the overall delirium rate (33% pre versus 31% post; p = 0.84. However, there was a significant interaction between study phase and hospital (p = 0.03. Although one hospital did not experience a decline in delirium rate, the delirium rate at the other hospital declined from 42% to 19% (p = 0.08. This difference by hospital was mirrored in focus group feedback. The hospital that experienced a decline in delirium rates was more supportive of the intervention. Overall, post-intervention there were no significant differences in mean length of stay (12 days post versus 14 days pre; p = 0.74, falls (6% post

  18. Using Visualization in Cockpit Decision Support Systems

    Energy Technology Data Exchange (ETDEWEB)

    Aragon, Cecilia R.

    2005-07-01

    In order to safely operate their aircraft, pilots must makerapid decisions based on integrating and processing large amounts ofheterogeneous information. Visual displays are often the most efficientmethod of presenting safety-critical data to pilots in real time.However, care must be taken to ensure the pilot is provided with theappropriate amount of information to make effective decisions and notbecome cognitively overloaded. The results of two usability studies of aprototype airflow hazard visualization cockpit decision support systemare summarized. The studies demonstrate that such a system significantlyimproves the performance of helicopter pilots landing under turbulentconditions. Based on these results, design principles and implicationsfor cockpit decision support systems using visualization arepresented.

  19. Platform decisions supported by gaming

    DEFF Research Database (Denmark)

    Hansen, Poul H. Kyvsgård; Mikkola, Juliana Hsuan

    2007-01-01

    Platform is an ambiguous multidisciplinary concept. The philosophy behind it is easy to communicate and makes intuitively sense. However, the ease in communication does overshadow the high complexity when the concept is implemented. The practical industrial platform implementation challenge can...... be described as being a configuration problem with a high number of variables. These variables are different in nature; they have contradictory influence on the total performance, and, their importance change over time. Consequently, the specific platform decisions become highly complex and the consequences...

  20. DECISION SUPPORT SYSTEM TO SUPPORT DECISION PROCESSES WITH DATA MINING

    OpenAIRE

    Rupnik, Rok; Kukar, Matjaž

    2007-01-01

    Traditional techniques of data analysis do not enable the solution of all kind of problems and for that reason they have become insufficient. This caused a newinterdisciplinary field of data mining to arise, encompassing both classical statistical, and modern machine learning techniques to support the data analysis and knowledge discovery from data. Data mining methods are powerful in dealing with large quantities of data, but on the other hand they are difficult to master by business users t...

  1. [Cognitive traps and clinical decisions].

    Science.gov (United States)

    Motterlini, Matteo

    2017-12-01

    We are fallible, we have limited computational capabilities, limited access to information, little memory. Moreover, in everyday life, we feel joy, fear, anger, and other emotions that influence our decisions in a little, "calculated" way. Not everyone, however, is also aware that the mistakes we make are often systematic and therefore, in particular circumstances, are foreseeable. Doctors and patients are constantly called upon to make decisions. They need to identify relevant information (for example, the symptoms or outcome of an examination), formulate a judgment (for example a diagnosis), choose an action course among the various possible ones based on one's own preferences (e.g. medication or surgery), so act. The exact size of the medical error is unknown, but probably huge. In fact, the more we investigate and the more we find. Often these mistakes depend on the cognitive process. Any (rational) decision requires, in particular, an assessment of the possible effects of the action it implements; for example how much pleasure or pain it will cause us. In the medical field, too, the principle of informed consent provides that the patient's preferences and values are to guide clinical choices. Yet, not always the preferences that people express before making an experience match with their preferences after living that experience. Some ingenious experiments suggest (in a seemingly paradoxical way) that before a direct experience, people prefer less pain; after that experience they prefer more, but with a better memory.

  2. 'My kidneys, my choice, decision aid': supporting shared decision making.

    Science.gov (United States)

    Fortnum, Debbie; Smolonogov, Tatiana; Walker, Rachael; Kairaitis, Luke; Pugh, Debbie

    2015-06-01

    For patients with chronic kidney disease (CKD) who are progressing to end-stage kidney disease (ESKD) a decision of whether to undertake dialysis or conservative care is a critical component of the patient journey. Shared decision making for complex decisions such as this could be enhanced by a decision aid, a practice which is well utilised in other disciplines but limited for nephrology. A multidisciplinary team in Australia and New Zealand (ANZ) utilised current decision-making theory and best practice to develop the 'My Kidneys, My Choice', a decision aid for the treatment of kidney disease. A patient-centred, five-sectioned tool is now complete and freely available to all ANZ units to support the ESKD education and shared decision-making process. Distribution and education have occurred across ANZ and evaluation of the decision aid in practice is in the first phase. Development of a new tool such as an ESKD decision aid requires vision, multidisciplinary input and ongoing implementation resources. This tool is being integrated into ANZ, ESKD education practice and is promoting the philosophy of shared decision making. © 2014 European Dialysis and Transplant Nurses Association/European Renal Care Association.

  3. Shared decision making in type 2 diabetes with a support decision tool that takes into account clinical factors, the intensity of treatment and patient preferences : Design of a cluster randomised (OPTIMAL) trial

    NARCIS (Netherlands)

    Den Ouden, Henk; Vos, Rimke C.; Reidsma, Carla; Rutten, Guy Ehm

    2015-01-01

    Background: No more than 10-15% of type 2 diabetes mellitus (T2DM) patients achieve all treatment goals regarding glycaemic control, lipids and blood pressure. Shared decision making (SDM) should increase that percentage; however, not all support decision tools are appropriate. Because the

  4. Boosting Quality Registries with Clinical Decision Support Functionality*. User Acceptance of a Prototype Applied to HIV/TB Drug Therapy.

    Science.gov (United States)

    Wannheden, Carolina; Hvitfeldt-Forsberg, Helena; Eftimovska, Elena; Westling, Katarina; Ellenius, Johan

    2017-08-11

    The care of HIV-related tuberculosis (HIV/TB) is complex and challenging. Clinical decision support (CDS) systems can contribute to improve quality of care, but more knowledge is needed on factors determining user acceptance of CDS. To analyze physicians' and nurses' acceptance of a CDS prototype for evidence-based drug therapy recommendations for HIV/TB treatment. Physicians and nurses were involved in designing a CDS prototype intended for future integration with the Swedish national HIV quality registry. Focus group evaluation was performed with ten nurses and four physicians, respectively. The Unified Theory of Acceptance and Use of Technology (UTAUT) was used to analyze acceptance. We identified several potential benefits with the CDS prototype as well as some concerns that could be addressed by redesign. There was also concern about dependence on physician attitudes, as well as technical, organizational, and legal issues. Acceptance evaluation at a prototype stage provided rich data to improve the future design of a CDS prototype. Apart from design and development efforts, substantial organizational efforts are needed to enable the implementation and maintenance of a future CDS system.

  5. Decision support for utility environmental risk management

    International Nuclear Information System (INIS)

    Balson, W.E.; Wilson, D.S.

    1991-01-01

    This paper reviews a number of decision support methods developed and applied by Decision Focus Incorporated to help utility personnel manage current environmental problems. This work has been performed for the Environmental Risk Analysis Program of EPRI's Environment Division, and also for a number of electric utilities across the country. These are two distinct types of decision support software tools that have been created: economic risk management and environmental risk analysis. These types differ primarily in the identification of who will make a decision. Economic risk management tools are directed primarily at decisions made by electric utilities. Environmental risk analysis tools are directed primarily at decisions made by legislative or regulatory agencies, about which a utility may wish to comment

  6. PTT Advisor: A CDC-supported initiative to develop a mobile clinical laboratory decision support application for the iOS platform.

    Science.gov (United States)

    Savel, Thomas G; Lee, Brian A; Ledbetter, Greg; Brown, Sara; Lavalley, Dale; Taylor, Julie; Thompson, Pam

    2013-01-01

    This manuscript describes the development of PTT (Partial Thromboplastin Time) Advisor, one of the first of a handful of iOS-based mobile applications to be released by the US Centers for Disease Control and Prevention (CDC). PTT Advisor has been a collaboration between two groups at CDC (Informatics R&D and Laboratory Science), and one partner team (Clinical Laboratory Integration into Healthcare Collaborative - CLIHC). The application offers clinicians a resource to quickly select the appropriate follow-up tests to evaluate patients with a prolonged PTT and a normal Prothrombin Time (PT) laboratory result. The application was designed leveraging an agile methodology, and best practices in user experience (UX) design and mobile application development. As it is an open-source project, the code to PTT Advisor was made available to the public under the Apache Software License. On July 6, 2012, the free app was approved by Apple, and was published to their App Store. Regardless of the complexity of the mobile application, the level of effort required in the development process should not be underestimated. There are several issues that make designing the UI for a mobile phone challenging (not just small screen size): the touchscreen, users' mobile mindset (tasks need to be quick and focused), and the fact that mobile UI conventions/expectations are still being defined and refined (due to the maturity level of the field of mobile application development).

  7. Costs associated with implementation of computer-assisted clinical decision support system for antenatal and delivery care: case study of Kassena-Nankana district of northern Ghana.

    Directory of Open Access Journals (Sweden)

    Maxwell Ayindenaba Dalaba

    Full Text Available This study analyzed cost of implementing computer-assisted Clinical Decision Support System (CDSS in selected health care centres in Ghana.A descriptive cross sectional study was conducted in the Kassena-Nankana district (KND. CDSS was deployed in selected health centres in KND as an intervention to manage patients attending antenatal clinics and the labour ward. The CDSS users were mainly nurses who were trained. Activities and associated costs involved in the implementation of CDSS (pre-intervention and intervention were collected for the period between 2009-2013 from the provider perspective. The ingredients approach was used for the cost analysis. Costs were grouped into personnel, trainings, overheads (recurrent costs and equipment costs (capital cost. We calculated cost without annualizing capital cost to represent financial cost and cost with annualizing capital costs to represent economic cost.Twenty-two trained CDSS users (at least 2 users per health centre participated in the study. Between April 2012 and March 2013, users managed 5,595 antenatal clients and 872 labour clients using the CDSS. We observed a decrease in the proportion of complications during delivery (pre-intervention 10.74% versus post-intervention 9.64% and a reduction in the number of maternal deaths (pre-intervention 4 deaths versus post-intervention 1 death. The overall financial cost of CDSS implementation was US$23,316, approximately US$1,060 per CDSS user trained. Of the total cost of implementation, 48% (US$11,272 was pre-intervention cost and intervention cost was 52% (US$12,044. Equipment costs accounted for the largest proportion of financial cost: 34% (US$7,917. When economic cost was considered, total cost of implementation was US$17,128-lower than the financial cost by 26.5%.The study provides useful information in the implementation of CDSS at health facilities to enhance health workers' adherence to practice guidelines and taking accurate decisions to

  8. Physicians’ use of computerized clinical decision supports to improve medication management in the elderly – the Seniors Medication Alert and Review Technology intervention

    Science.gov (United States)

    Alagiakrishnan, Kannayiram; Wilson, Patricia; Sadowski, Cheryl A; Rolfson, Darryl; Ballermann, Mark; Ausford, Allen; Vermeer, Karla; Mohindra, Kunal; Romney, Jacques; Hayward, Robert S

    2016-01-01

    Background Elderly people (aged 65 years or more) are at increased risk of polypharmacy (five or more medications), inappropriate medication use, and associated increased health care costs. The use of clinical decision support (CDS) within an electronic medical record (EMR) could improve medication safety. Methods Participatory action research methods were applied to preproduction design and development and postproduction optimization of an EMR-embedded CDS implementation of the Beers’ Criteria for medication management and the Cockcroft–Gault formula for estimating glomerular filtration rates (GFR). The “Seniors Medication Alert and Review Technologies” (SMART) intervention was used in primary care and geriatrics specialty clinics. Passive (chart messages) and active (order-entry alerts) prompts exposed potentially inappropriate medications, decreased GFR, and the possible need for medication adjustments. Physician reactions were assessed using surveys, EMR simulations, focus groups, and semi-structured interviews. EMR audit data were used to identify eligible patient encounters, the frequency of CDS events, how alerts were managed, and when evidence links were followed. Results Analysis of subjective data revealed that most clinicians agreed that CDS appeared at appropriate times during patient care. Although managing alerts incurred a modest time burden, most also agreed that workflow was not disrupted. Prevalent concerns related to clinician accountability and potential liability. Approximately 36% of eligible encounters triggered at least one SMART alert, with GFR alert, and most frequent medication warnings were with hypnotics and anticholinergics. Approximately 25% of alerts were overridden and ~15% elicited an evidence check. Conclusion While most SMART alerts validated clinician choices, they were received as valuable reminders for evidence-informed care and education. Data from this study may aid other attempts to implement Beers’ Criteria in

  9. Evaluation of selected environmental decision support software

    International Nuclear Information System (INIS)

    Sullivan, T.M.; Moskowitz, P.D.; Gitten, M.

    1997-06-01

    Decision Support Software (DSS) continues to be developed to support analysis of decisions pertaining to environmental management. Decision support systems are computer-based systems that facilitate the use of data, models, and structured decision processes in decision making. The optimal DSS should attempt to integrate, analyze, and present environmental information to remediation project managers in order to select cost-effective cleanup strategies. The optimal system should have a balance between the sophistication needed to address the wide range of complicated sites and site conditions present at DOE facilities, and ease of use (e.g., the system should not require data that is typically unknown and should have robust error checking of problem definition through input, etc.). In the first phase of this study, an extensive review of the literature, the Internet, and discussions with sponsors and developers of DSS led to identification of approximately fifty software packages that met the preceding definition

  10. A Hyperknowledge Framework of Decision Support Systems.

    Science.gov (United States)

    Chang, Ai-Mei; And Others

    1994-01-01

    Presents a hyperknowledge framework of decision support systems (DSS). This framework formalizes specifics about system functionality, representation of knowledge, navigation of the knowledge system, and user-interface traits as elements of a DSS environment that conforms closely to human cognitive processes in decision making. (Contains 52…

  11. Geospatial decision support systems for societal decision making

    Science.gov (United States)

    Bernknopf, R.L.

    2005-01-01

    While science provides reliable information to describe and understand the earth and its natural processes, it can contribute more. There are many important societal issues in which scientific information can play a critical role. Science can add greatly to policy and management decisions to minimize loss of life and property from natural and man-made disasters, to manage water, biological, energy, and mineral resources, and in general, to enhance and protect our quality of life. However, the link between science and decision-making is often complicated and imperfect. Technical language and methods surround scientific research and the dissemination of its results. Scientific investigations often are conducted under different conditions, with different spatial boundaries, and in different timeframes than those needed to support specific policy and societal decisions. Uncertainty is not uniformly reported in scientific investigations. If society does not know that data exist, what the data mean, where to use the data, or how to include uncertainty when a decision has to be made, then science gets left out -or misused- in a decision making process. This paper is about using Geospatial Decision Support Systems (GDSS) for quantitative policy analysis. Integrated natural -social science methods and tools in a Geographic Information System that respond to decision-making needs can be used to close the gap between science and society. The GDSS has been developed so that nonscientists can pose "what if" scenarios to evaluate hypothetical outcomes of policy and management choices. In this approach decision makers can evaluate the financial and geographic distribution of potential policy options and their societal implications. Actions, based on scientific information, can be taken to mitigate hazards, protect our air and water quality, preserve the planet's biodiversity, promote balanced land use planning, and judiciously exploit natural resources. Applications using the

  12. Whose decision is it anyway? How clinicians support decision-making participation after acquired brain injury.

    Science.gov (United States)

    Knox, Lucy; Douglas, Jacinta M; Bigby, Christine

    2013-01-01

    To raise professional awareness of factors that may influence the support offered by clinicians to people with acquired brain injury (ABI), and to consider the potential implications of these factors in terms of post-injury rehabilitation and living. A review of the literature was conducted to identify factors that determine how clinicians provide support and influence opportunities for individuals with ABI to participate in decision making across the rehabilitation continuum. Clinical case studies are used to highlight two specific issues: (1) hidden assumptions on the part of the practitioner, and (2) perceptions of risk operating in clinical practice. There are a range of factors which may influence the decision-making support provided by clinicians and, ultimately, shape lifetime outcomes for individuals with ABI. A multidimensional framework may assist clinicians to identify relevant factors and consider their potential implications including those that influence how clinicians involved in supporting decision making approach this task. Participation in decision making is an undisputed human right and central to the provision of person-centred care. Further research is required to understand how clinical practice can maximise both opportunities and support for increased decision-making participation by individuals with ABI. There is an increasing focus on the rights of all individuals to be supported to participate in decision making about their life. A number of changes associated with ABI mean that individuals with ABI will require support with decision making. Clinicians have a critical role in providing this support over the course of the rehabilitation continuum. Clinicians need to be aware of the range of factors that may influence the decision-making support they provide. A multidimensional framework may be used by clinicians to identify influences on the decision-making support they provide.

  13. Aggregate assessments support improved operational decision making

    International Nuclear Information System (INIS)

    Bauer, R.

    2003-01-01

    At Darlington Nuclear aggregate assessment of plant conditions is carried out in support of Operational Decision Making. This paper discusses how aggregate assessments have been applied to Operator Workarounds leading to improved prioritisation and alignment of work programs in different departments. As well, aggregate assessment of plant and human performance factors has been carried out to identify criteria which support conservative decision making in the main control room during unit transients. (author)

  14. Fault Isolation for Shipboard Decision Support

    DEFF Research Database (Denmark)

    Lajic, Zoran; Blanke, Mogens; Nielsen, Ulrik Dam

    2010-01-01

    Fault detection and fault isolation for in-service decision support systems for marine surface vehicles will be presented in this paper. The stochastic wave elevation and the associated ship responses are modeled in the frequency domain. The paper takes as an example fault isolation of a containe......Fault detection and fault isolation for in-service decision support systems for marine surface vehicles will be presented in this paper. The stochastic wave elevation and the associated ship responses are modeled in the frequency domain. The paper takes as an example fault isolation...... to the quality of decisions given to navigators....

  15. Solutions for decision support in university management

    Directory of Open Access Journals (Sweden)

    Andrei STANCIU

    2009-06-01

    Full Text Available The paper proposes an overview of decision support systems in order to define the role of a system to assist decision in university management. The authors present new technologies and the basic concepts of multidimensional data analysis using models of business processes within the universities. Based on information provided by scientific literature and on the authors’ experience, the study aims to define selection criteria in choosing a development environment for designing a support system dedicated to university management. The contributions consist in designing a data warehouse model and models of OLAP analysis to assist decision in university management.

  16. Supporting Informed Decision Making in Prevention of Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Constantino MARTINS

    2015-05-01

    Full Text Available Identifying and making the correct decision on the best health treatment or screening test option can become a difficult task. Therefore is important that the patients get all types of information appropriate to manage their health. Decision aids can be very useful when there is more than one reasonable option about a treatment or uncertain associated with screening tests. The decision aids tools help people to understand their clinical condition, through the description of the different options available. The purpose of this paper is to present the project “Supporting Informed Decision Making In Prevention of Prostate Cancer” (SIDEMP. This project is focused on the creation of a Web-based decision platform specifically directed to screening prostate cancer, that will support the patient in the process of making an informed decision

  17. Examining perceptions of the usefulness and usability of a mobile-based system for pharmacogenomics clinical decision support: a mixed methods study

    Directory of Open Access Journals (Sweden)

    Kathrin Blagec

    2016-02-01

    Full Text Available Background. Pharmacogenomic testing has the potential to improve the safety and efficacy of pharmacotherapy, but clinical application of pharmacogenetic knowledge has remained uncommon. Clinical Decision Support (CDS systems could help overcome some of the barriers to clinical implementation. The aim of this study was to evaluate the perception and usability of a web- and mobile-enabled CDS system for pharmacogenetics-guided drug therapy–the Medication Safety Code (MSC system–among potential users (i.e., physicians and pharmacists. Furthermore, this study sought to collect data on the practicability and comprehensibility of potential layouts of a proposed personalized pocket card that is intended to not only contain the machine-readable data for use with the MSC system but also human-readable data on the patient’s pharmacogenomic profile. Methods. We deployed an emergent mixed methods design encompassing (1 qualitative interviews with pharmacists and pharmacy students, (2 a survey among pharmacogenomics experts that included both qualitative and quantitative elements and (3 a quantitative survey among physicians and pharmacists. The interviews followed a semi-structured guide including a hypothetical patient scenario that had to be solved by using the MSC system. The survey among pharmacogenomics experts focused on what information should be printed on the card and how this information should be arranged. Furthermore, the MSC system was evaluated based on two hypothetical patient scenarios and four follow-up questions on the perceived usability. The second survey assessed physicians’ and pharmacists’ attitude towards the MSC system. Results. In total, 101 physicians, pharmacists and PGx experts coming from various relevant fields evaluated the MSC system. Overall, the reaction to the MSC system was positive across all investigated parameters and among all user groups. The majority of participants were able to solve the patient

  18. Evaluating Ethical Responsibility in Inverse Decision Support

    Directory of Open Access Journals (Sweden)

    Ahmad M. Kabil

    2012-01-01

    Full Text Available Decision makers have considerable autonomy on how they make decisions and what type of support they receive. This situation places the DSS analyst in a different relationship with the client than his colleagues who support regular MIS applications. This paper addresses an ethical dilemma in “Inverse Decision Support,” when the analyst supports a decision maker who requires justification for a preconceived selection that does not correspond to the best option that resulted from the professional resolution of the problem. An extended application of the AHP model is proposed for evaluating the ethical responsibility in selecting a suboptimal alternative. The extended application is consistent with the Inverse Decision Theory that is used extensively in medical decision making. A survey of decision analysts is used to assess their perspective of using the proposed extended application. The results show that 80% of the respondents felt that the proposed extended application is useful in business practices. 14% of them expanded the usability of the extended application to academic teaching of the ethics theory. The extended application is considered more usable in a country with a higher Transparency International Corruption Perceptions Index (TICPI than in a country with a lower one.

  19. Preparing for a decision support system.

    Science.gov (United States)

    Callan, K

    2000-08-01

    The increasing pressure to reduce costs and improve outcomes is driving the health care industry to view information as a competitive advantage. Timely information is required to help reduce inefficiencies and improve patient care. Numerous disparate operational or transactional information systems with inconsistent and often conflicting data are no longer adequate to meet the information needs of integrated care delivery systems and networks in competitive managed care environments. This article reviews decision support system characteristics and describes a process to assess the preparedness of an organization to implement and use decision support systems to achieve a more effective, information-based decision process. Decision support tools included in this article range from reports to data mining.

  20. Comparison of Overridden Medication-related Clinical Decision Support in the Intensive Care Unit between a Commercial System and a Legacy System.

    Science.gov (United States)

    Wong, Adrian; Wright, Adam; Seger, Diane L; Amato, Mary G; Fiskio, Julie M; Bates, David

    2017-08-23

    Electronic health records (EHRs) with clinical decision support (CDS) have shown to be effective at improving patient safety. Despite this, alerts delivered as part of CDS are overridden frequently, which is of concern in the critical care population as this group may have an increased risk of harm. Our organization recently transitioned from an internally-developed EHR to a commercial system. Data comparing various EHR systems, especially after transitions between EHRs, are needed to identify areas for improvement. To compare the two systems and identify areas for potential improvement with the new commercial system at a single institution. Overridden medication-related CDS alerts were included from October to December of the systems' respective years (legacy, 2011; commercial, 2015), restricted to three intensive care units. The two systems were compared with regards to CDS presentation and override rates for four types of CDS: drug-allergy, drug-drug interaction (DDI), geriatric and renal alerts. A post hoc analysis to evaluate for adverse drug events (ADEs) potentially resulting from overridden alerts was performed for 'contraindicated' DDIs via chart review. There was a significant increase in provider exposure to alerts and alert overrides in the commercial system (commercial: n=5,535; legacy: n=1,030). Rates of overrides were higher for the allergy and DDI alerts (pcommercial system. Geriatric and renal alerts were significantly different in incidence and presentation between the two systems. No ADEs were identified in an analysis of 43 overridden contraindicated DDI alerts. The vendor system had much higher rates of both alerts and overrides, although we did not find evidence of harm in a review of DDIs which were overridden. We propose recommendations for improving our current system which may be helpful to other similar institutions; improving both alert presentation and the underlying knowledge base appear important.

  1. Evaluation of AHRQ's on-time pressure ulcer prevention program: a facilitator-assisted clinical decision support intervention for nursing homes.

    Science.gov (United States)

    Olsho, Lauren E W; Spector, William D; Williams, Christianna S; Rhodes, William; Fink, Rebecca V; Limcangco, Rhona; Hurd, Donna

    2014-03-01

    Pressure ulcers present serious health and economic consequences for nursing home residents. The Agency for Healthcare Research & Quality, in partnership with the New York State Department of Health, implemented the pressure ulcer module of On-Time Quality Improvement for Long Term Care (On-Time), a clinical decision support intervention to reduce pressure ulcer incidence rates. To evaluate the effectiveness of the On-Time program in reducing the rate of in-house-acquired pressure ulcers among nursing home residents. We employed an interrupted time-series design to identify impacts of 4 core On-Time program components on resident pressure ulcer incidence in 12 New York State nursing homes implementing the intervention (n=3463 residents). The sample was purposively selected to include nursing homes with high baseline prevalence and incidence of pressure ulcers and high motivation to reduce pressure ulcers. Differential timing and sequencing of 4 core On-Time components across intervention nursing homes and units enabled estimation of separate impacts for each component. Inclusion of a nonequivalent comparison group of 13 nursing homes not implementing On-Time (n=2698 residents) accounts for potential mean-reversion bias. Impacts were estimated via a random-effects Poisson model including resident-level and facility-level covariates. We find a large and statistically significant reduction in pressure ulcer incidence associated with the joint implementation of 4 core On-Time components (incidence rate ratio=0.409; P=0.035). Impacts vary with implementation of specific component combinations. On-Time implementation is associated with sizable reductions in pressure ulcer incidence.

  2. Context based support for Clinical Reasoning

    DEFF Research Database (Denmark)

    Vilstrup Pedersen, Klaus

    2004-01-01

    In many areas of the medical domain, the decision process i.e. reasoning, involving health care professionals is distributed, cooperative and complex. Computer based decision support systems has usually been focusing on the outcome of the decision making and treated it as a single task....... In this paper a framework for a Clinical Reasoning Knowledge Warehouse (CRKW) is presented, intended to support the reasoning process, by providing the decision participants with an analysis platform that captures and enhances information and knowledge. The CRKW mixes theories and models from Artificial...... Intelligence, Knowledge Management Systems and Business Intelligence to make context sensitive, patient case specific analysis and knowledge management. The knowledge base consists of patient health records, reasoning process information and clinical guidelines. Patient specific information and knowledge...

  3. Formalisation for decision support in anaesthesiology

    NARCIS (Netherlands)

    Renardel de Lavalette, G R; Groenboom, R.; Rotterdam, E; van Harmelen, F; ten Teije, A; de Geus, F.

    1997-01-01

    This paper reports on research for decision support for anaesthesiologists at the University Hospital in Groningen, the Netherlands. Based on CAROLA, an existing automated operation documentation system, we designed a support environment that will assist in real-time diagnosis. The core of the work

  4. Integrated Clinical Decision Support Systems Promote Absolute Cardiovascular Risk Assessment: An Important Primary Prevention Measure in Aboriginal and Torres Strait Islander Primary Health Care

    Directory of Open Access Journals (Sweden)

    Veronica Matthews

    2017-09-01

    Full Text Available BackgroundAboriginal and Torres Strait Islander Australians experience a greater burden of disease compared to non-Indigenous Australians. Around one-fifth of the health disparity is caused by cardiovascular disease (CVD. Despite the importance of absolute cardiovascular risk assessment (CVRA as a screening and early intervention tool, few studies have reported its use within the Australian Indigenous primary health care (PHC sector. This study utilizes data from a large-scale quality improvement program to examine variation in documented CVRA as a primary prevention strategy for individuals without prior CVD across four Australian jurisdictions. We also examine the proportion with elevated risk and follow-up actions recorded.MethodsWe undertook cross-sectional analysis of 2,052 client records from 97 PHC centers to assess CVRA in Indigenous adults aged ≥20 years with no recorded chronic disease diagnosis (2012–2014. Multilevel regression was used to quantify the variation in CVRA attributable to health center and client level factors. The main outcome measure was the proportion of eligible adults who had CVRA recorded. Secondary outcomes were the proportion of clients with elevated risk that had follow-up actions recorded.ResultsApproximately 23% (n = 478 of eligible clients had documented CVRA. Almost all assessments (99% were conducted in the Northern Territory. Within this jurisdiction, there was wide variation between centers in the proportion of clients with documented CVRA (median 38%; range 0–86%. Regression analysis showed health center factors accounted for 48% of the variation. Centers with integrated clinical decision support systems were more likely to document CVRA (OR 21.1; 95% CI 5.4–82.4; p < 0.001. Eleven percent (n = 53 of clients were found with moderate/high CVD risk, of whom almost one-third were under 35 years (n = 16. Documentation of follow-up varied with respect to the targeted risk factor

  5. Heuristics in Managing Complex Clinical Decision Tasks in Experts' Decision Making.

    Science.gov (United States)

    Islam, Roosan; Weir, Charlene; Del Fiol, Guilherme

    2014-09-01

    Clinical decision support is a tool to help experts make optimal and efficient decisions. However, little is known about the high level of abstractions in the thinking process for the experts. The objective of the study is to understand how clinicians manage complexity while dealing with complex clinical decision tasks. After approval from the Institutional Review Board (IRB), three clinical experts were interviewed the transcripts from these interviews were analyzed. We found five broad categories of strategies by experts for managing complex clinical decision tasks: decision conflict, mental projection, decision trade-offs, managing uncertainty and generating rule of thumb. Complexity is created by decision conflicts, mental projection, limited options and treatment uncertainty. Experts cope with complexity in a variety of ways, including using efficient and fast decision strategies to simplify complex decision tasks, mentally simulating outcomes and focusing on only the most relevant information. Understanding complex decision making processes can help design allocation based on the complexity of task for clinical decision support design.

  6. Using Visualization in Cockpit Decision Support Systems

    Science.gov (United States)

    Aragon, Cecilia R.

    2005-01-01

    In order to safely operate their aircraft, pilots must make rapid decisions based on integrating and processing large amounts of heterogeneous information. Visual displays are often the most efficient method of presenting safety-critical data to pilots in real time. However, care must be taken to ensure the pilot is provided with the appropriate amount of information to make effective decisions and not become cognitively overloaded. The results of two usability studies of a prototype airflow hazard visualization cockpit decision support system are summarized. The studies demonstrate that such a system significantly improves the performance of helicopter pilots landing under turbulent conditions. Based on these results, design principles and implications for cockpit decision support systems using visualization are presented.

  7. Decision support system for surface irrigation design

    OpenAIRE

    Gonçalves, José M.; Pereira, L.S.

    2009-01-01

    The SADREG decision support system was developed to help decision makers in the process of design and selection of farm surface irrigation systems to respond to requirements of modernization of surface irrigation—furrow, basin, and border irrigation. It includes a database, simulation models, user-friendly interfaces, and multicriteria analysis models. SADREG is comprised of two components: design and selection. The first component applies database information, and through several si...

  8. Biometric and intelligent decision making support

    CERN Document Server

    Kaklauskas, Arturas

    2015-01-01

    This book presents different methods for analyzing the body language (movement, position, use of personal space, silences, pauses and tone, the eyes, pupil dilation or constriction, smiles, body temperature and the like) for better understanding people’s needs and actions, including biometric data gathering and reading. Different studies described in this book indicate that sufficiently much data, information and knowledge can be gained by utilizing biometric technologies. This is the first, wide-ranging book that is devoted completely to the area of intelligent decision support systems, biometrics technologies and their integrations. This book is designated for scholars, practitioners and doctoral and master’s degree students in various areas and those who are interested in the latest biometric and intelligent decision making support problems and means for their resolutions, biometric and intelligent decision making support systems and the theory and practice of their integration and the opportunities fo...

  9. Computerised decision support systems for healthcare professionals: an interpretative review.

    Science.gov (United States)

    Cresswell, Kathrin; Majeed, Azeem; Bates, David W; Sheikh, Aziz

    2012-01-01

    Computerised decision support systems are designed to support clinicians in making decisions and thereby enhance the quality and safety of care. We aimed to undertake an interpretative review of the empirical evidence on computerised decision support systems, their contexts of use, and summarise evidence on the effectiveness of these tools and insights into how these can be successfully implemented and adopted. We systematically searched the empirical literature to identify systematic literature reviews on computerised decision support applications and their impact on the quality and safety of healthcare delivery over a 13-year period (1997-2010). The databases searched included: MEDLINE, EMBASE, The Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, The Cochrane Central Register of Controlled Trials, The Cochrane Methodology Register, The Health Technology Assessment Database, and The National Health Service (NHS) Economic Evaluation Database. To be eligible for inclusion, systematic reviews needed to address computerised decision support systems, and at least one of the following: impact on safety; quality; or organisational, implementation or adoption considerations. Our searches yielded 121 systematic reviews relating to eHealth, of which we identified 41 as investigating computerised decision support systems. These indicated that, whilst there was a lack of investigating potential risks, such tools can result in improvements in practitioner performance in the promotion of preventive care and guideline adherence, particularly if specific information is available in real time and systems are effectively integrated into clinical workflows. However, the evidence regarding impact on patient outcomes was less clear-cut with reviews finding either no, inconsistent or modest benefits. Whilst the potential of clinical decision support systems in improving, in particular, practitioner performance is considerable, such technology may

  10. Relational Algebra in Spatial Decision Support Systems Ontologies.

    Science.gov (United States)

    Diomidous, Marianna; Chardalias, Kostis; Koutonias, Panagiotis; Magnita, Adrianna; Andrianopoulos, Charalampos; Zimeras, Stelios; Mechili, Enkeleint Aggelos

    2017-01-01

    Decision Support Systems (DSS) is a powerful tool, for facilitates researchers to choose the correct decision based on their final results. Especially in medical cases where doctors could use these systems, to overcome the problem with the clinical misunderstanding. Based on these systems, queries must be constructed based on the particular questions that doctors must answer. In this work, combination between questions and queries would be presented via relational algebra.

  11. Cost of installing and operating an electronic clinical decision support system for maternal health care: case of Tanzania rural primary health centres.

    Science.gov (United States)

    Saronga, Happiness Pius; Dalaba, Maxwell Ayindenaba; Dong, Hengjin; Leshabari, Melkizedeck; Sauerborn, Rainer; Sukums, Felix; Blank, Antje; Kaltschmidt, Jens; Loukanova, Svetla

    2015-04-02

    Poor quality of care is among the causes of high maternal and newborn disease burden in Tanzania. Potential reason for poor quality of care is the existence of a "know-do gap" where by health workers do not perform to the best of their knowledge. An electronic clinical decision support system (CDSS) for maternal health care was piloted in six rural primary health centers of Tanzania to improve performance of health workers by facilitating adherence to World Health Organization (WHO) guidelines and ultimately improve quality of maternal health care. This study aimed at assessing the cost of installing and operating the system in the health centers. This retrospective study was conducted in Lindi, Tanzania. Costs incurred by the project were analyzed using Ingredients approach. These costs broadly included vehicle, computers, furniture, facility, CDSS software, transport, personnel, training, supplies and communication. These were grouped into installation and operation cost; recurrent and capital cost; and fixed and variable cost. We assessed the CDSS in terms of its financial and economic cost implications. We also conducted a sensitivity analysis on the estimations. Total financial cost of CDSS intervention amounted to 185,927.78 USD. 77% of these costs were incurred in the installation phase and included all the activities in preparation for the actual operation of the system for client care. Generally, training made the largest share of costs (33% of total cost and more than half of the recurrent cost) followed by CDSS software- 32% of total cost. There was a difference of 31.4% between the economic and financial costs. 92.5% of economic costs were fixed costs consisting of inputs whose costs do not vary with the volume of activity within a given range. Economic cost per CDSS contact was 52.7 USD but sensitive to discount rate, asset useful life and input cost variations. Our study presents financial and economic cost estimates of installing and operating an

  12. System for selecting relevant information for decision support.

    Science.gov (United States)

    Kalina, Jan; Seidl, Libor; Zvára, Karel; Grünfeldová, Hana; Slovák, Dalibor; Zvárová, Jana

    2013-01-01

    We implemented a prototype of a decision support system called SIR which has a form of a web-based classification service for diagnostic decision support. The system has the ability to select the most relevant variables and to learn a classification rule, which is guaranteed to be suitable also for high-dimensional measurements. The classification system can be useful for clinicians in primary care to support their decision-making tasks with relevant information extracted from any available clinical study. The implemented prototype was tested on a sample of patients in a cardiological study and performs an information extraction from a high-dimensional set containing both clinical and gene expression data.

  13. Patients' Values in Clinical Decision-Making.

    Science.gov (United States)

    Faggion, Clovis Mariano; Pachur, Thorsten; Giannakopoulos, Nikolaos Nikitas

    2017-09-01

    Shared decision-making involves the participation of patient and dental practitioner. Well-informed decision-making requires that both parties understand important concepts that may influence the decision. This fourth article in a series of 4 aims to discuss the importance of patients' values when a clinical decision is made. We report on how to incorporate important concepts for well-informed, shared decision-making. Here, we present patient values as an important issue, in addition to previously established topics such as the risk of bias of a study, cost-effectiveness of treatment approaches, and a comparison of therapeutic benefit with potential side effects. We provide 2 clinical examples and suggestions for a decision tree, based on the available evidence. The information reported in this article may improve the relationship between patient and dental practitioner, resulting in more well-informed clinical decisions. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. The Pedagogical Reflection Model - an educational perspective on clinical decisions

    DEFF Research Database (Denmark)

    Voergaard Poulsen, Bettina; Vibholm Persson, Stine; Skriver, Mette

    Clinical decision-making is important in patient-centred nursing, which is known in nursing education and research (1) The Pedagogical Reflection Model (PRM) can provide a framework that supports students’ decision-making in patient-specific situations. PRM is based on the assumption that clinical......) The aims of this study were to explore how nurse students and clinical supervisors use PRM as method to reflect before, during and after PRM guidance in relation to clinical decisions in the first year of clinical practice...... decision-making needs to take into account; 1) clinical experiences, 2) the perspective of the patient, 3) clinical observations and investigations, 4) knowledge about patients experiences of being a patient and ill, 5) medical knowledge about diseases, and 6) the organizational framework (2,3,4)(Figure 1...

  15. Decision support frameworks and tools for conservation

    Science.gov (United States)

    Schwartz, Mark W.; Cook, Carly N.; Pressey, Robert L.; Pullin, Andrew S.; Runge, Michael C.; Salafsky, Nick; Sutherland, William J.; Williamson, Matthew A.

    2018-01-01

    The practice of conservation occurs within complex socioecological systems fraught with challenges that require transparent, defensible, and often socially engaged project planning and management. Planning and decision support frameworks are designed to help conservation practitioners increase planning rigor, project accountability, stakeholder participation, transparency in decisions, and learning. We describe and contrast five common frameworks within the context of six fundamental questions (why, who, what, where, when, how) at each of three planning stages of adaptive management (project scoping, operational planning, learning). We demonstrate that decision support frameworks provide varied and extensive tools for conservation planning and management. However, using any framework in isolation risks diminishing potential benefits since no one framework covers the full spectrum of potential conservation planning and decision challenges. We describe two case studies that have effectively deployed tools from across conservation frameworks to improve conservation actions and outcomes. Attention to the critical questions for conservation project planning should allow practitioners to operate within any framework and adapt tools to suit their specific management context. We call on conservation researchers and practitioners to regularly use decision support tools as standard practice for framing both practice and research.

  16. Modem: data exchange among decision support systems

    International Nuclear Information System (INIS)

    Baig, S.; Zaehringer, M.

    2003-01-01

    The aim of the European Research and Development project MODEM (Monitoring Data and Information Exchange Among Decision Support Systems) is to achieve practical improvements for data exchange among decision support systems (DSS). Hence, the results of model calculations become comparable. This is a precondition for harmonised decision making. Based on the analysis of existing procedures, it was decided to use the PUSH-PULL concept. Notifications are actively and automatically sent by the DSS (PUSH). The data can then be downloaded form an in-formation server (PULL). The format of the data is defined in XML (extended markup language). Participants of the project are the DSS: RODOS, ARGOS and RECASS. First, the data is comprised of the source term and meteorological information. Results of the prognoses and measurement data are also to be exchanged. Exercises testing and improving the pro-cedures form an integral part of the project. (orig.)

  17. Legal Considerations in Clinical Decision Making.

    Science.gov (United States)

    Ursu, Samuel C.

    1992-01-01

    Discussion of legal issues in dental clinical decision making looks at the nature and elements of applicable law, especially malpractice, locus of responsibility, and standards of care. Greater use of formal decision analysis in clinical dentistry and better research on diagnosis and treatment are recommended, particularly in light of increasing…

  18. Healthcare performance turned into decision support

    DEFF Research Database (Denmark)

    Sørup, Christian Michel; Jacobsen, Peter

    2013-01-01

    from the healthcare sector, the results obtained could be restricted to this sector. Inclusion of data from Arbejdsmarkedets Tillægspension (ATP) showed no deviation from the results in the healthcare sector. Practical implications – The product of the study is a decision support tool for leaders...

  19. Development of an ecological decision support system

    NARCIS (Netherlands)

    van Beusekom, Frits; Brazier, Frances; Schipper, Piet; Treur, Jan; del Pobil, A.P.

    1998-01-01

    In this paper a knowledge-based decision support system is described that determines the abiotic (chemical and physical) characteristics of a site on the basis of in-homogeneous samples of plant species. Techniques from the area of non-monotonic reasoning are applied to model multi-interpretable

  20. Modeling uncertainty in requirements engineering decision support

    Science.gov (United States)

    Feather, Martin S.; Maynard-Zhang, Pedrito; Kiper, James D.

    2005-01-01

    One inherent characteristic of requrements engineering is a lack of certainty during this early phase of a project. Nevertheless, decisions about requirements must be made in spite of this uncertainty. Here we describe the context in which we are exploring this, and some initial work to support elicitation of uncertain requirements, and to deal with the combination of such information from multiple stakeholders.

  1. A Computer Prescribing Order Entry-Clinical Decision Support system designed for neonatal care: results of the 'preselected prescription' concept at the bedside.

    Science.gov (United States)

    Gouyon, B; Iacobelli, S; Saliba, E; Quantin, C; Pignolet, A; Jacqz-Aigrain, E; Gouyon, J B

    2017-02-01

    The neonatal intensive care units (NICUs) are at the highest risk of drug dose error of all hospital wards. NICUs also have the most complicated prescription modalities. The computerization of the prescription process is currently recommended to decrease the risk of preventable adverse drug effects (pADEs) in NICUs. However, Computer Prescribing Order Entry-Clinical Decision Support (C.P.O.E./C.D.S.) systems have been poorly studied in NICUs, and their technical compatibility with neonatal specificities has been limited. We set up a performance study of the preselected prescription of drugs for neonates, which limited the role of the prescriber to choosing the drugs and their indications. A single 29 bed neonatal ward used this neonatal C.P.O.E./C.D.S. system for all prescriptions of all hospitalized newborns over an 18-month period. The preselected prescription of drugs was based on the indication, gestational age, body weight and post-natal age. The therapeutic protocols were provided by a formulary reference (330 drugs) that had been specifically designed for newborns. The preselected prescription also gave complete information about preparation and administration of drugs by nurses. The prescriber was allowed to modify the preselected prescription but alarms provided warning when the prescription was outside the recommended range. The main clinical characteristics and all items of each line of prescription were stored in a data warehouse, thus enabling this study to take place. Seven hundred and sixty successive newborns (from 24 to 42 weeks' gestation) were prescribed 52 392 lines of prescription corresponding to 65 drugs; About 30·4% of neonates had at least one out of licensed prescription; A prescription out of the recommended range for daily dose was recorded for 1·0% of all drug prescriptions. WHAT IS NEW?: The C.P.O.E./C.D.S. systems can currently provide a complete preselected prescription in NICUs according to dose rules, which are specific to

  2. Decision analysis in the clinical neurosciences

    NARCIS (Netherlands)

    D.W.J. Dippel (Diederik)

    1994-01-01

    textabstractDiagnostic and therapeutic choice in neurology can fortunately be made without formal decision support in the majority of cases. in many patients a diagnosis and treatment choice are relatively easy to establish. This study however, concerns the application of a decision support

  3. ADDIS: A decision support system for evidence-based medicine

    NARCIS (Netherlands)

    G. van Valkenhoef (Gert); T. Tervonen (Tommi); T. Zwinkels (Tijs); B. de Brock (Bert); H.L. Hillege (Hans)

    2013-01-01

    textabstractClinical trials are the main source of information for the efficacy and safety evaluation of medical treatments. Although they are of pivotal importance in evidence-based medicine, there is a lack of usable information systems providing data-analysis and decision support capabilities for

  4. Text summarization as a decision support aid

    Directory of Open Access Journals (Sweden)

    Workman T

    2012-05-01

    Full Text Available Abstract Background PubMed data potentially can provide decision support information, but PubMed was not exclusively designed to be a point-of-care tool. Natural language processing applications that summarize PubMed citations hold promise for extracting decision support information. The objective of this study was to evaluate the efficiency of a text summarization application called Semantic MEDLINE, enhanced with a novel dynamic summarization method, in identifying decision support data. Methods We downloaded PubMed citations addressing the prevention and drug treatment of four disease topics. We then processed the citations with Semantic MEDLINE, enhanced with the dynamic summarization method. We also processed the citations with a conventional summarization method, as well as with a baseline procedure. We evaluated the results using clinician-vetted reference standards built from recommendations in a commercial decision support product, DynaMed. Results For the drug treatment data, Semantic MEDLINE enhanced with dynamic summarization achieved average recall and precision scores of 0.848 and 0.377, while conventional summarization produced 0.583 average recall and 0.712 average precision, and the baseline method yielded average recall and precision values of 0.252 and 0.277. For the prevention data, Semantic MEDLINE enhanced with dynamic summarization achieved average recall and precision scores of 0.655 and 0.329. The baseline technique resulted in recall and precision scores of 0.269 and 0.247. No conventional Semantic MEDLINE method accommodating summarization for prevention exists. Conclusion Semantic MEDLINE with dynamic summarization outperformed conventional summarization in terms of recall, and outperformed the baseline method in both recall and precision. This new approach to text summarization demonstrates potential in identifying decision support data for multiple needs.

  5. The conceptual foundation of environmental decision support.

    Science.gov (United States)

    Reichert, Peter; Langhans, Simone D; Lienert, Judit; Schuwirth, Nele

    2015-05-01

    Environmental decision support intends to use the best available scientific knowledge to help decision makers find and evaluate management alternatives. The goal of this process is to achieve the best fulfillment of societal objectives. This requires a careful analysis of (i) how scientific knowledge can be represented and quantified, (ii) how societal preferences can be described and elicited, and (iii) how these concepts can best be used to support communication with authorities, politicians, and the public in environmental management. The goal of this paper is to discuss key requirements for a conceptual framework to address these issues and to suggest how these can best be met. We argue that a combination of probability theory and scenario planning with multi-attribute utility theory fulfills these requirements, and discuss adaptations and extensions of these theories to improve their application for supporting environmental decision making. With respect to (i) we suggest the use of intersubjective probabilities, if required extended to imprecise probabilities, to describe the current state of scientific knowledge. To address (ii), we emphasize the importance of value functions, in addition to utilities, to support decisions under risk. We discuss the need for testing "non-standard" value aggregation techniques, the usefulness of flexibility of value functions regarding attribute data availability, the elicitation of value functions for sub-objectives from experts, and the consideration of uncertainty in value and utility elicitation. With respect to (iii), we outline a well-structured procedure for transparent environmental decision support that is based on a clear separation of scientific prediction and societal valuation. We illustrate aspects of the suggested methodology by its application to river management in general and with a small, didactical case study on spatial river rehabilitation prioritization. Copyright © 2015 The Authors. Published by

  6. Decision support tools for policy and planning

    International Nuclear Information System (INIS)

    Jacyk, P.; Schultz, D.; Spangenberg, L.

    1995-01-01

    A decision support system (DSS) is being developed at the Radioactive Liquid Waste Treatment Facility, Los Alamos National Laboratory (LANL). The DSS will be used to evaluate alternatives for improving LANL's existing central radioactive waste water treatment plant and to evaluate new site-wide liquid waste treatment schemes that are required in order to handle the diverse waste streams produced at LANL. The decision support system consists of interacting modules that perform the following tasks: rigorous process simulation, configuration management, performance analysis, cost analysis, risk analysis, environmental impact assessment, transportation modeling, and local, state, and federal regulation compliance checking. Uncertainty handling techniques are used with these modules and also with a decision synthesis module which combines results from the modules listed above. We believe the DSS being developed can be applied to almost any other industrial water treatment facility with little modification because in most situations the waste streams are less complex, fewer regulations apply, and the political environment is simpler. The techniques being developed are also generally applicable to policy and planning decision support systems in the chemical process industry

  7. Tsunami early warning and decision support

    Directory of Open Access Journals (Sweden)

    T. Steinmetz

    2010-09-01

    Full Text Available An innovative newly developed modular and standards based Decision Support System (DSS is presented which forms part of the German Indonesian Tsunami Early Warning System (GITEWS. The GITEWS project stems from the effort to implement an effective and efficient Tsunami Early Warning and Mitigation System for the coast of Indonesia facing the Sunda Arc along the islands of Sumatra, Java and Bali. The geological setting along an active continental margin which is very close to densely populated areas is a particularly difficult one to cope with, because potential tsunamis' travel times are thus inherently short. National policies require an initial warning to be issued within the first five minutes after an earthquake has occurred. There is an urgent requirement for an end-to-end solution where the decision support takes the entire warning chain into account. The system of choice is based on pre-computed scenario simulations and rule-based decision support which is delivered to the decision maker through a sophisticated graphical user interface (GUI using information fusion and fast information aggregation to create situational awareness in the shortest time possible. The system also contains risk and vulnerability information which was designed with the far end of the warning chain in mind – it enables the decision maker to base his acceptance (or refusal of the supported decision also on regionally differentiated risk and vulnerability information (see Strunz et al., 2010. While the system strives to provide a warning as quickly as possible, it is not in its proper responsibility to send and disseminate the warning to the recipients. The DSS only broadcasts its messages to a dissemination system (and possibly any other dissemination system which is operated under the responsibility of BMKG – the meteorological, climatological and geophysical service of Indonesia – which also hosts the tsunami early warning center. The system is to be seen

  8. Supporting multi-stakeholder environmental decisions.

    Science.gov (United States)

    Hajkowicz, Stefan A

    2008-09-01

    This paper examines how multiple criteria analysis (MCA) can be used to support multi-stakeholder environmental management decisions. It presents a study through which 48 stakeholders from environmental, primary production and community interest groups used MCA to prioritise 30 environmental management problems in the Mackay-Whitsunday region of Queensland, Australia. The MCA model, with procedures for aggregating multi-stakeholder output, was used to inform a final decision on the priority of the region's environmental management problems. The result was used in the region's environmental management plan as required under Australia's Natural Heritage Trust programme. The study shows how relatively simple MCA methods can help stakeholders make group decisions, even when they hold strongly conflicting preferences.

  9. Decision Strategy Research and Policy Support

    International Nuclear Information System (INIS)

    Hardeman, F.

    2002-01-01

    The objective of SCK-CEN's R and D programme on decision strategies and policy support is: (1) to investigate the decision making process, with all its relevant dimensions, in the context of radiation protection or other nuclear issues (with particular emphasis on emergency preparedness); (2) to disseminate knowledge on decision making and nuclear emergencies, including the organisation of training courses, the contribution to manuals or guidelines, the participation in working groups or discussion forums; (3) to assist the authorities and the industry on any topic related to radiation protection and to make expertise and infrastructure available; (4) to participate in and contribute to initiatives related to social sciences and their implementation into SCK-CEN; (5) to co-ordinate efforts of SCK-CEN related to medical applications of ionising radiation. Principal achievements in 2001 are described

  10. Decision Strategy Research and Policy Support

    Energy Technology Data Exchange (ETDEWEB)

    Hardeman, F

    2002-04-01

    The objective of SCK-CEN's R and D programme on decision strategies and policy support is: (1) to investigate the decision making process, with all its relevant dimensions, in the context of radiation protection or other nuclear issues (with particular emphasis on emergency preparedness); (2) to disseminate knowledge on decision making and nuclear emergencies, including the organisation of training courses, the contribution to manuals or guidelines, the participation in working groups or discussion forums; (3) to assist the authorities and the industry on any topic related to radiation protection and to make expertise and infrastructure available; (4) to participate in and contribute to initiatives related to social sciences and their implementation into SCK-CEN; (5) to co-ordinate efforts of SCK-CEN related to medical applications of ionising radiation. Principal achievements in 2001 are described.

  11. Spill operation system decision support system

    International Nuclear Information System (INIS)

    Clark, R.

    1992-01-01

    The MSRC Spill Operation System (SOS) is a tool for the support of decision-making at the time of a catastrophic oil spill. SOS provides MSRC decision-makers with access to information about the source of the spill, the spill environment, and the availability of spill response resources. This system is designed to meet the information needs of a Response Supervisor, an Environmental Advisor, Logistics/Maintenance Supervisor, Operations Supervisor, and the MSRC Regional General Manager. The SOS project Objectives are: (1) integrate currently available data, systems, and technologies; (2) develop an application that effectively supports mobilized operations and can be adapted to support normal operations; (3) ensure that the development of computer applications is driven by user needs and not by technology; and (4) coordinate with government and other industry organizations to avoid duplication of effort. Design Objectives for SOS are: (1) centralize management information storage while decentralizing decision making capabilities; (2) boost User confidence by providing a system that is easy to learn, easy to use, and is open-quotes Sailor Proofclose quotes; and (3) use visualization technology in providing spill related information. This approach includes the use of Geographic Information System (GIS) technology for maps and geographically associated resource; and support MSRC's concept of operation which includes - a swift notification of response personnel; fast mobilization of response resources; and accurate tracking of resources during a spill. MSRC is organized into five responsibility regions

  12. Temporal reasoning for decision support in medicine.

    Science.gov (United States)

    Augusto, Juan Carlos

    2005-01-01

    Handling time-related concepts is essential in medicine. During diagnosis it can make a substantial difference to know the temporal order in which some symptoms occurred or for how long they lasted. During prognosis the potential evolutions of a disease are conceived as a description of events unfolding in time. In therapy planning the different steps of treatment must be applied in a precise order, with a given frequency and for a certain span of time in order to be effective. This article offers a survey on the use of temporal reasoning for decision support-related tasks in medicine. Key publications of the area, mainly circumscribed to the latest two decades, are reviewed and classified according to three important stages of patient treatment requiring decision support: diagnosis, prognosis and therapy planning/management. Other complementary publications, like those on time-centered information storage and retrieval, are also considered as they provide valuable support to the above mentioned three stages. Key areas are highlighted and used to organize the latest contributions. The survey of previous research is followed by an analysis of what can still be improved and what is needed to make the next generation of decision support systems for medicine more effective. It can be observed that although the area has been considerably developed, there are still areas where more research is needed to make time-based systems of widespread use in decision support-related areas of medicine. Several suggestions for further exploration are proposed as a result of the survey.

  13. Recommendations on future development of decision support systems

    DEFF Research Database (Denmark)

    MCarthur, Stephen; Chen, Minjiang; Marinelli, Mattia

    Deliverable 8.3 reports on the consolidation of experiences from visualisation, decision support prototypes experiments and recommendations on future developments of decision support systems......Deliverable 8.3 reports on the consolidation of experiences from visualisation, decision support prototypes experiments and recommendations on future developments of decision support systems...

  14. Computer-supported collaborative decision-making

    CERN Document Server

    Filip, Florin Gheorghe; Ciurea, Cristian

    2017-01-01

    This is a book about how management and control decisions are made by persons who collaborate and possibly use the support of an information system. The decision is the result of human conscious activities aiming at choosing a course of action for attaining a certain objective (or a set of objectives). The act of collaboration implies that several entities who work together and share responsibilities to jointly plan, implement and evaluate a program of activities to achieve the common goals. The book is intended to present a balanced view of the domain to include both well-established concepts and a selection of new results in the domains of methods and key technologies. It is meant to answer several questions, such as: a) “How are evolving the business models towards the ever more collaborative schemes?”; b) “What is the role of the decision-maker in the new context?” c) “What are the basic attributes and trends in the domain of decision-supporting information systems?”; d) “Which are the basic...

  15. Knowledge representation for decision support systems

    International Nuclear Information System (INIS)

    Methlie, L.B.

    1985-01-01

    This book is organized into three sections in accordance with the structure of the conference program. First section contains four major papers which were commissioned by the Programme Committee to set the tone for the conference and to provide a structured source of relevant material from contributing disciplines. The second section contains specific papers submitted to the conference, and concerned with the following topics of specific interest: epistemological issues for decision support systems (DSS), capturing organizational knowledge for DSS, complementarity between human and formal DSS, and representations for adaption. The third section contains the short papers on any topic of relevance to the theme of the conference. It is hoped that the two working conferences organized by WG 8.3 will contribute to the development of a coherent knowledge and understanding of the class of computerized information systems called Decision Support Systems. (Auth.)

  16. Decision Support System for Fighter Pilots

    DEFF Research Database (Denmark)

    Randleff, Lars Rosenberg

    2007-01-01

    During a mission over enemy territory a fighter aircraft may be engaged by ground based threats. The pilot can use different measures to avoid the aircraft from being detected by e.g. enemy radar systems. If the enemy detects the aircraft a missile may be fired to seek and destroy the aircraft...... and countermeasures that can be applied to mitigate threats. This work is concerned with finding proper evasive actions when a fighter aircraft is engaged by ground based threats. To help the pilot in deciding on these actions a decision support system may be implemented. The environment in which such a system must....... When new threats occur the decision support system must be able to provide suggestions within a fraction of a second. Since the time it takes to find an optimal solution to the mathematical model can not comply with this requirement solutions are sought using a metaheuristic....

  17. Decision support system to select cover systems

    International Nuclear Information System (INIS)

    Bostick, K.V.

    1995-01-01

    The objective of this technology is to provide risk managers with a defensible, objective way to select capping alternatives for remediating radioactive and mixed waste landfills. The process of selecting containment cover technologies for mixed waste landfills requires consideration of many complex and interrelated technical, regulatory, and economic issues. A Decision Support System (DSS) is needed to integrate the knowledge of experts from scientific, engineering, and management disciplines to help in selecting the best capping practice for the site

  18. A decision support system for forensic entomology

    OpenAIRE

    Morvan , Gildas; Jolly , Daniel; Dupont , Daniel; Kubiak , Philippe

    2007-01-01

    International audience; This paper presents a multiagent-based model of insect development on a dead body and a three layers Decision Support System architecture able to perform retrodictive (abductive) reasoning from multiagent-based models or more generally, complex systems models. This architecture is used in order to compute post-mortem intervals from entomological data sampled on cadavers. Knowing the exact time of a death is fundamental in criminal investigations. Thus, it is necessary ...

  19. Personalized Clinical Decision Making in Gastrointestinal Malignancies

    DEFF Research Database (Denmark)

    Hess, Søren; Bjerring, Ole Steen; Pfeiffer, Per

    2016-01-01

    and initial stages. This article outlines the potential use of fluorodeoxyglucose-PET/CT in clinical decision making with special regard to preoperative evaluation and response assessment in gastric cancer (including the gastroesophageal junction), pancreatic cancer (excluding neuroendocrine tumors...

  20. An exploration of clinical decision making in mental health triage.

    Science.gov (United States)

    Sands, Natisha

    2009-08-01

    Mental health (MH) triage is a specialist area of clinical nursing practice that involves complex decision making. The discussion in this article draws on the findings of a Ph.D. study that involved a statewide investigation of the scope of MH triage nursing practice in Victoria, Australia. Although the original Ph.D. study investigated a number of core practices in MH triage, the focus of the discussion in this article is specifically on the findings related to clinical decision making in MH triage, which have not previously been published. The study employed an exploratory descriptive research design that used mixed data collection methods including a survey questionnaire (n = 139) and semistructured interviews (n = 21). The study findings related to decision making revealed a lack of empirically tested evidence-based decision-making frameworks currently in use to support MH triage nursing practice. MH triage clinicians in Australia rely heavily on clinical experience to underpin decision making and have little of knowledge of theoretical models for practice, such as methodologies for rating urgency. A key recommendation arising from the study is the need to develop evidence-based decision-making frameworks such as clinical guidelines to inform and support MH triage clinical decision making.

  1. Clinical Information Support System (CISS)

    Data.gov (United States)

    Department of Veterans Affairs — Clinical Information Support System (CISS) is a web-based portal application that provides a framework of services for the VA enterprise and supplies an integration...

  2. Decision analysis. Clinical art or Clinical Science

    Science.gov (United States)

    1977-05-01

    having helped some clients. Over the past half century, psychotherapy has faced a series of crises concerned with its transformation from an art to a...clinical science . These include validation of the effectiveness of various forms of therapy, validating elements of treatment programs and

  3. Decision support models for natural gas dispatch

    International Nuclear Information System (INIS)

    Chin, L.; Vollmann, T.E.

    1992-01-01

    A decision support model is presented which will give utilities the support tools to manage the purchasing of natural gas supplies in the most cost effective manner without reducing winter safety stocks to below minimum levels. In Business As Usual (BAU) purchasing quantities vary with the daily forecasts. With Material Requirements Planning (MRP) and Linear Programming (LP), two types of factors are used: seasonal weather and decision rule. Under current practices, BAU simulation uses the least expensive gas source first, then adding successively more expensive sources. Material Requirements Planning is a production planning technique which uses a parent item master production schedule to determine time phased requirements for component points. Where the MPS is the aggregate gas demand forecasts for the contract year. This satisfies daily demand with least expensive gas and uses more expensive when necessary with automatic computation of available-to-promise (ATP) gas a dispacher knows daily when extra gas supplies may be ATP. Linear Programming is a mathematical algorithm used to determine optimal allocations of scarce resources to achieve a desired result. The LP model determines optimal daily gas purchase decisions with respect to supply cost minimization. Using these models, it appears possible to raise gross income margins 6 to 10% with minimal additions of customers and no new gas supply

  4. Decision support models for natural gas dispatch

    Energy Technology Data Exchange (ETDEWEB)

    Chin, L. (Bentley College, Waltham, MA (United States)); Vollmann, T.E. (International Inst. for Management Development, Lausanne (Switzerland))

    A decision support model is presented which will give utilities the support tools to manage the purchasing of natural gas supplies in the most cost effective manner without reducing winter safety stocks to below minimum levels. In Business As Usual (BAU) purchasing quantities vary with the daily forecasts. With Material Requirements Planning (MRP) and Linear Programming (LP), two types of factors are used: seasonal weather and decision rule. Under current practices, BAU simulation uses the least expensive gas source first, then adding successively more expensive sources. Material Requirements Planning is a production planning technique which uses a parent item master production schedule to determine time phased requirements for component points. Where the MPS is the aggregate gas demand forecasts for the contract year. This satisfies daily demand with least expensive gas and uses more expensive when necessary with automatic computation of available-to-promise (ATP) gas a dispacher knows daily when extra gas supplies may be ATP. Linear Programming is a mathematical algorithm used to determine optimal allocations of scarce resources to achieve a desired result. The LP model determines optimal daily gas purchase decisions with respect to supply cost minimization. Using these models, it appears possible to raise gross income margins 6 to 10% with minimal additions of customers and no new gas supply.

  5. Modular Architecture for Integrated Model-Based Decision Support.

    Science.gov (United States)

    Gaebel, Jan; Schreiber, Erik; Oeser, Alexander; Oeltze-Jafra, Steffen

    2018-01-01

    Model-based decision support systems promise to be a valuable addition to oncological treatments and the implementation of personalized therapies. For the integration and sharing of decision models, the involved systems must be able to communicate with each other. In this paper, we propose a modularized architecture of dedicated systems for the integration of probabilistic decision models into existing hospital environments. These systems interconnect via web services and provide model sharing and processing capabilities for clinical information systems. Along the lines of IHE integration profiles from other disciplines and the meaningful reuse of routinely recorded patient data, our approach aims for the seamless integration of decision models into hospital infrastructure and the physicians' daily work.

  6. Decision aids for people considering taking part in clinical trials.

    Science.gov (United States)

    Gillies, Katie; Cotton, Seonaidh C; Brehaut, Jamie C; Politi, Mary C; Skea, Zoe

    2015-11-27

    Several interventions have been developed to promote informed consent for participants in clinical trials. However, many of these interventions focus on the content and structure of information (e.g. enhanced information or changes to the presentation format) rather than the process of decision making. Patient decision aids support a decision making process about medical options. Decision aids support the decision process by providing information about available options and their associated outcomes, alongside information that enables patients to consider what value they place on particular outcomes, and provide structured guidance on steps of decision making. They have been shown to be effective for treatment and screening decisions but evidence on their effectiveness in the context of informed consent for clinical trials has not been synthesised. To assess the effectiveness of decision aids for clinical trial informed consent compared to no intervention, standard information (i.e. usual practice) or an alternative intervention on the decision making process. We searched the following databases and to March 2015: Cochrane Central Register of Controlled Trials (CENTRAL), The Cochrane Library; MEDLINE (OvidSP) (from 1950); EMBASE (OvidSP) (from 1980); PsycINFO (OvidSP) (from 1806); ASSIA (ProQuest) (from 1987); WHO International Clinical Trials Registry Platform (ICTRP) (http://apps.who.int/trialsearch/); ClinicalTrials.gov; ISRCTN Register (http://www.controlled-trials.com/isrctn/). We also searched reference lists of included studies and relevant reviews. We contacted study authors and other experts. There were no language restrictions. We included randomised and quasi-randomised controlled trials comparing decision aids in the informed consent process for clinical trials alone, or in conjunction with standard information (such as written or verbal) or alongside alternative interventions (e.g. paper-based versus web-based decision aids). Included trials involved

  7. Computerised decision support systems for healthcare professionals: an interpretative review

    Directory of Open Access Journals (Sweden)

    Kathrin Cresswell

    2013-03-01

    Full Text Available Purpose Computerised decision support systems are designed to support clinicians in making decisions and thereby enhance the quality and safety of care. We aimed to undertake an interpretative review of the empirical evidence on computerised decision support systems, their contexts of use, and summarise evidence on the effectiveness of these tools and insights into how these can be successfully implemented and adopted.Methods We systematically searched the empirical literature to identify systematic literature reviews on computerised decision support applications and their impact on the quality and safety of healthcare delivery over a 13-year period (1997–2010. The databases searched included: MEDLINE, EMBASE, The Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, The Cochrane Central Register of Controlled Trials, The Cochrane Methodology Register, The Health Technology Assessment Database, and The National Health Service (NHS Economic Evaluation Database. To be eligible for inclusion, systematic reviews needed to address computerised decision support systems, and at least one of the following: impact on safety; quality; or organisational, implementation or adoption considerations.Results Our searches yielded 121 systematic reviews relating to eHealth, of which we identified 41 as investigating computerised decision support systems. These indicated that, whilst there was a lack of investigating potential risks, such tools can result in improvements in practitioner performance in the promotion of preventive care and guideline adherence, particularly if specific information is available in real time and systems are effectively integrated into clinical workflows. However, the evidence regarding impact on patient outcomes was less clear-cut with reviews finding either no, inconsistent or modest benefits.Conclusions Whilst the potential of clinical decision support systems in improving, in particular

  8. Operator decision support system for sodium loop

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Kwang Hyeang; Park, Kyu Ho; Kim, Tak Kon; Jo, Choong Ho; Seong, Kyeong A; Lee, Keon Myeong; Kim, Yeong Dal; Kim, Chang Beom; Kim, Jong Kyu; Jo, Hee Chang; Lee, Ji Hyeong; Jeong, Yoon Soo; Chio, Jong Hyeong; Jeong, Bong Joon; Hong, Joon Seong; Kim, Bong Wan; Seong, Byeong Hak [Korea Advanced Institute Science and Technology, Taejon (Korea, Republic of)

    1994-07-01

    The objective of this study is to develop an operator decision support system by computerizing the sodium circuit. This study developed graphical display interface for the control panel which provides the safety control of equipment, the recognition of experimental process states and sodium circuit states. In this study, basic work to develop an operator decision support real-time expert system for sodium loop was carried out. Simplification of control commands and effective operation of various real-time data and signals by equipment code standardization are studied. The cost ineffectiveness of the single processor structure provides the ground for the development of cost effective parallel processing system. The important tasks of this study are (1) design and implementation of control state surveillance panel of sodium loop, (2) requirement analysis of operator support real-time expert system for sodium loop, (3) design of standard code rule for operating equipment and research on the cost effective all purpose parallel processing system and (4) requirement analysis of expert system and design of control state variables and user interface for experimental process. 10 refs., 36 figs., 20 tabs.

  9. Handling risk attitudes for preference learning and intelligent decision support

    DEFF Research Database (Denmark)

    Franco de los Ríos, Camilo; Hougaard, Jens Leth; Nielsen, Kurt

    2015-01-01

    Intelligent decision support should allow integrating human knowledge with efficient algorithms for making interpretable and useful recommendations on real world decision problems. Attitudes and preferences articulate and come together under a decision process that should be explicitly modeled...

  10. Decision support software technology demonstration plan

    Energy Technology Data Exchange (ETDEWEB)

    SULLIVAN,T.; ARMSTRONG,A.

    1998-09-01

    The performance evaluation of innovative and alternative environmental technologies is an integral part of the US Environmental Protection Agency's (EPA) mission. Early efforts focused on evaluating technologies that supported the implementation of the Clean Air and Clean Water Acts. In 1986 the Agency began to demonstrate and evaluate the cost and performance of remediation and monitoring technologies under the Superfund Innovative Technology Evaluation (SITE) program (in response to the mandate in the Superfund Amendments and Reauthorization Act of 1986 (SARA)). In 1990, the US Technology Policy was announced. This policy placed a renewed emphasis on making the best use of technology in achieving the national goals of improved quality of life for all Americans, continued economic growth, and national security. In the spirit of the technology policy, the Agency began to direct a portion of its resources toward the promotion, recognition, acceptance, and use of US-developed innovative environmental technologies both domestically and abroad. Decision Support Software (DSS) packages integrate environmental data and simulation models into a framework for making site characterization, monitoring, and cleanup decisions. To limit the scope which will be addressed in this demonstration, three endpoints have been selected for evaluation: Visualization; Sample Optimization; and Cost/Benefit Analysis. Five topics are covered in this report: the objectives of the demonstration; the elements of the demonstration plan; an overview of the Site Characterization and Monitoring Technology Pilot; an overview of the technology verification process; and the purpose of this demonstration plan.

  11. Decision Analysis: Engineering Science or Clinical Art

    Science.gov (United States)

    1979-11-01

    TECHNICAL REPORT TR 79-2-97 DECISION ANALYSIS: ENGINEERING SCIENCE OR CLINICAL ART ? by Dennis M. Buede Prepared for Defense Advanced Research...APPLICATIONS OF THE ENGINEER- ING SCIENCE AND CLINICAL ART EXTREMES 9 3.1 Applications of the Engineering Science Approach 9 3.1.1 Mexican electrical...DISCUSSION 29 4.1 Engineering Science versus Clinical Art : A Characterization of When Each is Most Attractive 30 4.2 The Implications of the Engineering

  12. Clinical decision making in veterinary practice

    OpenAIRE

    Everitt, Sally

    2011-01-01

    Aim The aim of this study is to develop an understanding of the factors which influence veterinary surgeons’ clinical decision making during routine consultations. Methods The research takes a qualitative approach using video-cued interviews, in which one of the veterinary surgeon’s own consultations is used as the basis of a semi-structured interview exploring decision making in real cases. The research focuses primarily on small animal consultations in first opinion practice, how...

  13. Using a group decision support system to make investment prioritisation decisions

    OpenAIRE

    Read, Martin; Gear, Tony; Minkes, Leonard; Irving, Ann

    2013-01-01

    This paper is concerned with how decision making groups involved in making investment prioritisation decisions involving funding of technology and science projects may be supported by a group decision support system (GDSS). While interested in decision outcomes, the primary focus of this paper is the role of a group support system as an aid to developing shared understanding within a group. The paper develops the conceptual framework of decision-making, communication and group support, and de...

  14. Development of transportation asset management decision support tools : final report.

    Science.gov (United States)

    2017-08-09

    This study developed a web-based prototype decision support platform to demonstrate the benefits of transportation asset management in monitoring asset performance, supporting asset funding decisions, planning budget tradeoffs, and optimizing resourc...

  15. Reactive Software Agent Anesthesia Decision Support System

    Directory of Open Access Journals (Sweden)

    Grant H. Kruger

    2011-12-01

    Full Text Available Information overload of the anesthesiologist through technological advances have threatened the safety of patients under anesthesia in the operating room (OR. Traditional monitoring and alarm systems provide independent, spatially distributed indices of patient physiological state. This creates the potential to distract caregivers from direct patient care tasks. To address this situation, a novel reactive agent decision support system with graphical human machine interface was developed. The system integrates the disparate data sources available in the operating room, passes the data though a decision matrix comprising a deterministic physiologic rule base established through medical research. Patient care is improved by effecting change to the care environment by displaying risk factors and alerts as an intuitive color coded animation. The system presents a unified, contextually appropriate snapshot of the patient state including current and potential risk factors, and alerts of critical patient events to the operating room team without requiring any user intervention. To validate the efficacy of the system, a retrospective analysis focusing on the hypotension rules were performed. Results show that even with vigilant and highly trained clinicians, deviations from ideal patient care exist and it is here that the proposed system may allow more standardized and improved patient care and potentially outcomes.

  16. Marketing Decision Making and Decision Support: Challenges and Perspectives for Successful Marketing Management Support Systems

    NARCIS (Netherlands)

    G.H. van Bruggen (Gerrit); B. Wierenga (Berend)

    2009-01-01

    textabstractMarketing management support systems (MMSS) are computer-enabled devices that help marketers to make better decisions. Marketing processes can be quite complex, involving large numbers of variables and mostly outcomes are the results of the actions of many different stakeholders (e.g.,

  17. Semantic technologies in a decision support system

    Science.gov (United States)

    Wasielewska, K.; Ganzha, M.; Paprzycki, M.; Bǎdicǎ, C.; Ivanovic, M.; Lirkov, I.

    2015-10-01

    The aim of our work is to design a decision support system based on ontological representation of domain(s) and semantic technologies. Specifically, we consider the case when Grid / Cloud user describes his/her requirements regarding a "resource" as a class expression from an ontology, while the instances of (the same) ontology represent available resources. The goal is to help the user to find the best option with respect to his/her requirements, while remembering that user's knowledge may be "limited." In this context, we discuss multiple approaches based on semantic data processing, which involve different "forms" of user interaction with the system. Specifically, we consider: (a) ontological matchmaking based on SPARQL queries and class expression, (b) graph-based semantic closeness of instances representing user requirements (constructed from the class expression) and available resources, and (c) multicriterial analysis based on the AHP method, which utilizes expert domain knowledge (also ontologically represented).

  18. THE DECISION SUPPORT SYSTEM IN ROMANIA

    Directory of Open Access Journals (Sweden)

    Ana V.Monica POP

    2013-10-01

    Full Text Available In the present paper we will try to analyze the Decision Support System (DSS and the way in which it is applied or not in the Romanian Small and Medium Sized Enterprises (SMEs (with examples. We also will see if the system is beneficial for these Romanian Enterprises. We analyzed through interviews 50 small and medium-sized Romanian enterprises. They do not accept their name to be published. As a consequence, we will present only the results. It is underlined in the conclusions the differences between the small and the medium size enterprises in respect of the models they are using. The most important benefits of DSS (generally are represented by increased efficiency, competitive advantages and better managerial process.

  19. Decision support for organ offers in liver transplantation.

    Science.gov (United States)

    Volk, Michael L; Goodrich, Nathan; Lai, Jennifer C; Sonnenday, Christopher; Shedden, Kerby

    2015-06-01

    Organ offers in liver transplantation are high-risk medical decisions with a low certainty of whether a better liver offer will come along before death. We hypothesized that decision support could improve the decision to accept or decline. With data from the Scientific Registry of Transplant Recipients, survival models were constructed for 42,857 waiting-list patients and 28,653 posttransplant patients from 2002 to 2008. Daily covariate-adjusted survival probabilities from these 2 models were combined into a 5-year area under the curve to create an individualized prediction of whether an organ offer should be accepted for a given patient. Among 650,832 organ offers from 2008 to 2013, patient survival was compared by whether the clinical decision was concordant or discordant with model predictions. The acceptance benefit (AB)--the predicted gain or loss of life by accepting a given organ versus waiting for the next organ--ranged from 3 to -22 years (harm) and varied geographically; for example, the average benefit of accepting a donation after cardiac death organ ranged from 0.47 to -0.71 years by donation service area. Among organ offers, even when AB was >1 year, the offer was only accepted 10% of the time. Patient survival from the time of the organ offer was better if the model recommendations and the clinical decision were concordant: for offers with AB > 0, the 3-year survival was 80% if the offer was accepted and 66% if it was declined (P decision support may improve patient survival in liver transplantation. © 2015 American Association for the Study of Liver Diseases.

  20. Decision support system for health care resources allocation.

    Science.gov (United States)

    Sebaa, Abderrazak; Nouicer, Amina; Tari, AbdelKamel; Tarik, Ramtani; Abdellah, Ouhab

    2017-06-01

    A study about healthcare resources can improve decisions regarding the allotment and mobilization of medical resources and to better guide future investment in the health sector. The aim of this work was to design and implement a decision support system to improve medical resources allocation of Bejaia region. To achieve the retrospective cohort study, we integrated existing clinical databases from different Bejaia department health sector institutions (an Algerian department) to collect information about patients from January 2015 through December 2015. Data integration was performed in a data warehouse using the multi-dimensional model and OLAP cube. During implementation, we used Microsoft SQL server 2012 and Microsoft Excel 2010. A medical decision support platform was introduced, and was implemented during the planning stages allowing the management of different medical orientations, it provides better apportionment and allotment of medical resources, and ensures that the allocation of health care resources has optimal effects on improving health. In this study, we designed and implemented a decision support system which would improve health care in Bejaia department to especially assist in the selection of the optimum location of health center and hospital, the specialty of the health center, the medical equipment and the medical staff.

  1. The role of depression pharmacogenetic decision support tools in shared decision making.

    Science.gov (United States)

    Arandjelovic, Katarina; Eyre, Harris A; Lenze, Eric; Singh, Ajeet B; Berk, Michael; Bousman, Chad

    2017-10-29

    Patients discontinue antidepressant medications due to lack of knowledge, unrealistic expectations, and/or unacceptable side effects. Shared decision making (SDM) invites patients to play an active role in their treatment and may indirectly improve outcomes through enhanced engagement in care, adherence to treatment, and positive expectancy of medication outcomes. We believe decisional aids, such as pharmacogenetic decision support tools (PDSTs), facilitate SDM in the clinical setting. PDSTs may likewise predict drug tolerance and efficacy, and therefore adherence and effectiveness on an individual-patient level. There are several important ethical considerations to be navigated when integrating PDSTs into clinical practice. The field requires greater empirical research to demonstrate clinical utility, and the mechanisms thereof, as well as exploration of the ethical use of these technologies.

  2. Development of a Web-Based Clinical Decision Support System for Drug Prescription: Non-Interventional Naturalistic Description of the Antipsychotic Prescription Patterns in 4345 Outpatients and Future Applications.

    Science.gov (United States)

    Berrouiguet, Sofian; Barrigón, Maria Luisa; Brandt, Sara A; Ovejero-García, Santiago; Álvarez-García, Raquel; Carballo, Juan Jose; Lenca, Philippe; Courtet, Philippe; Baca-García, Enrique

    2016-01-01

    The emergence of electronic prescribing devices with clinical decision support systems (CDSS) is able to significantly improve management pharmacological treatments. We developed a web application available on smartphones in order to help clinicians monitor prescription and further propose CDSS. A web application (www.MEmind.net) was developed to assess patients and collect data regarding gender, age, diagnosis and treatment. We analyzed antipsychotic prescriptions in 4345 patients attended in five Psychiatric Community Mental Health Centers from June 2014 to October 2014. The web-application reported average daily dose prescribed for antipsychotics, prescribed daily dose (PDD), and the PDD to defined daily dose (DDD) ratio. The MEmind web-application reported that antipsychotics were used in 1116 patients out of the total sample, mostly in 486 (44%) patients with schizophrenia related disorders but also in other diagnoses. Second generation antipsychotics (quetiapine, aripiprazole and long-acting paliperidone) were preferably employed. Low doses were more frequently used than high doses. Long acting paliperidone and ziprasidone however, were the only two antipsychotics used at excessive dosing. Antipsychotic polypharmacy was used in 287 (26%) patients with classic depot drugs, clotiapine, amisulpride and clozapine. In this study we describe the first step of the development of a web application that is able to make polypharmacy, high dose usage and off label usage of antipsychotics visible to clinicians. Current development of the MEmind web application may help to improve prescription security via momentary feedback of prescription and clinical decision support system.

  3. Clinical decision making: how surgeons do it.

    Science.gov (United States)

    Crebbin, Wendy; Beasley, Spencer W; Watters, David A K

    2013-06-01

    Clinical decision making is a core competency of surgical practice. It involves two distinct types of mental process best considered as the ends of a continuum, ranging from intuitive and subconscious to analytical and conscious. In practice, individual decisions are usually reached by a combination of each, according to the complexity of the situation and the experience/expertise of the surgeon. An expert moves effortlessly along this continuum, according to need, able to apply learned rules or algorithms to specific presentations, choosing these as a result of either pattern recognition or analytical thinking. The expert recognizes and responds quickly to any mismatch between what is observed and what was expected, coping with gaps in information and making decisions even where critical data may be uncertain or unknown. Even for experts, the cognitive processes involved are difficult to articulate as they tend to be very complex. However, if surgeons are to assist trainees in developing their decision-making skills, the processes need to be identified and defined, and the competency needs to be measurable. This paper examines the processes of clinical decision making in three contexts: making a decision about how to manage a patient; preparing for an operative procedure; and reviewing progress during an operative procedure. The models represented here are an exploration of the complexity of the processes, designed to assist surgeons understand how expert clinical decision making occurs and to highlight the challenge of teaching these skills to surgical trainees. © 2013 The Authors. ANZ Journal of Surgery © 2013 Royal Australasian College of Surgeons.

  4. Theory, Software and Testing Examples for Decision Support Systems

    OpenAIRE

    Lewandowski, A.; Wierzbicki, A.P.

    1987-01-01

    Research in methodology of Decision Support Systems is one of the activities within the System and Decision Sciences Program which was initiated seven years ago and is still in the center of interests of SDS. During these years several methodological approaches and software tools have been developed; among others the DIDAS (Dynamic Interactive Decision Analysis and Support) and SCDAS (Selection Committed Decision Analysis and Support). Both methodologies gained a certain level of popularity a...

  5. Quantitative Decision Support Requires Quantitative User Guidance

    Science.gov (United States)

    Smith, L. A.

    2009-12-01

    Is it conceivable that models run on 2007 computer hardware could provide robust and credible probabilistic information for decision support and user guidance at the ZIP code level for sub-daily meteorological events in 2060? In 2090? Retrospectively, how informative would output from today’s models have proven in 2003? or the 1930’s? Consultancies in the United Kingdom, including the Met Office, are offering services to “future-proof” their customers from climate change. How is a US or European based user or policy maker to determine the extent to which exciting new Bayesian methods are relevant here? or when a commercial supplier is vastly overselling the insights of today’s climate science? How are policy makers and academic economists to make the closely related decisions facing them? How can we communicate deep uncertainty in the future at small length-scales without undermining the firm foundation established by climate science regarding global trends? Three distinct aspects of the communication of the uses of climate model output targeting users and policy makers, as well as other specialist adaptation scientists, are discussed. First, a brief scientific evaluation of the length and time scales at which climate model output is likely to become uninformative is provided, including a note on the applicability the latest Bayesian methodology to current state-of-the-art general circulation models output. Second, a critical evaluation of the language often employed in communication of climate model output, a language which accurately states that models are “better”, have “improved” and now “include” and “simulate” relevant meteorological processed, without clearly identifying where the current information is thought to be uninformative and misleads, both for the current climate and as a function of the state of the (each) climate simulation. And thirdly, a general approach for evaluating the relevance of quantitative climate model output

  6. Proposal for Development of EBM-CDSS (Evidence-Based Clinical Decision Support System) to Aid Prognostication in Terminally Ill Patients

    Science.gov (United States)

    2013-10-01

    Abramson MA, Pandharipande P, Ruan D, Gold JS, Whang EE. Radical resection for T1b gallbladder cancer: a decision analysis. HPB. 2009;11(8):656-63. 10...studies assessing prognosis of lung cancer patients without treatment were eligible for inclusion. Data on mortality was extracted from all included...eligibility. Disagreements about study inclusion or exclusion were resolved via discussion until a consensus was reached. Data Extraction Data extraction

  7. Decision support to enable sustainability in development projects

    CSIR Research Space (South Africa)

    Meyer, IA

    2014-10-01

    Full Text Available that are not always explicitly linked to development outcomes. Throughout this process, scope exists to aid decision makers, through a simplistic set of decision models, to make better decisions. The emphasis is on decisions that support long-term value creation...

  8. Dashboard visualizations: Supporting real-time throughput decision-making.

    Science.gov (United States)

    Franklin, Amy; Gantela, Swaroop; Shifarraw, Salsawit; Johnson, Todd R; Robinson, David J; King, Brent R; Mehta, Amit M; Maddow, Charles L; Hoot, Nathan R; Nguyen, Vickie; Rubio, Adriana; Zhang, Jiajie; Okafor, Nnaemeka G

    2017-07-01

    Providing timely and effective care in the emergency department (ED) requires the management of individual patients as well as the flow and demands of the entire department. Strategic changes to work processes, such as adding a flow coordination nurse or a physician in triage, have demonstrated improvements in throughput times. However, such global strategic changes do not address the real-time, often opportunistic workflow decisions of individual clinicians in the ED. We believe that real-time representation of the status of the entire emergency department and each patient within it through information visualizations will better support clinical decision-making in-the-moment and provide for rapid intervention to improve ED flow. This notion is based on previous work where we found that clinicians' workflow decisions were often based on an in-the-moment local perspective, rather than a global perspective. Here, we discuss the challenges of designing and implementing visualizations for ED through a discussion of the development of our prototype Throughput Dashboard and the potential it holds for supporting real-time decision-making. Copyright © 2017. Published by Elsevier Inc.

  9. The Impact of Electronic Knowledge-Based Nursing Content and Decision-Support on Nursing-Sensitive Patient Outcomes

    Science.gov (United States)

    2017-01-01

    Behavior Observation Techniques • Clinical Nursing Research • Decision Support Systems, Clinical • Dissemination, Information • Evidence-Based...gap and getting nurses in clinical settings to use evidence to support clinical decision -making (Duffy et al. 2015; Melynk, Fineout-Overholt...patient outcomes. However, it has been shown that nurses ’ knowledge and use of best evidence for clinical decision - making is often hindered by many

  10. Design of decision support interventions for medication prescribing.

    Science.gov (United States)

    Horsky, Jan; Phansalkar, Shobha; Desai, Amrita; Bell, Douglas; Middleton, Blackford

    2013-06-01

    Describe optimal design attributes of clinical decision support (CDS) interventions for medication prescribing, emphasizing perceptual, cognitive and functional characteristics that improve human-computer interaction (HCI) and patient safety. Findings from published reports on success, failures and lessons learned during implementation of CDS systems were reviewed and interpreted with regard to HCI and software usability principles. We then formulated design recommendations for CDS alerts that would reduce unnecessary workflow interruptions and allow clinicians to make informed decisions quickly, accurately and without extraneous cognitive and interactive effort. Excessive alerting that tends to distract clinicians rather than provide effective CDS can be reduced by designing only high severity alerts as interruptive dialog boxes and less severe warnings without explicit response requirement, by curating system knowledge bases to suppress warnings with low clinical utility and by integrating contextual patient data into the decision logic. Recommended design principles include parsimonious and consistent use of color and language, minimalist approach to the layout of information and controls, the use of font attributes to convey hierarchy and visual prominence of important data over supporting information, the inclusion of relevant patient data in the context of the alert and allowing clinicians to respond with one or two clicks. Although HCI and usability principles are well established and robust, CDS and EHR system interfaces rarely conform to the best known design conventions and are seldom conceived and designed well enough to be truly versatile and dependable tools. These relatively novel interventions still require careful monitoring, research and analysis of its track record to mature. Clarity and specificity of alert content and optimal perceptual and cognitive attributes, for example, are essential for providing effective decision support to clinicians

  11. Decision Support for Environmental Management of Industrial ...

    Science.gov (United States)

    Non-hazardous solid materials from industrial processes, once regarded as waste and disposed in landfills, offer numerous environmental and economic advantages when put to beneficial uses (BUs). Proper management of these industrial non-hazardous secondary materials (INSM) requires estimates of their probable environmental impacts among disposal as well as BU options. The U.S. Environmental Protection Agency (EPA) has recently approved new analytical methods (EPA Methods 1313–1316) to assess leachability of constituents of potential concern in these materials. These new methods are more realistic for many disposal and BU options than historical methods, such as the toxicity characteristic leaching protocol. Experimental data from these new methods are used to parameterize a chemical fate and transport (F&T) model to simulate long-term environmental releases from flue gas desulfurization gypsum (FGDG) when disposed of in an industrial landfill or beneficially used as an agricultural soil amendment. The F&T model is also coupled with optimization algorithms, the Beneficial Use Decision Support System (BUDSS), under development by EPA to enhance INSM management. The objective of this paper is to demonstrate the methodologies and encourage similar applications to improve environmental management and BUs of INSM through F&T simulation coupled with optimization, using realistic model parameterization.

  12. Global Turbulence Decision Support for Aviation

    Science.gov (United States)

    Williams, J.; Sharman, R.; Kessinger, C.; Feltz, W.; Wimmers, A.

    2009-09-01

    Turbulence is widely recognized as the leading cause of injuries to flight attendants and passengers on commercial air carriers, yet legacy decision support products such as SIGMETs and SIGWX charts provide relatively low spatial- and temporal-resolution assessments and forecasts of turbulence, with limited usefulness for strategic planning and tactical turbulence avoidance. A new effort is underway to develop an automated, rapid-update, gridded global turbulence diagnosis and forecast system that addresses upper-level clear-air turbulence, mountain-wave turbulence, and convectively-induced turbulence. This NASA-funded effort, modeled on the U.S. Federal Aviation Administration's Graphical Turbulence Guidance (GTG) and GTG Nowcast systems, employs NCEP Global Forecast System (GFS) model output and data from NASA and operational satellites to produce quantitative turbulence nowcasts and forecasts. A convective nowcast element based on GFS forecasts and satellite data provides a basis for diagnosing convective turbulence. An operational prototype "Global GTG” system has been running in real-time at the U.S. National Center for Atmospheric Research since the spring of 2009. Initial verification based on data from TRMM, Cloudsat and MODIS (for the convection nowcasting) and AIREPs and AMDAR data (for turbulence) are presented. This product aims to provide the "single authoritative source” for global turbulence information for the U.S. Next Generation Air Transportation System.

  13. Decision support tools for advanced energy management

    International Nuclear Information System (INIS)

    Marik, Karel; Schindler, Zdenek; Stluka, Petr

    2008-01-01

    Rising fuel costs boost energy prices, which is a driving force for improving efficiency of operation of any energy generation facility. This paper focuses on enhancing the operation of distributed integrated energy systems (IES), system that bring together all forms of cooling, heating and power (CCHP) technologies. Described methodology can be applied in power generation and district heating companies, as well as in small-scale systems that supply multiple types of utilities to consumers in industrial, commercial, residential and governmental spheres. Dispatching of such system in an optimal way needs to assess large number of production and purchasing schemes in conditions of continually changing market and variable utility demands influenced by many external factors, very often by weather conditions. The paper describes a combination of forecasting and optimization methods that supports effective decisions in IES system management. The forecaster generates the future most probable utility demand several hours or days ahead, derived from the past energy consumer behaviour. The optimizer generates economically most efficient operating schedule for the IES system that matches these forecasted energy demands and respects expected purchased energy prices. (author)

  14. Post Disaster Assessment with Decision Support System

    Directory of Open Access Journals (Sweden)

    May Florence J. Franco

    2016-05-01

    Full Text Available The study aimed to develop an online system that would expedite the response of agencies after disaster strikes; generate a list of the kinds and volume of relief aids needed per family affected for a fair, precise and timely distribution; implement community-based ICT by remotely gathering all the necessary data needed for disaster assessment; and adhere to ISO 9126 standards. The system was designed to calculate the effects of disaster in human lives and economy. Integrated into the system were Goggle Maps, Mines and GeoSciences Bureau Hazard Maps, SMS sending features, best passable routes calculations, and decision support on the needs that has to be addressed. The system was made live at pdrrmcguimaras.herokuapp.com to allow remote data entry. The functionality and usability of the system were evaluated by 19 potential users by computing for the arithmetic Mean and Standard Deviation of the survey. The result showed that most of them strongly agreed that the system is acceptable based on these criteria. A group of IT experts also evaluated the system’s conformance to ISO 9126 standards using the same method. The result showed that majority of them strongly agreed that the system conforms to this international standard. The system is seen as a valuable tool for the Provincial Disaster Risk Reduction Management Council (PDRRMC and the National Disaster Risk Reduction Management Council (NDRRMC for it could help expedite the assessment of the effects of disasters and the formulation of response plans and strategies.

  15. A generic accounting model to support operations management decisions

    NARCIS (Netherlands)

    Verdaasdonk, P.J.A.; Wouters, M.J.F.

    2001-01-01

    Information systems are generally unable to generate information about the financial consequences of operations management decisions. This is because the procedures for determining the relevant accounting information for decision support are not formalised in ways that can be implemented in

  16. On Decision Support for Sustainability and Resilience of Infrastructure

    DEFF Research Database (Denmark)

    Nielsen, Michael Havbro Faber; Qin, J.; Miragliaa, S.

    2017-01-01

    in Bayesian decision analysis and probabilistic systems performance modelling. A principal example for decision support at regulatory level is presented for a coupled system comprised of infrastructure, social, hazard and environmental subsystems. The infrastructure systems is modelled as multi...

  17. Healthcare performance turned into decision support.

    Science.gov (United States)

    Sørup, Christian Michel; Jacobsen, Peter

    2013-01-01

    The purpose of this study is to first create an overview of relevant factors directly influencing employee absence in the healthcare sector. The overview is used to further investigate the factors identified using employee satisfaction survey scores exclusively. The result of the overall objective is a management framework that allows managers to gain insight into the current status of risk factors with high influence on employee absence levels. The research consists of a quantitative literature study supported by formal and semi-formal interviews conducted at the case organisations. Employee satisfaction surveys were applied to analyse the development over time of selected factors correlated with concurrent employee absence rates. Checking for causal results, comparisons with the included published literature findings were also carried out. Four major clustered factors, three of which constitute the term "social capital", showed a high degree of connection with employee absence rates. The factors are general satisfaction, fairness, reliance and co-operation. Integrating the four elements in a management framework will provide valuable and holistic information about the determinants with regard to current levels of employee absence. The framework will be a valuable support for leaders with the authority to alter the determinants of employee absence. Since a great part of the empirical material is supplied from the healthcare sector, the results obtained could be restricted to this sector. Inclusion of data from Arbejdsmarkedets Tillaegspension (ATP) showed no deviation from the results in the healthcare sector. The product of the study is a decision support tool for leaders to cope with levels of employee absence. The framework is holistic and can prove to be a valuable tool to take a bearing of where to focus future initiatives. Gathering former observational studies in a complete overview embracing many relevant factors that influence sickness absence has not yet

  18. Decision support system for the diagnosis of schizophrenia disorders

    Directory of Open Access Journals (Sweden)

    D. Razzouk

    2006-01-01

    Full Text Available Clinical decision support systems are useful tools for assisting physicians to diagnose complex illnesses. Schizophrenia is a complex, heterogeneous and incapacitating mental disorder that should be detected as early as possible to avoid a most serious outcome. These artificial intelligence systems might be useful in the early detection of schizophrenia disorder. The objective of the present study was to describe the development of such a clinical decision support system for the diagnosis of schizophrenia spectrum disorders (SADDESQ. The development of this system is described in four stages: knowledge acquisition, knowledge organization, the development of a computer-assisted model, and the evaluation of the system's performance. The knowledge was extracted from an expert through open interviews. These interviews aimed to explore the expert's diagnostic decision-making process for the diagnosis of schizophrenia. A graph methodology was employed to identify the elements involved in the reasoning process. Knowledge was first organized and modeled by means of algorithms and then transferred to a computational model created by the covering approach. The performance assessment involved the comparison of the diagnoses of 38 clinical vignettes between an expert and the SADDESQ. The results showed a relatively low rate of misclassification (18-34% and a good performance by SADDESQ in the diagnosis of schizophrenia, with an accuracy of 66-82%. The accuracy was higher when schizophreniform disorder was considered as the presence of schizophrenia disorder. Although these results are preliminary, the SADDESQ has exhibited a satisfactory performance, which needs to be further evaluated within a clinical setting.

  19. Proposal for Development of EBM-CDSS (Evidence-based Clinical Decision Support System) to Aid Prognostication in Terminally Ill Patients

    Science.gov (United States)

    2014-10-01

    clinical research studies. The importance of meta-analysis stems from the necessity to combine research findings that if considered separately they would...patient data collected from nine randomized trials studying the effect of Allogeneic Peripheral Blood Stem -cell transplantation (PBSCT) compared to Bone...leukemia ( CLL ) Chronic myelogenous leukemia (CML) Hodgkin’s disease (HD) Idiopathic myelofibrosis (IMF) Myelodysplastic symdrome (MDS) Multiple

  20. Developing a Support Tool for Global Product Development Decisions

    DEFF Research Database (Denmark)

    Søndergaard, Erik Stefan; Ahmed-Kristensen, Saeema

    2016-01-01

    This paper investigates how global product development decisions are made through a multiple-case study in three Danish engineering. The paper identifies which information and methods are applied for making decisions and how decision-making can be supported based on previous experience. The paper...... presents results from 51 decisions made in the three companies, and based on the results of the studies a framework for a decision-support tool is outlined and discussed. The paper rounds off with an identification of future research opportunities in the area of global product development and decision-making....

  1. Dialogic Consensus In Clinical Decision-Making.

    Science.gov (United States)

    Walker, Paul; Lovat, Terry

    2016-12-01

    This paper is predicated on the understanding that clinical encounters between clinicians and patients should be seen primarily as inter-relations among persons and, as such, are necessarily moral encounters. It aims to relocate the discussion to be had in challenging medical decision-making situations, including, for example, as the end of life comes into view, onto a more robust moral philosophical footing than is currently commonplace. In our contemporary era, those making moral decisions must be cognizant of the existence of perspectives other than their own, and be attuned to the demands of inter-subjectivity. Applicable to clinical practice, we propose and justify a Habermasian approach as one useful means of achieving what can be described as dialogic consensus. The Habermasian approach builds around, first, his discourse theory of morality as universalizable to all and, second, communicative action as a cooperative search for truth. It is a concrete way to ground the discourse which must be held in complex medical decision-making situations, in its actual reality. Considerations about the theoretical underpinnings of the application of dialogic consensus to clinical practice, and potential difficulties, are explored.

  2. Development of the Supported Decision Making Inventory System.

    Science.gov (United States)

    Shogren, Karrie A; Wehmeyer, Michael L; Uyanik, Hatice; Heidrich, Megan

    2017-12-01

    Supported decision making has received increased attention as an alternative to guardianship and a means to enable people with intellectual and developmental disabilities to exercise their right to legal capacity. Assessments are needed that can used by people with disabilities and their systems of supports to identify and plan for needed supports to enable decision making. This article describes the steps taken to develop such an assessment tool, the Supported Decision Making Inventory System (SDMIS), and initial feedback received from self-advocates with intellectual disability. The three sections of the SDMIS (Supported Decision Making Personal Factors Inventory, Supported Decision Making Environmental Demands Inventory, and Decision Making Autonomy Inventory) are described and implications for future research, policy, and practice are discussed.

  3. Proposal for Development of EBM-CDSS (Evidence-based Clinical Decision Support System) to Aid Prognostication in Terminally Ill Patients

    Science.gov (United States)

    2012-10-01

    administrative permissions to advertise our study at TGH and among USF physicians. That is, we have obtained permissions from USF media relations department to...for estimating survival time in palliative care, Montreal, CANADA: Centre of Bioethics , Clinical Research Institute of Montreal, 2007. [18] P...guardianship program. Such a proxy must be selected by the provider’s bioethics committee and must not be employed by the provider. If the provider

  4. Fault Detection for Shipboard Monitoring and Decision Support Systems

    DEFF Research Database (Denmark)

    Lajic, Zoran; Nielsen, Ulrik Dam

    2009-01-01

    In this paper a basic idea of a fault-tolerant monitoring and decision support system will be explained. Fault detection is an important part of the fault-tolerant design for in-service monitoring and decision support systems for ships. In the paper, a virtual example of fault detection...... will be presented for a containership with a real decision support system onboard. All possible faults can be simulated and detected using residuals and the generalized likelihood ratio (GLR) algorithm....

  5. The relationship between patient data and pooled clinical management decisions.

    Science.gov (United States)

    Ludbrook, G I; O'Loughlin, E J; Corcoran, T B; Grant, C

    2013-01-01

    A strong relationship between patient data and preoperative clinical decisions could potentially be used to support clinical decisions in preoperative management. The aim of this exploratory study was to determine the relationship between key patient data and pooled clinical opinions on management. In a previous study, panels of anaesthetists compared the quality of computer-assisted patient health assessments with outpatient consultations and made decisions on the need for preoperative tests, no preoperative outpatient assessment, possible postoperative intensive care unit/high dependency unit requirements and aspiration prophylaxis. In the current study, the relationship between patient data and these decisions was examined using binomial logistic regression analysis. Backward stepwise regression was used to identify independent predictors of each decision (at P >0.15), which were then incorporated into a predictive model. The number of factors related to each decision varied: blood picture (four factors), biochemistry (six factors), coagulation studies (three factors), electrocardiography (eight factors), chest X-ray (seven factors), preoperative outpatient assessment (17 factors), intensive care unit requirement (eight factors) and aspiration prophylaxis (one factor). The factor types also varied, but included surgical complexity, age, gender, number of medications or comorbidities, body mass index, hypertension, central nervous system condition, heart disease, sleep apnoea, smoking, persistent pain and stroke. Models based on these relationships usually demonstrated good sensitivity and specificity, with receiver operating characteristics in the following areas under curve: blood picture (0.75), biochemistry (0.86), coagulation studies (0.71), electrocardiography (0.90), chest X-ray (0.85), outpatient assessment (0.85), postoperative intensive care unit requirement (0.88) and aspiration prophylaxis (0.85). These initial results suggest modelling of patient

  6. Investing in deliberation: a definition and classification of decision support interventions for people facing difficult health decisions.

    Science.gov (United States)

    Elwyn, Glyn; Frosch, Dominick; Volandes, Angelo E; Edwards, Adrian; Montori, Victor M

    2010-01-01

    This article provides an analysis of 'decision aids', interventions to support patients facing tough decisions. Interest has increased since the concept of shared decision making has become widely considered to be a means of achieving desirable clinical outcomes. We consider the aims of these interventions and examine assumptions about their use. We propose three categories, interventions that are used in face-to-face encounters, those designed for use outside clinical encounters and those which are mediated, using telephone or other communication media. We propose the following definition: decision support interventions help people think about choices they face; they describe where and why choice exists; they provide information about options, including, where reasonable, the option of taking no action. These interventions help people to deliberate, independently or in collaboration with others, about options, by considering relevantattributes; they support people to forecast how they might feel about short, intermediate and long-term outcomes which have relevant consequences, in ways which help the process of constructing preferences and eventual decision making, appropriate to their individual situation. Although quality standards have been published for these interventions, we are also cautious about premature closure and consider that the need for short versions for use inside clinical encounters and long versions for external use requires further research. More work is also needed on the use of narrative formats and the translation of theory into practical designs. The interest in decision support interventions for patients heralds a transformation in clinical practice although many important areas remain unresolved.

  7. The Use of Advanced Warfighting Experiments to Support Acquisition Decisions

    National Research Council Canada - National Science Library

    Strayer, Kenneth

    1999-01-01

    .... Specifically, the thesis evaluated the effectiveness of the Army Task Force XXI AWE in providing information to support investment decisions and refinement of requirements for information age technologies...

  8. An open-loop, physiologic model-based decision support system can provide appropriate ventilator settings

    DEFF Research Database (Denmark)

    Karbing, Dan Stieper; Spadaro, Savino; Dey, Nilanjan

    2018-01-01

    OBJECTIVES: To evaluate the physiologic effects of applying advice on mechanical ventilation by an open-loop, physiologic model-based clinical decision support system. DESIGN: Prospective, observational study. SETTING: University and Regional Hospitals' ICUs. PATIENTS: Varied adult ICU population...

  9. Modeling Based Decision Support Environment, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — Phoenix Integration's vision is the creation of an intuitive human-in-the-loop engineering environment called Decision Navigator that leverages recent advances in...

  10. A Review of Automated Decision Support System

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... Intelligence AI that enable decision automation based on existing facts, knowledge ... The growing reliance on data impacts dynamic data extraction and retrieval of the ... entertainment, medical, and the web. III. DECISION ...

  11. A systematic review of decision support needs of parents making child health decisions

    Science.gov (United States)

    Jackson, Cath; Cheater, Francine M.; Reid, Innes

    2008-01-01

    Abstract Objective  To identify the decision support needs of parents attempting to make an informed health decision on behalf of a child. Context  The first step towards implementing patient decision support is to assess patients’ information and decision‐making needs. Search strategy  A systematic search of key bibliographic databases for decision support studies was performed in 2005. Reference lists of relevant review articles and key authors were searched. Three relevant journals were hand searched. Inclusion criteria  Non‐intervention studies containing data on decision support needs of parents making child health decisions. Data extraction and synthesis  Data were extracted on study characteristics, decision focus and decision support needs. Studies were quality assessed using a pre‐defined set of criteria. Data synthesis used the UK Evidence for Policy and Practice Information and Co‐ordinating Centre approach. Main results  One‐hundred and forty nine studies were included across various child health decisions, settings and study designs. Thematic analysis of decision support needs indicated three key issues: (i) information (including suggestions about the content, delivery, source, timing); (ii) talking to others (including concerns about pressure from others); and (iii) feeling a sense of control over the process that could be influenced by emotionally charged decisions, the consultation process, and structural or service barriers. These were consistent across decision type, study design and whether or not the study focused on informed decision making. PMID:18816320

  12. Big-Data Based Decision-Support Systems to Improve Clinicians’ Cognition

    Science.gov (United States)

    Roosan, Don; Samore, Matthew; Jones, Makoto; Livnat, Yarden; Clutter, Justin

    2016-01-01

    Complex clinical decision-making could be facilitated by using population health data to inform clinicians. In two previous studies, we interviewed 16 infectious disease experts to understand complex clinical reasoning. For this study, we focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. We found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians’ cognition to deal with complex problems. These cognitive strategies could be supported by population health data, and all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records. Our findings provide directions for task allocation and design of decision-support applications for health care industry development of Big data based decision-support systems. PMID:27990498

  13. Big-Data Based Decision-Support Systems to Improve Clinicians' Cognition.

    Science.gov (United States)

    Roosan, Don; Samore, Matthew; Jones, Makoto; Livnat, Yarden; Clutter, Justin

    2016-01-01

    Complex clinical decision-making could be facilitated by using population health data to inform clinicians. In two previous studies, we interviewed 16 infectious disease experts to understand complex clinical reasoning. For this study, we focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. We found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians' cognition to deal with complex problems. These cognitive strategies could be supported by population health data, and all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records. Our findings provide directions for task allocation and design of decision-support applications for health care industry development of Big data based decision-support systems.

  14. Launching a virtual decision lab: development and field-testing of a web-based patient decision support research platform.

    Science.gov (United States)

    Hoffman, Aubri S; Llewellyn-Thomas, Hilary A; Tosteson, Anna N A; O'Connor, Annette M; Volk, Robert J; Tomek, Ivan M; Andrews, Steven B; Bartels, Stephen J

    2014-12-12

    Over 100 trials show that patient decision aids effectively improve patients' information comprehension and values-based decision making. However, gaps remain in our understanding of several fundamental and applied questions, particularly related to the design of interactive, personalized decision aids. This paper describes an interdisciplinary development process for, and early field testing of, a web-based patient decision support research platform, or virtual decision lab, to address these questions. An interdisciplinary stakeholder panel designed the web-based research platform with three components: a) an introduction to shared decision making, b) a web-based patient decision aid, and c) interactive data collection items. Iterative focus groups provided feedback on paper drafts and online prototypes. A field test assessed a) feasibility for using the research platform, in terms of recruitment, usage, and acceptability; and b) feasibility of using the web-based decision aid component, compared to performance of a videobooklet decision aid in clinical care. This interdisciplinary, theory-based, patient-centered design approach produced a prototype for field-testing in six months. Participants (n = 126) reported that: the decision aid component was easy to use (98%), information was clear (90%), the length was appropriate (100%), it was appropriately detailed (90%), and it held their interest (97%). They spent a mean of 36 minutes using the decision aid and 100% preferred using their home/library computer. Participants scored a mean of 75% correct on the Decision Quality, Knowledge Subscale, and 74 out of 100 on the Preparation for Decision Making Scale. Completing the web-based decision aid reduced mean Decisional Conflict scores from 31.1 to 19.5 (p development of a web-based patient decision support research platform that was feasible for use in research studies in terms of recruitment, acceptability, and usage. Within this platform, the web

  15. A Benchmark Usability Study of the Tactical Decision Making Under Stress Decision Support System

    National Research Council Canada - National Science Library

    Schmorrow, Dylan

    1998-01-01

    This study evaluates the usability of a U.S. Navy Decision Support System (DSS). The DSS was developed to enhance the performance of tactical decision makers within a Navy Combat Information Center...

  16. Making interactive decision support for patients a reality.

    NARCIS (Netherlands)

    Evans, R.W.; Elwyn, G.; Edwards, A.

    2004-01-01

    Interactive decision support applications might help patients to make difficult decisions about their health care. They lie in the context of traditional decision aids, which are known to have effects on a number of patient outcomes, including knowledge and decisional conflict. The problem of

  17. A Multi-criterial Decision Support System for Forest Management

    Science.gov (United States)

    Donald Nute; Geneho Kim; Walter D. Potter; Mark J. Twery; H. Michael Rauscher; Scott Thomasma; Deborah Bennett; Peter Kollasch

    1999-01-01

    We describe a research project that has as its goal development of a full-featured decision support system for managing forested land to satisfy multiple criteria represented as timber, wildlife, water, ecological, and wildlife objectives. The decision process proposed for what was originally conceived of as a Northeast Decision Model (NED) includes data acquisition,...

  18. A multicriteria decision support methodology for evaluating airport expansion plans

    NARCIS (Netherlands)

    Vreeker, R.; Nijkamp, P.; ter Welle, C.

    2001-01-01

    Rational decision-making requires an assessment of advantages and disadvantages of choice possibilities, including non-market effects (such as externalities). This also applies to strategic decision-making in the transport sector (including aviation). In the past decades various decision support and

  19. A conceptual evolutionary aseismic decision support framework for hospitals

    Science.gov (United States)

    Hu, Yufeng; Dargush, Gary F.; Shao, Xiaoyun

    2012-12-01

    In this paper, aconceptual evolutionary framework for aseismic decision support for hospitalsthat attempts to integrate a range of engineering and sociotechnical models is presented. Genetic algorithms are applied to find the optimal decision sets. A case study is completed to demonstrate how the frameworkmay applytoa specific hospital.The simulations show that the proposed evolutionary decision support framework is able to discover robust policy sets in either uncertain or fixed environments. The framework also qualitatively identifies some of the characteristicbehavior of the critical care organization. Thus, by utilizing the proposedframework, the decision makers are able to make more informed decisions, especially toenhance the seismic safety of the hospitals.

  20. MOIDSS?- Mobile Online Intelligent Decision Support System, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — GRID has had a successfully completed Phase I 'Mobile Online Intelligent Decision Support System' (MOIDSS). The system developed into a total solution that supports...

  1. Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: An example from a vertigo phase III study with longitudinal count data as primary endpoint

    Directory of Open Access Journals (Sweden)

    Adrion Christine

    2012-09-01

    Full Text Available Abstract Background A statistical analysis plan (SAP is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. Methods We focus on generalized linear mixed models (GLMMs for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs. The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC or probability integral transform (PIT, and by using proper scoring rules (e.g. the logarithmic score. Results The instruments under study

  2. Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: an example from a vertigo phase III study with longitudinal count data as primary endpoint.

    Science.gov (United States)

    Adrion, Christine; Mansmann, Ulrich

    2012-09-10

    A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). The instruments under study provide excellent tools for preparing decisions

  3. Becoming a Mother: Supported Decision-Making in Context

    Science.gov (United States)

    Jamieson, Rhiann; Theodore, Kate; Raczka, Roman

    2016-01-01

    Little is known about how women with intellectual disabilities make decisions in relation to pregnancy. Social support is important for mothers with intellectual disabilities in many areas. This study explored how the support network influenced the decision-making of women with intellectual disabilities in relation to pregnancy. The study extended…

  4. Intelligent decision support system for operators of the supply ...

    African Journals Online (AJOL)

    Intelligent decision support system for operators of the supply department of oil and gas extracting industry. ... PROMOTING ACCESS TO AFRICAN RESEARCH ... abnormal situations, pre-crash sensing, industrial drilling, decision-making support systems. Full Text: EMAIL FREE FULL TEXT EMAIL FREE FULL TEXT

  5. Demonstration of decision support for real time operation

    DEFF Research Database (Denmark)

    Catterson, Victoria; MCarthur, Stephen; Chen, Minjiang

    ELECTRA Deliverable 8.2 reports on the demonstration of decision support within the future control room in light of voltage and frequency control in the 2030+ power system. The decision support must identify key threats and vulnerabilities, and propose and prioritise appropriate interventions....

  6. Decision support modeling for milk valorization

    NARCIS (Netherlands)

    Banaszewska, A.

    2014-01-01

    The research presented in this thesis concerns decision problems in practice that require structured, precise, scientific studies to provide strong, reliable answers. An opportunity to contribute to both practice and science emerged in 2008 when two large, Dutch dairy companies merged, creating

  7. Cost Decision Support in Product Design

    NARCIS (Netherlands)

    Liebers, A.; Kals, H.J.J.

    1997-01-01

    The constraints addressed in decision making during product design, process planning and production planning determine the admissible solution space for the manufacture of products. The solution space determines largely the costs that are incurred in the production process. In order to be able to

  8. Decision-support tools for climate change mitigation planning

    DEFF Research Database (Denmark)

    Puig, Daniel; Aparcana Robles, Sandra Roxana

    . For example, in the case of life-cycle analysis, the evaluation criterion entails that the impacts of interest are examined across the entire life-cycle of the product under study, from extraction of raw materials, to product disposal. Effectively, then, the choice of decision-support tool directs......This document describes three decision-support tools that can aid the process of planning climate change mitigation actions. The phrase ‘decision-support tools’ refers to science-based analytical procedures that facilitate the evaluation of planning options (individually or compared to alternative...... options) against a particular evaluation criterion or set of criteria. Most often decision-support tools are applied with the help of purpose-designed software packages and drawing on specialised databases.The evaluation criteria alluded to above define and characterise each decision-support tool...

  9. Decision support for mastitis on farms with an automatic milking system

    NARCIS (Netherlands)

    Steeneveld, W.

    2010-01-01

    For an optimal mastitis management on farms with an automatic milking system (AMS), two individual cow decisions are important. First, there is a need for decision support on which mastitis alerts have the highest priority for visual checking for clinical mastitis (CM). In essence, all cows with

  10. QLIKVIEW APPLICATION - SUPPORT IN DECISION MAKING

    Directory of Open Access Journals (Sweden)

    Luminita SERBANESCU

    2017-12-01

    Full Text Available Control over the company, an objective that any manager wants, can only be exercised on the basis of real and complex business data. For this, a higher level of application is required, with Business Intelligence applications that provide information that no one else can offer faster. Winning time can be used to identify other issues related to available information or activities that add value to the company. After all, management time is a decision, and the decision is valuable only if it occurs at the right time. In this article I presented the benefits of implementing a business intelligence solution in a company, as well as how to design analytical reports using the QlikView application.

  11. Decision support for redesigning wastewater treatment technologies.

    Science.gov (United States)

    McConville, Jennifer R; Künzle, Rahel; Messmer, Ulrike; Udert, Kai M; Larsen, Tove A

    2014-10-21

    This paper offers a methodology for structuring the design space for innovative process engineering technology development. The methodology is exemplified in the evaluation of a wide variety of treatment technologies for source-separated domestic wastewater within the scope of the Reinvent the Toilet Challenge. It offers a methodology for narrowing down the decision-making field based on a strict interpretation of treatment objectives for undiluted urine and dry feces and macroenvironmental factors (STEEPLED analysis) which influence decision criteria. Such an evaluation identifies promising paths for technology development such as focusing on space-saving processes or the need for more innovation in low-cost, energy-efficient urine treatment methods. Critical macroenvironmental factors, such as housing density, transportation infrastructure, and climate conditions were found to affect technology decisions regarding reactor volume, weight of outputs, energy consumption, atmospheric emissions, investment cost, and net revenue. The analysis also identified a number of qualitative factors that should be carefully weighed when pursuing technology development; such as availability of O&M resources, health and safety goals, and other ethical issues. Use of this methodology allows for coevolution of innovative technology within context constraints; however, for full-scale technology choices in the field, only very mature technologies can be evaluated.

  12. Intelligent Information System to support decision making.

    Directory of Open Access Journals (Sweden)

    Kathrin Rodríguez Llanes

    2010-06-01

    Full Text Available Making decisions is complicated in a generalized way, the materials and humans resources of the entity we belong to depends on it, such as the fulfillment of its goals. But when the situations are complex, making decisions turns into a very difficult work, due to the great amount of aspects to consider when making the right choice. To make this efficiently the administration must to consult an important volume of information, which generally, is scattered and in any different formats. That’s why appears the need of developing software that crowd together all that information and be capable of, by using powerful search engines and process algorithms improve the good decisions making process. Considering previous explanation, a complete freeware developed product is proposed, this constitutes a generic and multi-platform solution, that using artificial intelligence techniques, specifically the cases based reasoning, gives the possibility to leaders of any institution or organism of making the right choice in any situation.With client-server architecture, this system is consumed from web as a service and it can be perfectly integrated with a management system or the geographic information system to facilitate the business process.

  13. TUW @ TREC Clinical Decision Support Track 2015

    Science.gov (United States)

    2015-11-20

    between 2003 and 2013, and the ShARe/CLEF eHealth Evaluation Lab [15,3,11,12] running since 2013. Here we briefly describe the participation of Vienna...Mart́ınez, G. Zuccon, and J. R. M. Palotti. Overview of the share/clef ehealth evaluation lab 2014. In Information Access Evaluation. Mul- tilinguality...Symposium, IIiX ’14, pages 283–286. ACM, 2014. 11. J. Palotti, G. Zuccon, L. Goeuriot, L. Kelly, A. Hanbury, G. Jones, M. Lupu, and P. Pecina. Clef ehealth

  14. Cyborg practices: call-handlers and computerised decision support systems in urgent and emergency care.

    Science.gov (United States)

    Pope, Catherine; Halford, Susan; Turnbull, Joanne; Prichard, Jane

    2014-06-01

    This article draws on data collected during a 2-year project examining the deployment of a computerised decision support system. This computerised decision support system was designed to be used by non-clinical staff for dealing with calls to emergency (999) and urgent care (out-of-hours) services. One of the promises of computerised decisions support technologies is that they can 'hold' vast amounts of sophisticated clinical knowledge and combine it with decision algorithms to enable standardised decision-making by non-clinical (clerical) staff. This article draws on our ethnographic study of this computerised decision support system in use, and we use our analysis to question the 'automated' vision of decision-making in healthcare call-handling. We show that embodied and experiential (human) expertise remains central and highly salient in this work, and we propose that the deployment of the computerised decision support system creates something new, that this conjunction of computer and human creates a cyborg practice.

  15. Decision Performance Using Spatial Decision Support Systems: A Geospatial Reasoning Ability Perspective

    Science.gov (United States)

    Erskine, Michael A.

    2013-01-01

    As many consumer and business decision makers are utilizing Spatial Decision Support Systems (SDSS), a thorough understanding of how such decisions are made is crucial for the information systems domain. This dissertation presents six chapters encompassing a comprehensive analysis of the impact of geospatial reasoning ability on…

  16. Enhanced health E-decision literacy via interactive multi-criterial support

    DEFF Research Database (Denmark)

    Kaltoft, Mette Kjer; Almeida, J.; Moncho Mas, Vicent

    Healthcare lacks a generic language for decisional communication. We aim to enhance health decision literacy via specific e-decision support. Given the multi-criterial, preference-sensitive nature of decision-making, we implement the Multi-Criteria Decision Analysis (MCDA) technique online...... in an interactive and visual template (Annalisa), developing decision-specific tools at the clinical/personal and group/policy levels. Our current nationally funded project on bone health caters for home-prepared, informed and preference-based consent and taps into existing e-health infrastructures towards person...

  17. Personalised Multi-Criterial Online Decision Support for Siblings Considering Stem Cell Donation

    DEFF Research Database (Denmark)

    Kaltoft, Mette Kjer; Salkeld, Glenn; Dowie, Jack

    2016-01-01

    Person-centred decision support combines the best available information on the considerations that matter to the individual, with the importance the person attaches to those considerations. Nurses and other health professionals can benefit from being able to draw on this support within a clinical...... of a decision. By interactive decision support within a clinical conversation, each stakeholder can gain a personalised opinion, as well as increased generic health decision literacy [2]....... conversation. A case study and storyline on four siblings facing a transplant coordinator's call to donate stem cells to their brother [1] is 'translated' and used to demonstrate how an interactive multi-criteria aid can be developed for each within a conversational mode. The personalized dialogue and decision...

  18. Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment.

    Science.gov (United States)

    McMeekin, Peter; Flynn, Darren; Ford, Gary A; Rodgers, Helen; Gray, Jo; Thomson, Richard G

    2015-11-11

    Individualised prediction of outcomes can support clinical and shared decision making. This paper describes the building of such a model to predict outcomes with and without intravenous thrombolysis treatment following ischaemic stroke. A decision analytic model (DAM) was constructed to establish the likely balance of benefits and risks of treating acute ischaemic stroke with thrombolysis. Probability of independence, (modified Rankin score mRS ≤ 2), dependence (mRS 3 to 5) and death at three months post-stroke was based on a calibrated version of the Stroke-Thrombolytic Predictive Instrument using data from routinely treated stroke patients in the Safe Implementation of Treatments in Stroke (SITS-UK) registry. Predictions in untreated patients were validated using data from the Virtual International Stroke Trials Archive (VISTA). The probability of symptomatic intracerebral haemorrhage in treated patients was incorporated using a scoring model from Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST) data. The model predicts probabilities of haemorrhage, death, independence and dependence at 3-months, with and without thrombolysis, as a function of 13 patient characteristics. Calibration (and inclusion of additional predictors) of the Stroke-Thrombolytic Predictive Instrument (S-TPI) addressed issues of under and over prediction. Validation with VISTA data confirmed that assumptions about treatment effect were just. The C-statistics for independence and death in treated patients in the DAM were 0.793 and 0.771 respectively, and 0.776 for independence in untreated patients from VISTA. We have produced a DAM that provides an estimation of the likely benefits and risks of thrombolysis for individual patients, which has subsequently been embedded in a computerised decision aid to support better decision-making and informed consent.

  19. Therapy Decision Support Based on Recommender System Methods.

    Science.gov (United States)

    Gräßer, Felix; Beckert, Stefanie; Küster, Denise; Schmitt, Jochen; Abraham, Susanne; Malberg, Hagen; Zaunseder, Sebastian

    2017-01-01

    We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender , are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system.

  20. Therapy Decision Support Based on Recommender System Methods

    Directory of Open Access Journals (Sweden)

    Felix Gräßer

    2017-01-01

    Full Text Available We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system.

  1. Intelligent decision technology support in practice

    CERN Document Server

    Neves-Silva, Rui; Jain, Lakhmi; Phillips-Wren, Gloria; Watada, Junzo; Howlett, Robert

    2016-01-01

    This book contains a collection of innovative chapters emanating from topics raised during the 5th KES International Conference on Intelligent Decision Technologies (IDT), held during 2013 at Sesimbra, Portugal. The authors were invited to expand their original papers into a plethora of innovative chapters espousing IDT methodologies and applications. This book documents leading-edge contributions, representing advances in Knowledge-Based and Intelligent Information and Engineering System. It acknowledges that researchers recognize that society is familiar with modern Advanced Information Processing and increasingly expect richer IDT systems. Each chapter concentrates on the theory, design, development, implementation, testing or evaluation of IDT techniques or applications.  Anyone that wants to work with IDT or simply process knowledge should consider reading one or more chapters and focus on their technique of choice. Most readers will benefit from reading additional chapters to access alternative techniq...

  2. Clinical Decision Making of Nurses Working in Hospital Settings

    Directory of Open Access Journals (Sweden)

    Ida Torunn Bjørk

    2011-01-01

    Full Text Available This study analyzed nurses' perceptions of clinical decision making (CDM in their clinical practice and compared differences in decision making related to nurse demographic and contextual variables. A cross-sectional survey was carried out with 2095 nurses in four hospitals in Norway. A 24-item Nursing Decision Making Instrument based on cognitive continuum theory was used to explore how nurses perceived their CDM when meeting an elective patient for the first time. Data were analyzed with descriptive frequencies, t-tests, Chi-Square test, and linear regression. Nurses' decision making was categorized into analytic-systematic, intuitive-interpretive, and quasi-rational models of CDM. Most nurses reported the use of quasi-rational models during CDM thereby supporting the tenet that cognition most often includes properties of both analysis and intuition. Increased use of intuitive-interpretive models of CDM was associated with years in present job, further education, male gender, higher age, and working in predominantly surgical units.

  3. Towards an integrated approach in supporting microbiological food safety decisions

    NARCIS (Netherlands)

    Havelaar, A.H.; Bräunig, J.; Christiansen, K.; Cornu, M.; Hald, T.; Mangen, M.J.J.; Molbak, K.; Pielaat, A.; Snary, E.; Pelt, van W.; Velthuis, A.G.J.; Wahlström, H.

    2007-01-01

    Decisions on food safety involve consideration of a wide range of concerns including the public health impact of foodborne illness, the economic importance of the agricultural sector and the food industry, and the effectiveness and efficiency of interventions. To support such decisions, we propose

  4. A Gaussian decision-support tool for engineering design process

    NARCIS (Netherlands)

    Rajabali Nejad, Mohammadreza; Spitas, Christos

    2013-01-01

    Decision-making in design is of great importance, resulting in success or failure of a system (Liu et al., 2010; Roozenburg and Eekels, 1995; Spitas, 2011a). This paper describes a robust decision-support tool for engineering design process, which can be used throughout the design process in either

  5. A Decision Support System for Corporations Cybersecurity Management

    OpenAIRE

    Roldán-Molina, G.; Almache-Cueva, M.; Silva-Rabadão, C.; Yevseyeva, Iryna; Basto-Fernandes, V.

    2017-01-01

    This paper presents ongoing work on a decision aiding software intended to support cyber risks and cyber threats analysis of an information and communications technological infrastructure. The software will help corporations Chief Information Security Officers on cyber security risk analysis, decision-making, prevention measures and risk strategies for the infrastructure and information assets protection.

  6. A spatial decision support system for pipe-break susceptibility ...

    African Journals Online (AJOL)

    lying properties. Existing decision support systems available in the field of water distribution system maintenance mainly focus on leak detection and pipe rehabilitation/replacement strategies. These existing systems, however, do not address the ...