Decision Mining Revisited – Discovering Overlapping Rules

NARCIS (Netherlands)

Mannhardt, F.; de Leoni, M.; Reijers, H.A.; van der Aalst, W.M.P.

2016-01-01

Decision mining enriches process models with rules underlying decisions in processes using historical process execution data. Choices between multiple activities are specified through rules defined over process data. Existing decision mining methods focus on discovering mutually-exclusive rules,

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.

A diagnosis-based clinical decision rule for spinal pain part 2: review of the literature

Directory of Open Access Journals (Sweden)

Hurwitz Eric L

2008-08-01

Full Text Available Abstract Background Spinal pain is a common and often disabling problem. The research on various treatments for spinal pain has, for the most part, suggested that while several interventions have demonstrated mild to moderate short-term benefit, no single treatment has a major impact on either pain or disability. There is great need for more accurate diagnosis in patients with spinal pain. In a previous paper, the theoretical model of a diagnosis-based clinical decision rule was presented. The approach is designed to provide the clinician with a strategy for arriving at a specific working diagnosis from which treatment decisions can be made. It is based on three questions of diagnosis. In the current paper, the literature on the reliability and validity of the assessment procedures that are included in the diagnosis-based clinical decision rule is presented. Methods The databases of Medline, Cinahl, Embase and MANTIS were searched for studies that evaluated the reliability and validity of clinic-based diagnostic procedures for patients with spinal pain that have relevance for questions 2 (which investigates characteristics of the pain source and 3 (which investigates perpetuating factors of the pain experience. In addition, the reference list of identified papers and authors' libraries were searched. Results A total of 1769 articles were retrieved, of which 138 were deemed relevant. Fifty-one studies related to reliability and 76 related to validity. One study evaluated both reliability and validity. Conclusion Regarding some aspects of the DBCDR, there are a number of studies that allow the clinician to have a reasonable degree of confidence in his or her findings. This is particularly true for centralization signs, neurodynamic signs and psychological perpetuating factors. There are other aspects of the DBCDR in which a lesser degree of confidence is warranted, and in which further research is needed.

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.

Design and Analysis of Decision Rules via Dynamic Programming

KAUST Repository

Amin, Talha M.

2017-04-24

The areas of machine learning, data mining, and knowledge representation have many different formats used to represent information. Decision rules, amongst these formats, are the most expressive and easily-understood by humans. In this thesis, we use dynamic programming to design decision rules and analyze them. The use of dynamic programming allows us to work with decision rules in ways that were previously only possible for brute force methods. Our algorithms allow us to describe the set of all rules for a given decision table. Further, we can perform multi-stage optimization by repeatedly reducing this set to only contain rules that are optimal with respect to selected criteria. One way that we apply this study is to generate small systems with short rules by simulating a greedy algorithm for the set cover problem. We also compare maximum path lengths (depth) of deterministic and non-deterministic decision trees (a non-deterministic decision tree is effectively a complete system of decision rules) with regards to Boolean functions. Another area of advancement is the presentation of algorithms for constructing Pareto optimal points for rules and rule systems. This allows us to study the existence of “totally optimal” decision rules (rules that are simultaneously optimal with regards to multiple criteria). We also utilize Pareto optimal points to compare and rate greedy heuristics with regards to two criteria at once. Another application of Pareto optimal points is the study of trade-offs between cost and uncertainty which allows us to find reasonable systems of decision rules that strike a balance between length and accuracy.

Dynamic programming approach for partial decision rule optimization

KAUST Repository

Amin, Talha

2012-10-04

This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between number of rows in a decision table T and number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δ γ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δ γ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.

Dynamic programming approach for partial decision rule optimization

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2012-01-01

This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between number of rows in a decision table T and number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δ γ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δ γ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.

Optimization of decision rule complexity for decision tables with many-valued decisions

KAUST Repository

Azad, Mohammad

2013-10-01

We describe new heuristics to construct decision rules for decision tables with many-valued decisions from the point of view of length and coverage which are enough good. We use statistical test to find leaders among the heuristics. After that, we compare our results with optimal result obtained by dynamic programming algorithms. The average percentage of relative difference between length (coverage) of constructed and optimal rules is at most 6.89% (15.89%, respectively) for leaders which seems to be a promising result. © 2013 IEEE.

Transformative decision rules, permutability, and non-sequential framing of decision problems

NARCIS (Netherlands)

Peterson, M.B.

2004-01-01

The concept of transformative decision rules provides auseful tool for analyzing what is often referred to as the`framing', or `problem specification', or `editing' phase ofdecision making. In the present study we analyze a fundamentalaspect of transformative decision rules, viz. permutability. A

The optimum decision rules for the oddity task.

Science.gov (United States)

Versfeld, N J; Dai, H; Green, D M

1996-01-01

This paper presents the optimum decision rule for an m-interval oddity task in which m-1 intervals contain the same signal and one is different or odd. The optimum decision rule depends on the degree of correlation among observations. The present approach unifies the different strategies that occur with "roved" or "fixed" experiments (Macmillan & Creelman, 1991, p. 147). It is shown that the commonly used decision rule for an m-interval oddity task corresponds to the special case of highly correlated observations. However, as is also true for the same-different paradigm, there exists a different optimum decision rule when the observations are independent. The relation between the probability of a correct response and d' is derived for the three-interval oddity task. Tables are presented of this relation for the three-, four-, and five-interval oddity task. Finally, an experimental method is proposed that allows one to determine the decision rule used by the observer in an oddity experiment.

Dynamic programming approach to optimization of approximate decision rules

KAUST Repository

Amin, Talha

2013-02-01

This paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure R(T) which is the number of unordered pairs of rows with different decisions in the decision table T. For a nonnegative real number β, we consider β-decision rules that localize rows in subtables of T with uncertainty at most β. Our algorithm constructs a directed acyclic graph Δβ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most β. The graph Δβ(T) allows us to describe the whole set of so-called irredundant β-decision rules. We can describe all irredundant β-decision rules with minimum length, and after that among these rules describe all rules with maximum coverage. We can also change the order of optimization. The consideration of irredundant rules only does not change the results of optimization. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository. © 2012 Elsevier Inc. All rights reserved.

Rules of Thumb in Life-Cycle Saving Decisions

OpenAIRE

Winter, Joachim; Schlafmann, Kathrin; Rodepeter, Ralf

2011-01-01

We analyse life-cycle saving decisions when households use simple heuristics, or rules of thumb, rather than solve the underlying intertemporal optimization problem. We simulate life-cycle saving decisions using three simple rules and compute utility losses relative to the solution of the optimization problem. Our simulations suggest that utility losses induced by following simple decision rules are relatively low. Moreover, the two main saving motives re ected by the canonical life-cyc...

Decision rules and group rationality: cognitive gain or standstill?

Directory of Open Access Journals (Sweden)

Petru Lucian Curşeu

Full Text Available Recent research in group cognition points towards the existence of collective cognitive competencies that transcend individual group members' cognitive competencies. Since rationality is a key cognitive competence for group decision making, and group cognition emerges from the coordination of individual cognition during social interactions, this study tests the extent to which collaborative and consultative decision rules impact the emergence of group rationality. Using a set of decision tasks adapted from the heuristics and biases literature, we evaluate rationality as the extent to which individual choices are aligned with a normative ideal. We further operationalize group rationality as cognitive synergy (the extent to which collective rationality exceeds average or best individual rationality in the group, and we test the effect of collaborative and consultative decision rules in a sample of 176 groups. Our results show that the collaborative decision rule has superior synergic effects as compared to the consultative decision rule. The ninety one groups working in a collaborative fashion made more rational choices (above and beyond the average rationality of their members than the eighty five groups working in a consultative fashion. Moreover, the groups using a collaborative decision rule were closer to the rationality of their best member than groups using consultative decision rules. Nevertheless, on average groups did not outperformed their best member. Therefore, our results reveal how decision rules prescribing interpersonal interactions impact on the emergence of collective cognitive competencies. They also open potential venues for further research on the emergence of collective rationality in human decision-making groups.

Decision rules and group rationality: cognitive gain or standstill?

Science.gov (United States)

Curşeu, Petru Lucian; Jansen, Rob J G; Chappin, Maryse M H

2013-01-01

Recent research in group cognition points towards the existence of collective cognitive competencies that transcend individual group members' cognitive competencies. Since rationality is a key cognitive competence for group decision making, and group cognition emerges from the coordination of individual cognition during social interactions, this study tests the extent to which collaborative and consultative decision rules impact the emergence of group rationality. Using a set of decision tasks adapted from the heuristics and biases literature, we evaluate rationality as the extent to which individual choices are aligned with a normative ideal. We further operationalize group rationality as cognitive synergy (the extent to which collective rationality exceeds average or best individual rationality in the group), and we test the effect of collaborative and consultative decision rules in a sample of 176 groups. Our results show that the collaborative decision rule has superior synergic effects as compared to the consultative decision rule. The ninety one groups working in a collaborative fashion made more rational choices (above and beyond the average rationality of their members) than the eighty five groups working in a consultative fashion. Moreover, the groups using a collaborative decision rule were closer to the rationality of their best member than groups using consultative decision rules. Nevertheless, on average groups did not outperformed their best member. Therefore, our results reveal how decision rules prescribing interpersonal interactions impact on the emergence of collective cognitive competencies. They also open potential venues for further research on the emergence of collective rationality in human decision-making groups.

Comparison of some classification algorithms based on deterministic and nondeterministic decision rules

KAUST Repository

Delimata, Paweł

2010-01-01

We discuss two, in a sense extreme, kinds of nondeterministic rules in decision tables. The first kind of rules, called as inhibitory rules, are blocking only one decision value (i.e., they have all but one decisions from all possible decisions on their right hand sides). Contrary to this, any rule of the second kind, called as a bounded nondeterministic rule, can have on the right hand side only a few decisions. We show that both kinds of rules can be used for improving the quality of classification. In the paper, two lazy classification algorithms of polynomial time complexity are considered. These algorithms are based on deterministic and inhibitory decision rules, but the direct generation of rules is not required. Instead of this, for any new object the considered algorithms extract from a given decision table efficiently some information about the set of rules. Next, this information is used by a decision-making procedure. The reported results of experiments show that the algorithms based on inhibitory decision rules are often better than those based on deterministic decision rules. We also present an application of bounded nondeterministic rules in construction of rule based classifiers. We include the results of experiments showing that by combining rule based classifiers based on minimal decision rules with bounded nondeterministic rules having confidence close to 1 and sufficiently large support, it is possible to improve the classification quality. © 2010 Springer-Verlag.

Greedy Algorithm for the Construction of Approximate Decision Rules for Decision Tables with Many-Valued Decisions

KAUST Repository

Azad, Mohammad

2016-10-20

The paper is devoted to the study of a greedy algorithm for construction of approximate decision rules. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. We consider bounds on the precision of this algorithm relative to the length of rules. To illustrate proposed approach we study a problem of recognition of labels of points in the plain. This paper contains also results of experiments with modified decision tables from UCI Machine Learning Repository.

Greedy Algorithm for the Construction of Approximate Decision Rules for Decision Tables with Many-Valued Decisions

KAUST Repository

Azad, Mohammad; Moshkov, Mikhail; Zielosko, Beata

2016-01-01

The paper is devoted to the study of a greedy algorithm for construction of approximate decision rules. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. We consider bounds on the precision of this algorithm relative to the length of rules. To illustrate proposed approach we study a problem of recognition of labels of points in the plain. This paper contains also results of experiments with modified decision tables from UCI Machine Learning Repository.

Implementation of adapted PECARN decision rule for children with minor head injury in the pediatric emergency department.

Science.gov (United States)

Bressan, Silvia; Romanato, Sabrina; Mion, Teresa; Zanconato, Stefania; Da Dalt, Liviana

2012-07-01

Of the currently published clinical decision rules for the management of minor head injury (MHI) in children, the Pediatric Emergency Care Applied Research Network (PECARN) rule, derived and validated in a large multicenter prospective study cohort, with high methodologic standards, appears to be the best clinical decision rule to accurately identify children at very low risk of clinically important traumatic brain injuries (ciTBI) in the pediatric emergency department (PED). This study describes the implementation of an adapted version of the PECARN rule in a tertiary care academic PED in Italy and evaluates implementation success, in terms of medical staff adherence and satisfaction, as well as its effects on clinical practice. The adapted PECARN decision rule algorithms for children (one for those younger than 2 years and one for those older than 2 years) were actively implemented in the PED of Padova, Italy, for a 6-month testing period. Adherence and satisfaction of medical staff to the new rule were calculated. Data from 356 visits for MHI during PECARN rule implementation and those of 288 patients attending the PED for MHI in the previous 6 months were compared for changes in computed tomography (CT) scan rate, ciTBI rate (defined as death, neurosurgery, intubation for longer than 24 hours, or hospital admission at least for two nights associated with TBI) and return visits for symptoms or signs potentially related to MHI. The safety and efficacy of the adapted PECARN rule in clinical practice were also calculated. Adherence to the adapted PECARN rule was 93.5%. The percentage of medical staff satisfied with the new rule, in terms of usefulness and ease of use for rapid decision-making, was significantly higher (96% vs. 51%, puse of the adapted PECARN rule in clinical practice was 100% (95% CI=36.8 to 100; three of three patients with ciTBI who received CT scan at first evaluation), while efficacy was 92.3% (95% CI=89 to 95; 326 of 353 patients without ci

Decision rule classifiers for multi-label decision tables

KAUST Repository

Alsolami, Fawaz

2014-01-01

Recently, multi-label classification problem has received significant attention in the research community. This paper is devoted to study the effect of the considered rule heuristic parameters on the generalization error. The results of experiments for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion. © 2014 Springer International Publishing.

The optimum decision rules for the oddity task

NARCIS (Netherlands)

Versfeld, N.J.; Dai, H.; Green, D.M.

1996-01-01

This paper presents the optimum decision rule for an m-interval oddity task in which m-1 intervals contain the same signal and one is different or odd. The optimum decision rule depends on the degree of correlation among observations. The present approach unifies the different strategies that occur

Business Rules Definition for Decision Support System Using Matrix Grammar

Directory of Open Access Journals (Sweden)

Eva Zámečníková

2016-06-01

Full Text Available This paper deals with formalization of business rules by formal grammars. In our work we focus on methods for high frequency data processing. We process data by using complex event platforms (CEP which allow to process high volume of data in nearly real time. Decision making process is contained by one level of processing of CEP. Business rules are used for decision making process description. For the business rules formalization we chose matrix grammar. The use of formal grammars is quite natural as the structure of rules and its rewriting is very similar both for the business rules and for formal grammar. In addition the matrix grammar allows to simulate dependencies and correlations between the rules. The result of this work is a model for data processing of knowledge-based decision support system described by the rules of formal grammar. This system will support the decision making in CEP. This solution may contribute to the speedup of decision making process in complex event processing and also to the formal verification of these systems.

Decision rule classifiers for multi-label decision tables

KAUST Repository

Alsolami, Fawaz; Azad, Mohammad; Chikalov, Igor; Moshkov, Mikhail

2014-01-01

for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion. © 2014 Springer International

Testing Decision Rules for Multiattribute Decision Making

NARCIS (Netherlands)

Seidl, C.; Traub, S.

1996-01-01

This paper investigates the existence of an editing phase and studies the com- pliance of subjects' behaviour with the most popular multiattribute decision rules. We observed that our data comply well with the existence of an editing phase, at least if we allow for a natural error rate of some 25%.

Optimization of decision rules based on dynamic programming approach

KAUST Repository

Zielosko, Beata

2014-01-14

This chapter is devoted to the study of an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure that is the difference between number of rows in a given decision table and the number of rows labeled with the most common decision for this table divided by the number of rows in the decision table. We fix a threshold γ, such that 0 ≤ γ < 1, and study so-called γ-decision rules (approximate decision rules) that localize rows in subtables which uncertainty is at most γ. Presented algorithm constructs a directed acyclic graph Δ γ T which nodes are subtables of the decision table T given by pairs "attribute = value". The algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The chapter contains also results of experiments with decision tables from UCI Machine Learning Repository. © 2014 Springer International Publishing Switzerland.

Classifiers based on optimal decision rules

KAUST Repository

Amin, Talha

2013-11-25

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

Classifiers based on optimal decision rules

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2013-01-01

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

An overview of bipolar qualitative decision rules

Science.gov (United States)

Bonnefon, Jean-Francois; Dubois, Didier; Fargier, Hélène

Making a good decision is often a matter of listing and comparing positive and negative arguments, as studies in cognitive psychology have shown. In such cases, the evaluation scale should be considered bipolar, that is, negative and positive values are explicitly distinguished. Generally, positive and negative features are evaluated separately, as done in Cumulative Prospect Theory. However, contrary to the latter framework that presupposes genuine numerical assessments, decisions are often made on the basis of an ordinal ranking of the pros and the cons, and focusing on the most salient features, i.e., the decision process is qualitative. In this paper, we report on a project aiming at characterizing several decision rules, based on possibilistic order of magnitude reasoning, and tailored for the joint handling of positive and negative affects, and at testing their empirical validity. The simplest rules can be viewed as extensions of the maximin and maximax criteria to the bipolar case and, like them, suffer from a lack of discrimination power. More decisive rules that refine them are also proposed. They account for both the principle of Pareto-efficiency and the notion of order of magnitude reasoning. The most decisive one uses a lexicographic ranking of the pros and cons. It comes down to a special case of Cumulative Prospect Theory, and subsumes the “Take the best” heuristic.

Optimization of approximate decision rules relative to number of misclassifications

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2012-01-01

In the paper, we study an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the number of misclassifications. We introduce an uncertainty measure J(T) which is a difference between the number of rows in a decision table T and the number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules that localize rows in subtables of T with uncertainty at most γ. The presented algorithm constructs a directed acyclic graph Δγ(T). Based on this graph we can describe the whole set of so-called irredundant γ-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 The authors and IOS Press. All rights reserved.

Optimization of approximate decision rules relative to number of misclassifications

KAUST Repository

Amin, Talha

2012-12-01

In the paper, we study an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the number of misclassifications. We introduce an uncertainty measure J(T) which is a difference between the number of rows in a decision table T and the number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules that localize rows in subtables of T with uncertainty at most γ. The presented algorithm constructs a directed acyclic graph Δγ(T). Based on this graph we can describe the whole set of so-called irredundant γ-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 The authors and IOS Press. All rights reserved.

Concurrent approach for evolving compact decision rule sets

Science.gov (United States)

Marmelstein, Robert E.; Hammack, Lonnie P.; Lamont, Gary B.

1999-02-01

The induction of decision rules from data is important to many disciplines, including artificial intelligence and pattern recognition. To improve the state of the art in this area, we introduced the genetic rule and classifier construction environment (GRaCCE). It was previously shown that GRaCCE consistently evolved decision rule sets from data, which were significantly more compact than those produced by other methods (such as decision tree algorithms). The primary disadvantage of GRaCCe, however, is its relatively poor run-time execution performance. In this paper, a concurrent version of the GRaCCE architecture is introduced, which improves the efficiency of the original algorithm. A prototype of the algorithm is tested on an in- house parallel processor configuration and the results are discussed.

Rough set and rule-based multicriteria decision aiding

Directory of Open Access Journals (Sweden)

Roman Slowinski

2012-08-01

Full Text Available The aim of multicriteria decision aiding is to give the decision maker a recommendation concerning a set of objects evaluated from multiple points of view called criteria. Since a rational decision maker acts with respect to his/her value system, in order to recommend the most-preferred decision, one must identify decision maker's preferences. In this paper, we focus on preference discovery from data concerning some past decisions of the decision maker. We consider the preference model in the form of a set of "if..., then..." decision rules discovered from the data by inductive learning. To structure the data prior to induction of rules, we use the Dominance-based Rough Set Approach (DRSA. DRSA is a methodology for reasoning about data, which handles ordinal evaluations of objects on considered criteria and monotonic relationships between these evaluations and the decision. We review applications of DRSA to a large variety of multicriteria decision problems.

WINE ADVISOR EXPERT SYSTEM USING DECISION RULES

Directory of Open Access Journals (Sweden)

Dinuca Elena Claudia

2013-07-01

Full Text Available In this article I focus on developing an expert system for advising the choice of wine that best matches a specific occasion. An expert system is a computer application that performs a task that would be performed by a human expert. The implementation is done using Delphi programming language. I used to represent the knowledge bases a set of rules. The rules are of type IF THEN ELSE rules, decision rules based on different important wine features.

Design and Analysis of Decision Rules via Dynamic Programming

KAUST Repository

Amin, Talha M.

2017-01-01

Another area of advancement is the presentation of algorithms for constructing Pareto optimal points for rules and rule systems. This allows us to study the existence of “totally optimal” decision rules

Decision Analysis of Dynamic Spectrum Access Rules

Energy Technology Data Exchange (ETDEWEB)

Juan D. Deaton; Luiz A. DaSilva; Christian Wernz

2011-12-01

A current trend in spectrum regulation is to incorporate spectrum sharing through the design of spectrum access rules that support Dynamic Spectrum Access (DSA). This paper develops a decision-theoretic framework for regulators to assess the impacts of different decision rules on both primary and secondary operators. We analyze access rules based on sensing and exclusion areas, which in practice can be enforced through geolocation databases. Our results show that receiver-only sensing provides insufficient protection for primary and co-existing secondary users and overall low social welfare. On the other hand, using sensing information between the transmitter and receiver of a communication link, provides dramatic increases in system performance. The performance of using these link end points is relatively close to that of using many cooperative sensing nodes associated to the same access point and large link exclusion areas. These results are useful to regulators and network developers in understanding in developing rules for future DSA regulation.

Optimization of β-decision rules relative to number of misclassifications

KAUST Repository

Zielosko, Beata

2012-01-01

In the paper, we present an algorithm for optimization of approximate decision rules relative to the number of misclassifications. The considered algorithm is based on extensions of dynamic programming and constructs a directed acyclic graph Δ β (T). Based on this graph we can describe the whole set of so-called irredundant β-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 Springer-Verlag.

The REFER (REFer for EchocaRdiogram protocol: a prospective validation of a clinical decision rule, NT-proBNP, or their combination, in the diagnosis of heart failure in primary care. Rationale and design

Directory of Open Access Journals (Sweden)

Tait Lynda

2012-10-01

Full Text Available Abstract Background Heart failure is a major cause of mortality and morbidity. As mortality rates are high, it is important that patients seen by general practitioners with symptoms suggestive of heart failure are identified quickly and treated appropriately. Identifying patients with heart failure or deciding which patients need further tests is a challenge. All patients with suspected heart failure should be diagnosed using objective tests such as echocardiography, but it is expensive, often delayed, and limited by the significant skill shortage of trained echocardiographers. Alternative approaches for diagnosing heart failure are currently limited. Clinical decision tools that combine clinical signs, symptoms or patient characteristics are designed to be used to support clinical decision-making and validated according to strict methodological procedures. The REFER Study aims to determine the accuracy and cost-effectiveness of our previously derived novel, simple clinical decision rule, a natriuretic peptide assay, or their combination, in the triage for referral for echocardiography of symptomatic adult patients who present in general practice with symptoms suggestive of heart failure. Methods/design This is a prospective, Phase II observational, diagnostic validation study of a clinical decision rule, natriuretic peptides or their combination, for diagnosing heart failure in primary care. Consecutive adult primary care patients 55 years of age or over presenting to their general practitioner with a chief complaint of recent new onset shortness of breath, lethargy or peripheral ankle oedema of over 48 hours duration, with no obvious recurrent, acute or self-limiting cause will be enrolled. Our reference standard is based upon a three step expert specialist consensus using echocardiography and clinical variables and tests. Discussion Our clinical decision rule offers a potential solution to the diagnostic challenge of providing a timely and

Online learning algorithm for ensemble of decision rules

KAUST Repository

Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2011-01-01

We describe an online learning algorithm that builds a system of decision rules for a classification problem. Rules are constructed according to the minimum description length principle by a greedy algorithm or using the dynamic programming approach

Diagnostic accuracy of clinical decision rules to exclude fractures in acute ankle injuries : systematic review and meta-analysis

NARCIS (Netherlands)

Barelds, Ingrid; Krijnen, Wim P; van de Leur, Johannes P; van der Schans, Cees P; Goddard, Robert J

BACKGROUND: Ankle decision rules are developed to expedite patient care and reduce the number of radiographs of the ankle and foot. Currently, only three systematic reviews have been conducted on the accuracy of the Ottawa Ankle and Foot Rules (OAFR) in adults and children. However, no systematic

Performance of thirteen clinical rules to distinguish bacterial and presumed viral meningitis in Vietnamese children.

Directory of Open Access Journals (Sweden)

Nguyen Tien Huy

Full Text Available BACKGROUND AND PURPOSE: Successful outcomes from bacterial meningitis require rapid antibiotic treatment; however, unnecessary treatment of viral meningitis may lead to increased toxicities and expense. Thus, improved diagnostics are required to maximize treatment and minimize side effects and cost. Thirteen clinical decision rules have been reported to identify bacterial from viral meningitis. However, few rules have been tested and compared in a single study, while several rules are yet to be tested by independent researchers or in pediatric populations. Thus, simultaneous test and comparison of these rules are required to enable clinicians to select an optimal diagnostic rule for bacterial meningitis in settings and populations similar to ours. METHODS: A retrospective cross-sectional study was conducted at the Infectious Department of Pediatric Hospital Number 1, Ho Chi Minh City, Vietnam. The performance of the clinical rules was evaluated by area under a receiver operating characteristic curve (ROC-AUC using the method of DeLong and McNemar test for specificity comparison. RESULTS: Our study included 129 patients, of whom 80 had bacterial meningitis and 49 had presumed viral meningitis. Spanos's rule had the highest AUC at 0.938 but was not significantly greater than other rules. No rule provided 100% sensitivity with a specificity higher than 50%. Based on our calculation of theoretical sensitivity and specificity, we suggest that a perfect rule requires at least four independent variables that posses both sensitivity and specificity higher than 85-90%. CONCLUSIONS: No clinical decision rules provided an acceptable specificity (>50% with 100% sensitivity when applying our data set in children. More studies in Vietnam and developing countries are required to develop and/or validate clinical rules and more very good biomarkers are required to develop such a perfect rule.

ABOUT CLINICAL EXPERT SYSTEM BASED ON RULES USING DATA MINING TECHNOLOGY

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V. P. Martsenyuk

2015-05-01

Full Text Available In the work the topics of software implementation of rule induction method based on sequential covering algorithm are considered. Such approach allows us to develop clinical decision support system. The project is implemented within Netbeans IDE based on Java-classes.

Online learning algorithm for ensemble of decision rules

KAUST Repository

Chikalov, Igor

2011-01-01

We describe an online learning algorithm that builds a system of decision rules for a classification problem. Rules are constructed according to the minimum description length principle by a greedy algorithm or using the dynamic programming approach. © 2011 Springer-Verlag.

Decision Rules, Trees and Tests for Tables with Many-valued Decisions–comparative Study

KAUST Repository

Azad, Mohammad; Zielosko, Beata; Moshkov, Mikhail; Chikalov, Igor

2013-01-01

In this paper, we present three approaches for construction of decision rules for decision tables with many-valued decisions. We construct decision rules directly for rows of decision table, based on paths in decision tree, and based on attributes contained in a test (super-reduct). Experimental results for the data sets taken from UCI Machine Learning Repository, contain comparison of the maximum and the average length of rules for the mentioned approaches.

Decision Rules, Trees and Tests for Tables with Many-valued Decisions–comparative Study

KAUST Repository

Azad, Mohammad

2013-10-04

In this paper, we present three approaches for construction of decision rules for decision tables with many-valued decisions. We construct decision rules directly for rows of decision table, based on paths in decision tree, and based on attributes contained in a test (super-reduct). Experimental results for the data sets taken from UCI Machine Learning Repository, contain comparison of the maximum and the average length of rules for the mentioned approaches.

Dynamic Programming Approach for Exact Decision Rule Optimization

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2013-01-01

This chapter is devoted to the study of an extension of dynamic programming approach that allows sequential optimization of exact decision rules relative to the length and coverage. It contains also results of experiments with decision tables from

Relationships between length and coverage of decision rules

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2014-01-01

The paper describes a new tool for study relationships between length and coverage of exact decision rules. This tool is based on dynamic programming approach. We also present results of experiments with decision tables from UCI Machine Learning Repository.

Relationships between length and coverage of decision rules

KAUST Repository

Amin, Talha

2014-02-14

The paper describes a new tool for study relationships between length and coverage of exact decision rules. This tool is based on dynamic programming approach. We also present results of experiments with decision tables from UCI Machine Learning Repository.

Consultation system with knowledge representation by decision rules

Energy Technology Data Exchange (ETDEWEB)

Senne, E L.F.; Simoni, P O

1982-04-01

The use of decision rules in the representation of empirical knowledge supplied by application domain experts is discussed. Based on this representation, a system is described which employs artificial intelligence techniques to yield inferences within a specific domain. Three modules composing the system are described: the acquisition one, that allows the insertion of new rules; the diagnostic one, that uses rules in the inference process; and the explanation one, that exhibits reasons for each system action.

Dynamic Programming Approach for Exact Decision Rule Optimization

KAUST Repository

Amin, Talha

2013-01-01

This chapter is devoted to the study of an extension of dynamic programming approach that allows sequential optimization of exact decision rules relative to the length and coverage. It contains also results of experiments with decision tables from UCI Machine Learning Repository. © Springer-Verlag Berlin Heidelberg 2013.

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.

Portable Rule Extraction Method for Neural Network Decisions Reasoning

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Darius PLIKYNAS

2005-08-01

Full Text Available Neural network (NN methods are sometimes useless in practical applications, because they are not properly tailored to the particular market's needs. We focus thereinafter specifically on financial market applications. NNs have not gained full acceptance here yet. One of the main reasons is the "Black Box" problem (lack of the NN decisions explanatory power. There are though some NN decisions rule extraction methods like decompositional, pedagogical or eclectic, but they suffer from low portability of the rule extraction technique across various neural net architectures, high level of granularity, algorithmic sophistication of the rule extraction technique etc. The authors propose to eliminate some known drawbacks using an innovative extension of the pedagogical approach. The idea is exposed by the use of a widespread MLP neural net (as a common tool in the financial problems' domain and SOM (input data space clusterization. The feedback of both nets' performance is related and targeted through the iteration cycle by achievement of the best matching between the decision space fragments and input data space clusters. Three sets of rules are generated algorithmically or by fuzzy membership functions. Empirical validation of the common financial benchmark problems is conducted with an appropriately prepared software solution.

Unanimity rule and organizational decision-making : a simulation model

NARCIS (Netherlands)

Romme, A.G.L.

2004-01-01

Unanimity rule is an important benchmark for evaluating outcomes of decisions in the social sciences. However, organizational researchers tend to ignore unanimous decision making, for example, because unanimity may be difficult to realize in large groups and may suffer from individual participants

46 CFR 201.3 - Authentication of rules, orders, determinations and decisions of the Administration.

Science.gov (United States)

2010-10-01

... 46 Shipping 8 2010-10-01 2010-10-01 false Authentication of rules, orders, determinations and decisions of the Administration. 201.3 Section 201.3 Shipping MARITIME ADMINISTRATION, DEPARTMENT OF....3 Authentication of rules, orders, determinations and decisions of the Administration. All rules...

Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic.

Science.gov (United States)

Dehghani Soufi, Mahsa; Samad-Soltani, Taha; Shams Vahdati, Samad; Rezaei-Hachesu, Peyman

2018-06-01

Fast and accurate patient triage for the response process is a critical first step in emergency situations. This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes time, and is at risk of human errors. This study aims to design and evaluate a decision support system (DSS) to determine the triage level. A combination of the Rule-Based Reasoning (RBR) and Fuzzy Logic Classifier (FLC) approaches were used to predict the triage level of patients according to the triage specialist's opinions and Emergency Severity Index (ESI) guidelines. RBR was applied for modeling the first to fourth decision points of the ESI algorithm. The data relating to vital signs were used as input variables and modeled using fuzzy logic. Narrative knowledge was converted to If-Then rules using XML. The extracted rules were then used to create the rule-based engine and predict the triage levels. Fourteen RBR and 27 fuzzy rules were extracted and used in the rule-based engine. The performance of the system was evaluated using three methods with real triage data. The accuracy of the clinical decision support systems (CDSSs; in the test data) was 99.44%. The evaluation of the error rate revealed that, when using the traditional method, 13.4% of the patients were miss-triaged, which is statically significant. The completeness of the documentation also improved from 76.72% to 98.5%. Designed system was effective in determining the triage level of patients and it proved helpful for nurses as they made decisions, generated nursing diagnoses based on triage guidelines. The hybrid approach can reduce triage misdiagnosis in a highly accurate manner and improve the triage outcomes. Copyright © 2018 Elsevier B.V. All rights reserved.

A simple threshold rule is sufficient to explain sophisticated collective decision-making.

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Elva J H Robinson

Full Text Available Decision-making animals can use slow-but-accurate strategies, such as making multiple comparisons, or opt for simpler, faster strategies to find a 'good enough' option. Social animals make collective decisions about many group behaviours including foraging and migration. The key to the collective choice lies with individual behaviour. We present a case study of a collective decision-making process (house-hunting ants, Temnothorax albipennis, in which a previously proposed decision strategy involved both quality-dependent hesitancy and direct comparisons of nests by scouts. An alternative possible decision strategy is that scouting ants use a very simple quality-dependent threshold rule to decide whether to recruit nest-mates to a new site or search for alternatives. We use analytical and simulation modelling to demonstrate that this simple rule is sufficient to explain empirical patterns from three studies of collective decision-making in ants, and can account parsimoniously for apparent comparison by individuals and apparent hesitancy (recruitment latency effects, when available nests differ strongly in quality. This highlights the need to carefully design experiments to detect individual comparison. We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out. However, by using a simple threshold rule, decision-making groups are able to effectively compare options, without relying on any form of direct comparison of alternatives by individuals. This parsimonious mechanism could promote collective rationality in group decision-making.

A simple threshold rule is sufficient to explain sophisticated collective decision-making.

Science.gov (United States)

Robinson, Elva J H; Franks, Nigel R; Ellis, Samuel; Okuda, Saki; Marshall, James A R

2011-01-01

Decision-making animals can use slow-but-accurate strategies, such as making multiple comparisons, or opt for simpler, faster strategies to find a 'good enough' option. Social animals make collective decisions about many group behaviours including foraging and migration. The key to the collective choice lies with individual behaviour. We present a case study of a collective decision-making process (house-hunting ants, Temnothorax albipennis), in which a previously proposed decision strategy involved both quality-dependent hesitancy and direct comparisons of nests by scouts. An alternative possible decision strategy is that scouting ants use a very simple quality-dependent threshold rule to decide whether to recruit nest-mates to a new site or search for alternatives. We use analytical and simulation modelling to demonstrate that this simple rule is sufficient to explain empirical patterns from three studies of collective decision-making in ants, and can account parsimoniously for apparent comparison by individuals and apparent hesitancy (recruitment latency) effects, when available nests differ strongly in quality. This highlights the need to carefully design experiments to detect individual comparison. We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out. However, by using a simple threshold rule, decision-making groups are able to effectively compare options, without relying on any form of direct comparison of alternatives by individuals. This parsimonious mechanism could promote collective rationality in group decision-making.

Decision tables and rule engines in organ allocation systems for optimal transparency and flexibility

NARCIS (Netherlands)

Schaafsma, M.; Deijl, W. van der; Smits, J.M.M.; Rahmel, A.O.; Vries Robbé, P.F. de; Hoitsma, A.J.

2011-01-01

Organ allocation systems have become complex and difficult to comprehend. We introduced decision tables to specify the rules of allocation systems for different organs. A rule engine with decision tables as input was tested for the Kidney Allocation System (ETKAS). We compared this rule engine with

Decision tables and rule engines in organ allocation systems for optimal transparency and flexibility.

Science.gov (United States)

Schaafsma, Murk; van der Deijl, Wilfred; Smits, Jacqueline M; Rahmel, Axel O; de Vries Robbé, Pieter F; Hoitsma, Andries J

2011-05-01

Organ allocation systems have become complex and difficult to comprehend. We introduced decision tables to specify the rules of allocation systems for different organs. A rule engine with decision tables as input was tested for the Kidney Allocation System (ETKAS). We compared this rule engine with the currently used ETKAS by running 11,000 historical match runs and by running the rule engine in parallel with the ETKAS on our allocation system. Decision tables were easy to implement and successful in verifying correctness, completeness, and consistency. The outcomes of the 11,000 historical matches in the rule engine and the ETKAS were exactly the same. Running the rule engine simultaneously in parallel and in real time with the ETKAS also produced no differences. Specifying organ allocation rules in decision tables is already a great step forward in enhancing the clarity of the systems. Yet, using these tables as rule engine input for matches optimizes the flexibility, simplicity and clarity of the whole process, from specification to the performed matches, and in addition this new method allows well controlled simulations. © 2011 The Authors. Transplant International © 2011 European Society for Organ Transplantation.

Investigating decision rules with a new experimental design: the EXACT paradigm

Science.gov (United States)

Biscione, Valerio; Harris, Christopher M.

2015-01-01

In the decision-making field, it is important to distinguish between the perceptual process (how information is collected) and the decision rule (the strategy governing decision-making). We propose a new paradigm, called EXogenous ACcumulation Task (EXACT) to disentangle these two components. The paradigm consists of showing a horizontal gauge that represents the probability of receiving a reward at time t and increases with time. The participant is asked to press a button when they want to request a reward. Thus, the perceptual mechanism is hard-coded and does not need to be inferred from the data. Based on this paradigm, we compared four decision rules (Bayes Risk, Reward Rate, Reward/Accuracy, and Modified Reward Rate) and found that participants appeared to behave according to the Modified Reward Rate. We propose a new way of analysing the data by using the accuracy of responses, which can only be inferred in classic RT tasks. Our analysis suggests that several experimental findings such as RT distribution and its relationship with experimental conditions, usually deemed to be the result of a rise-to-threshold process, may be simply explained by the effect of the decision rule employed. PMID:26578916

Application of decision rules for empowering of Indonesian telematics services SMEs

Science.gov (United States)

Tosida, E. T.; Hairlangga, O.; Amirudin, F.; Ridwanah, M.

2018-03-01

The independence of the field of telematics became one of Indonesia's vision in 2024. One effort to achieve it can be done by empowering SMEs in the field of telematics. Empowerment carried out need a practical mechanism by utilizing data centered, including through the National Economic Census database (Susenas). Based on the Susenas can be formulated the decision rules of determining the provision of assistance for SMEs in the field of telematics. The way it did by generating the rule base through the classification technique. The CART algorithm-based decision rule model performs better than C45 and ID3 models. The high level of performance model is also in line with the regulations applied by the government. This becomes one of the strengths of research, because the resulting model is consistent with the existing conditions in Indonesia. The rules base generated from the three classification techniques show different rules. The CART technique has pattern matching with the realization of activities in The Ministry of Cooperatives and SMEs. So far, the government has difficulty in referring data related to the empowerment of SMEs telematics services. Therefore, the findings resulting from this research can be used as an alternative decision support system related to the program of empowerment of SMEs in telematics.

Choosing the rules: distinct and overlapping frontoparietal representations of task rules for perceptual decisions.

Science.gov (United States)

Zhang, Jiaxiang; Kriegeskorte, Nikolaus; Carlin, Johan D; Rowe, James B

2013-07-17

Behavior is governed by rules that associate stimuli with responses and outcomes. Human and monkey studies have shown that rule-specific information is widely represented in the frontoparietal cortex. However, it is not known how establishing a rule under different contexts affects its neural representation. Here, we use event-related functional MRI (fMRI) and multivoxel pattern classification methods to investigate the human brain's mechanisms of establishing and maintaining rules for multiple perceptual decision tasks. Rules were either chosen by participants or specifically instructed to them, and the fMRI activation patterns representing rule-specific information were compared between these contexts. We show that frontoparietal regions differ in the properties of their rule representations during active maintenance before execution. First, rule-specific information maintained in the dorsolateral and medial frontal cortex depends on the context in which it was established (chosen vs specified). Second, rule representations maintained in the ventrolateral frontal and parietal cortex are independent of the context in which they were established. Furthermore, we found that the rule-specific coding maintained in anticipation of stimuli may change with execution of the rule: representations in context-independent regions remain invariant from maintenance to execution stages, whereas rule representations in context-dependent regions do not generalize to execution stage. The identification of distinct frontoparietal systems with context-independent and context-dependent task rule representations, and the distinction between anticipatory and executive rule representations, provide new insights into the functional architecture of goal-directed behavior.

Ant-based extraction of rules in simple decision systems over ontological graphs

Directory of Open Access Journals (Sweden)

Pancerz Krzysztof

2015-06-01

Full Text Available In the paper, the problem of extraction of complex decision rules in simple decision systems over ontological graphs is considered. The extracted rules are consistent with the dominance principle similar to that applied in the dominancebased rough set approach (DRSA. In our study, we propose to use a heuristic algorithm, utilizing the ant-based clustering approach, searching the semantic spaces of concepts presented by means of ontological graphs. Concepts included in the semantic spaces are values of attributes describing objects in simple decision systems

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.

Termination of resuscitation in the prehospital setting: A comparison of decisions in clinical practice vs. recommendations of a termination rule.

Science.gov (United States)

Verhaert, Dominique V M; Bonnes, Judith L; Nas, Joris; Keuper, Wessel; van Grunsven, Pierre M; Smeets, Joep L R M; de Boer, Menko Jan; Brouwer, Marc A

2016-03-01

Of the proposed algorithms that provide guidance for in-field termination of resuscitation (TOR) decisions, the guidelines for cardiopulmonary resuscitation (CPR) refer to the basic and advanced life support (ALS)-TOR rules. To assess the potential consequences of implementation of the ALS-TOR rule, we performed a case-by-case evaluation of our in-field termination decisions and assessed the corresponding recommendations of the ALS-TOR rule. Cohort of non-traumatic out-of-hospital cardiac arrest (OHCA)-patients who were resuscitated by the ALS-practising emergency medical service (EMS) in the Nijmegen area (2008-2011). The ALS-TOR rule recommends termination in case all following criteria are met: unwitnessed arrest, no bystander CPR, no shock delivery, no return of spontaneous circulation (ROSC). Of the 598 cases reviewed, resuscitative efforts were terminated in the field in 46% and 15% survived to discharge. The ALS-TOR rule would have recommended in-field termination in only 6% of patients, due to high percentages of witnessed arrests (73%) and bystander CPR (54%). In current practice, absence of ROSC was the most important determinant of termination [aOR 35.6 (95% CI 18.3-69.3)]. Weaker associations were found for: unwitnessed and non-public arrests, non-shockable initial rhythms and longer EMS-response times. While designed to optimise hospital transportations, application of the ALS-TOR rule would almost double our hospital transportation rate to over 90% of OHCA-cases due to the favourable arrest circumstances in our region. Prior to implementation of the ALS-TOR rule, local evaluation of the potential consequences for the efficiency of triage is to be recommended and initiatives to improve field-triage for ALS-based EMS-systems are eagerly awaited. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Combining Fuzzy AHP with GIS and Decision Rules for Industrial Site Selection

Directory of Open Access Journals (Sweden)

Aissa Taibi

2017-12-01

Full Text Available This study combines Fuzzy Analytic Hierarchy Process (FAHP, Geographic Information System (GIS and Decision rules to provide decision makers with a ranking model for industrial sites in Algeria. A ranking of the suitable industrial areas is a crucial multi-criteria decision problem based on socio-economical and technical criteria as on environmental considerations. Fuzzy AHP is used for assessment of the candidate industrial sites by combining fuzzy set theory and analytic hierarchy process (AHP. The decision rule base serves as a filter that performs criteria pre-treatment involving a reduction of their numbers. GIS is used to overlay, generate criteria maps and for visualizing ranked zones on the map. The rank of a zone so obtained is an index that guides decision-makers to the best utilization of the zone in future.

Selecting short-statured children needing growth hormone testing: Derivation and validation of a clinical decision rule

Directory of Open Access Journals (Sweden)

Bréart Gérard

2008-07-01

Full Text Available Abstract Background Numerous short-statured children are evaluated for growth hormone (GH deficiency (GHD. In most patients, GH provocative tests are normal and are thus in retrospect unnecessary. Methods A retrospective cohort study was conducted to identify predictors of growth hormone (GH deficiency (GHD in children seen for short stature, and to construct a very sensitive and fairly specific predictive tool to avoid unnecessary GH provocative tests. GHD was defined by the presence of 2 GH concentration peaks Results The initial study included 167 patients, 36 (22% of whom had GHD, including 5 (3% with certain GHD. Independent predictors of GHD were: growth rate Conclusion We have derived and performed an internal validation of a highly sensitive decision rule that could safely help to avoid more than 2/3 of the unnecessary GH tests. External validation of this rule is needed before any application.

Portfolio theory and the alternative decision rule of cost-effectiveness analysis: theoretical and practical considerations.

Science.gov (United States)

Sendi, Pedram; Al, Maiwenn J; Gafni, Amiram; Birch, Stephen

2004-05-01

Bridges and Terris (Soc. Sci. Med. (2004)) critique our paper on the alternative decision rule of economic evaluation in the presence of uncertainty and constrained resources within the context of a portfolio of health care programs (Sendi et al. Soc. Sci. Med. 57 (2003) 2207). They argue that by not adopting a formal portfolio theory approach we overlook the optimal solution. We show that these arguments stem from a fundamental misunderstanding of the alternative decision rule of economic evaluation. In particular, the portfolio theory approach advocated by Bridges and Terris is based on the same theoretical assumptions that the alternative decision rule set out to relax. Moreover, Bridges and Terris acknowledge that the proposed portfolio theory approach may not identify the optimal solution to resource allocation problems. Hence, it provides neither theoretical nor practical improvements to the proposed alternative decision rule.

Dynamic programming approach to optimization of approximate decision rules

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2013-01-01

This paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure R(T) which is the number

Orthogonal search-based rule extraction for modelling the decision to transfuse.

Science.gov (United States)

Etchells, T A; Harrison, M J

2006-04-01

Data from an audit relating to transfusion decisions during intermediate or major surgery were analysed to determine the strengths of certain factors in the decision making process. The analysis, using orthogonal search-based rule extraction (OSRE) from a trained neural network, demonstrated that the risk of tissue hypoxia (ROTH) assessed using a 100-mm visual analogue scale, the haemoglobin value (Hb) and the presence or absence of on-going haemorrhage (OGH) were able to reproduce the transfusion decisions with a joint specificity of 0.96 and sensitivity of 0.93 and a positive predictive value of 0.9. The rules indicating transfusion were: 1. ROTH > 32 mm and Hb 13 mm and Hb 38 mm, Hb < 102 g x l(-1) and OGH; 4. Hb < 78 g x l(-1).

Termination of resuscitation in the prehospital setting: A comparison of decisions in clinical practice vs. recommendations of a termination rule

NARCIS (Netherlands)

Verhaert, D.V.; Bonnes, J.L.; Nas, J.; Keuper, W.; Grunsven, P.M. van; Smeets, J.L.; Boer, M.J. de; Brouwer, M.A.

2016-01-01

BACKGROUND: Of the proposed algorithms that provide guidance for in-field termination of resuscitation (TOR) decisions, the guidelines for cardiopulmonary resuscitation (CPR) refer to the basic and advanced life support (ALS)-TOR rules. To assess the potential consequences of implementation of the

48 CFR 6101.27 - Relief from decision or order [Rule 27].

Science.gov (United States)

2010-10-01

... order [Rule 27]. (a) Grounds. The Board may relieve a party from the operation of a final decision or... discovered, even through due diligence; (2) Justifiable or excusable mistake, inadvertence, surprise, or neglect; (3) Fraud, misrepresentation, or other misconduct of an adverse party; (4) The decision has been...

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.

Comparison of some classification algorithms based on deterministic and nondeterministic decision rules

KAUST Repository

Delimata, Paweł; Marszał-Paszek, Barbara; Moshkov, Mikhail; Paszek, Piotr; Skowron, Andrzej; Suraj, Zbigniew

2010-01-01

the considered algorithms extract from a given decision table efficiently some information about the set of rules. Next, this information is used by a decision-making procedure. The reported results of experiments show that the algorithms based on inhibitory

Feasibility of automatic evaluation of clinical rules in general practice.

NARCIS (Netherlands)

Opondo, D.; Visscher, S.; Eslami, S.; Medlock, S.; Verheij, R.; Korevaar, J.C.; Abu-Hanna, A.

2017-01-01

Purpose: To assess the extent to which clinical rules (CRs) can be implemented for automatic evaluationof quality of care in general practice.Methods: We assessed 81 clinical rules (CRs) adapted from a subset of Assessing Care of Vulnerable Elders(ACOVE) clinical rules, against Dutch College of

Determining rules for closing customer service centers: A public utility company's fuzzy decision

Science.gov (United States)

Dekorvin, Andre; Shipley, Margaret F.; Lea, Robert N.

1992-01-01

In the present work, we consider the general problem of knowledge acquisition under uncertainty. Simply stated, the problem reduces to the following: how can we capture the knowledge of an expert when the expert is unable to clearly formulate how he or she arrives at a decision? A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision may have been made. Unique to our work is the fuzzy set representation of the conditions or attributes upon which the expert may possibly base his fuzzy decision. From our examples, we infer certain and possible fuzzy rules for closing a customer service center and illustrate the importance of having the decision closely relate to the conditions under consideration.

Using data mining techniques to explore physicians' therapeutic decisions when clinical guidelines do not provide recommendations: methods and example for type 2 diabetes.

Science.gov (United States)

Toussi, Massoud; Lamy, Jean-Baptiste; Le Toumelin, Philippe; Venot, Alain

2009-06-10

Clinical guidelines carry medical evidence to the point of practice. As evidence is not always available, many guidelines do not provide recommendations for all clinical situations encountered in practice. We propose an approach for identifying knowledge gaps in guidelines and for exploring physicians' therapeutic decisions with data mining techniques to fill these knowledge gaps. We demonstrate our method by an example in the domain of type 2 diabetes. We analyzed the French national guidelines for the management of type 2 diabetes to identify clinical conditions that are not covered or those for which the guidelines do not provide recommendations. We extracted patient records corresponding to each clinical condition from a database of type 2 diabetic patients treated at Avicenne University Hospital of Bobigny, France. We explored physicians' prescriptions for each of these profiles using C5.0 decision-tree learning algorithm. We developed decision-trees for different levels of detail of the therapeutic decision, namely the type of treatment, the pharmaco-therapeutic class, the international non proprietary name, and the dose of each medication. We compared the rules generated with those added to the guidelines in a newer version, to examine their similarity. We extracted 27 rules from the analysis of a database of 463 patient records. Eleven rules were about the choice of the type of treatment and thirteen rules about the choice of the pharmaco-therapeutic class of each drug. For the choice of the international non proprietary name and the dose, we could extract only a few rules because the number of patient records was too low for these factors. The extracted rules showed similarities with those added to the newer version of the guidelines. Our method showed its usefulness for completing guidelines recommendations with rules learnt automatically from physicians' prescriptions. It could be used during the development of guidelines as a complementary source from

Using data mining techniques to explore physicians' therapeutic decisions when clinical guidelines do not provide recommendations: methods and example for type 2 diabetes

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Toussi Massoud

2009-06-01

Full Text Available Abstract Background Clinical guidelines carry medical evidence to the point of practice. As evidence is not always available, many guidelines do not provide recommendations for all clinical situations encountered in practice. We propose an approach for identifying knowledge gaps in guidelines and for exploring physicians' therapeutic decisions with data mining techniques to fill these knowledge gaps. We demonstrate our method by an example in the domain of type 2 diabetes. Methods We analyzed the French national guidelines for the management of type 2 diabetes to identify clinical conditions that are not covered or those for which the guidelines do not provide recommendations. We extracted patient records corresponding to each clinical condition from a database of type 2 diabetic patients treated at Avicenne University Hospital of Bobigny, France. We explored physicians' prescriptions for each of these profiles using C5.0 decision-tree learning algorithm. We developed decision-trees for different levels of detail of the therapeutic decision, namely the type of treatment, the pharmaco-therapeutic class, the international non proprietary name, and the dose of each medication. We compared the rules generated with those added to the guidelines in a newer version, to examine their similarity. Results We extracted 27 rules from the analysis of a database of 463 patient records. Eleven rules were about the choice of the type of treatment and thirteen rules about the choice of the pharmaco-therapeutic class of each drug. For the choice of the international non proprietary name and the dose, we could extract only a few rules because the number of patient records was too low for these factors. The extracted rules showed similarities with those added to the newer version of the guidelines. Conclusion Our method showed its usefulness for completing guidelines recommendations with rules learnt automatically from physicians' prescriptions. It could be used

Optimization and analysis of decision trees and rules: Dynamic programming approach

KAUST Repository

Alkhalid, Abdulaziz

2013-08-01

This paper is devoted to the consideration of software system Dagger created in KAUST. This system is based on extensions of dynamic programming. It allows sequential optimization of decision trees and rules relative to different cost functions, derivation of relationships between two cost functions (in particular, between number of misclassifications and depth of decision trees), and between cost and uncertainty of decision trees. We describe features of Dagger and consider examples of this systems work on decision tables from UCI Machine Learning Repository. We also use Dagger to compare 16 different greedy algorithms for decision tree construction. © 2013 Taylor and Francis Group, LLC.

Optimization and analysis of decision trees and rules: Dynamic programming approach

KAUST Repository

Alkhalid, Abdulaziz; Amin, Talha M.; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail; Zielosko, Beata

2013-01-01

This paper is devoted to the consideration of software system Dagger created in KAUST. This system is based on extensions of dynamic programming. It allows sequential optimization of decision trees and rules relative to different cost functions, derivation of relationships between two cost functions (in particular, between number of misclassifications and depth of decision trees), and between cost and uncertainty of decision trees. We describe features of Dagger and consider examples of this systems work on decision tables from UCI Machine Learning Repository. We also use Dagger to compare 16 different greedy algorithms for decision tree construction. © 2013 Taylor and Francis Group, LLC.

A Clinical Decision Support System for Breast Cancer Patients

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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.

[Usefulness of clinical prediction rules for ruling out deep vein thrombosis in a hospital emergency department].

Science.gov (United States)

Rosa-Jiménez, Francisco; Rosa-Jiménez, Ascensión; Lozano-Rodríguez, Aquiles; Santoro-Martínez, María Del Carmen; Duro-López, María Del Carmen; Carreras-Álvarez de Cienfuegos, Amelia

2015-01-01

To compare the efficacy of the most familiar clinical prediction rules in combination with D-dimer testing to rule out a diagnosis of deep vein thrombosis (DVT) in a hospital emergency department. Retrospective cross-sectional analysis of the case records of all patients attending a hospital emergency department with suspected lower-limb DVT between 1998 and 2002. Ten clinical prediction scores were calculated and D-dimer levels were available for all patients. The gold standard was ultrasound diagnosis of DVT by an independent radiologist who was blinded to clinical records. For each prediction rule, we analyzed the effectiveness of the prediction strategy defined by "low clinical probability and negative D-dimer level" against the ultrasound diagnosis. A total of 861 case records were reviewed and 577 cases were selected; the mean (SD) age was 66.7 (14.2) years. DVT was diagnosed in 145 patients (25.1%). Only the Wells clinical prediction rule and 4 other models had a false negative rate under 2%. The Wells criteria and the score published by Johanning and colleagues identified higher percentages of cases (15.6% and 11.6%, respectively). This study shows that several clinical prediction rules can be safely used in the emergency department, although none of them have proven more effective than the Wells criteria.

Excusing exclusion: Accounting for rule-breaking and sanctions in a Swedish methadone clinic.

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Petersson, Frida J M

2013-11-01

Methadone maintenance treatment has been subjected to much debate and controversy in Sweden during the last decades. Thresholds for getting access are high and control policies strict within the programmes. This article analyses how professionals working in a Swedish methadone clinic discuss and decide on appropriate responses to clients' rule-breaking behaviour. The research data consist of field notes from observations of three interprofessional team meetings where different clients' illicit drug use is discussed. A micro-sociological approach and accounts analysis was applied to the data. During their decision-oriented talk at the meetings, the professionals account for: (1) sanctions, (2) nonsanction, (3) mildness. In accounting for (2) and (3), they also account for clients' rule-breaking behaviour. Analysis shows how these ways of accounting are concerned with locating blame and responsibility for the act in question, as well as with constructing excuses and justifications for the clients and for the professionals themselves. In general, these results demonstrate that maintenance treatment in everyday professional decision-making, far from being a neutral evidence-based practice, involves a substantial amount of professional discretion and moral judgements. Sanctions are chosen according to the way in which a deviance from the rules is explained and, in doing so, a certain behaviour is deemed to be serious, dangerous and unacceptable - or excusable. Copyright © 2013 Elsevier B.V. All rights reserved.

Optimization of approximate decision rules relative to number of misclassifications: Comparison of greedy and dynamic programming approaches

KAUST Repository

Amin, Talha

2013-01-01

In the paper, we present a comparison of dynamic programming and greedy approaches for construction and optimization of approximate decision rules relative to the number of misclassifications. We use an uncertainty measure that is a difference between the number of rows in a decision table T and the number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules that localize rows in subtables of T with uncertainty at most γ. Experimental results with decision tables from the UCI Machine Learning Repository are also presented. © 2013 Springer-Verlag.

Stress influences decisions to break a safety rule in a complex simulation task in females.

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Starcke, Katrin; Brand, Matthias; Kluge, Annette

2016-07-01

The current study examines the effects of acutely induced laboratory stress on a complex decision-making task, the Waste Water Treatment Simulation. Participants are instructed to follow a certain decision rule according to safety guidelines. Violations of this rule are associated with potential high rewards (working faster and earning more money) but also with the risk of a catastrophe (an explosion). Stress was induced with the Trier Social Stress Test while control participants underwent a non-stress condition. In the simulation task, stressed females broke the safety rule more often than unstressed females: χ(2) (1, N=24)=10.36, pbreak the safety rule because stressed female participants focused on the potential high gains while they neglected the risk of potential negative consequences. Copyright © 2016 Elsevier B.V. All rights reserved.

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

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Atif Shahzad

2016-02-01

Full Text Available A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in changing shop conditions. Meta-heuristics are able to perform quite well and carry more knowledge of the problem domain, however at the cost of prohibitive computational effort in real-time. The primary purpose of this research lies in an offline extraction of this domain knowledge using decision trees to generate simple if-then rules that subsequently act as dispatching rules for scheduling in an online manner. We used similarity index to identify parametric and structural similarity in problem instances in order to implicitly support the learning algorithm for effective rule generation and quality index for relative ranking of the dispatching decisions. Maximum lateness is used as the scheduling objective in a job shop scheduling environment.

Understanding Decision-Making, Communication Rules, and Communication Satisfaction as Culture: Implications for Organizational Effectiveness.

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Shockley-Zalabak, Pamela

A study of decision making processes and communication rules, in a corporate setting undergoing change as a result of organizational ineffectiveness, examined whether (1) decisions about formal communication reporting systems were linked to management assumptions about technical creativity/effectiveness, (2) assumptions about…

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.

How well do clinical prediction rules perform in identifying serious infections in acutely ill children across an international network of ambulatory care datasets?

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Verbakel Jan Y

2013-01-01

Full Text Available Abstract Background Diagnosing serious infections in children is challenging, because of the low incidence of such infections and their non-specific presentation early in the course of illness. Prediction rules are promoted as a means to improve recognition of serious infections. A recent systematic review identified seven clinical prediction rules, of which only one had been prospectively validated, calling into question their appropriateness for clinical practice. We aimed to examine the diagnostic accuracy of these rules in multiple ambulatory care populations in Europe. Methods Four clinical prediction rules and two national guidelines, based on signs and symptoms, were validated retrospectively in seven individual patient datasets from primary care and emergency departments, comprising 11,023 children from the UK, the Netherlands, and Belgium. The accuracy of each rule was tested, with pre-test and post-test probabilities displayed using dumbbell plots, with serious infection settings stratified as low prevalence (LP; 20% . In LP and IP settings, sensitivity should be >90% for effective ruling out infection. Results In LP settings, a five-stage decision tree and a pneumonia rule had sensitivities of >90% (at a negative likelihood ratio (NLR of Conclusions None of the clinical prediction rules examined in this study provided perfect diagnostic accuracy. In LP or IP settings, prediction rules and evidence-based guidelines had high sensitivity, providing promising rule-out value for serious infections in these datasets, although all had a percentage of residual uncertainty. Additional clinical assessment or testing such as point-of-care laboratory tests may be needed to increase clinical certainty. None of the prediction rules identified seemed to be valuable for HP settings such as emergency departments.

MICE or NICE? An economic evaluation of clinical decision rules in the diagnosis of heart failure in primary care.

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Monahan, Mark; Barton, Pelham; Taylor, Clare J; Roalfe, Andrea K; Hobbs, F D Richard; Cowie, Martin; Davis, Russell; Deeks, Jon; Mant, Jonathan; McCahon, Deborah; McDonagh, Theresa; Sutton, George; Tait, Lynda

2017-08-15

Detection and treatment of heart failure (HF) can improve quality of life and reduce premature mortality. However, symptoms such as breathlessness are common in primary care, have a variety of causes and not all patients require cardiac imaging. In systems where healthcare resources are limited, ensuring those patients who are likely to have HF undergo appropriate and timely investigation is vital. A decision tree was developed to assess the cost-effectiveness of using the MICE (Male, Infarction, Crepitations, Edema) decision rule compared to other diagnostic strategies to identify HF patients presenting to primary care. Data from REFER (REFer for EchocaRdiogram), a HF diagnostic accuracy study, was used to determine which patients received the correct diagnosis decision. The model adopted a UK National Health Service (NHS) perspective. The current recommended National Institute for Health and Care Excellence (NICE) guidelines for identifying patients with HF was the most cost-effective option with a cost of £4400 per quality adjusted life year (QALY) gained compared to a "do nothing" strategy. That is, patients presenting with symptoms suggestive of HF should be referred straight for echocardiography if they had a history of myocardial infarction or if their NT-proBNP level was ≥400pg/ml. The MICE rule was more expensive and less effective than the other comparators. Base-case results were robust to sensitivity analyses. This represents the first cost-utility analysis comparing HF diagnostic strategies for symptomatic patients. Current guidelines in England were the most cost-effective option for identifying patients for confirmatory HF diagnosis. The low number of HF with Reduced Ejection Fraction patients (12%) in the REFER patient population limited the benefits of early detection. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

How accurate are interpretations of curriculum-based measurement progress monitoring data? Visual analysis versus decision rules.

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Van Norman, Ethan R; Christ, Theodore J

2016-10-01

Curriculum based measurement of oral reading (CBM-R) is used to monitor the effects of academic interventions for individual students. Decisions to continue, modify, or terminate these interventions are made by interpreting time series CBM-R data. Such interpretation is founded upon visual analysis or the application of decision rules. The purpose of this study was to compare the accuracy of visual analysis and decision rules. Visual analysts interpreted 108 CBM-R progress monitoring graphs one of three ways: (a) without graphic aids, (b) with a goal line, or (c) with a goal line and a trend line. Graphs differed along three dimensions, including trend magnitude, variability of observations, and duration of data collection. Automated trend line and data point decision rules were also applied to each graph. Inferential analyses permitted the estimation of the probability of a correct decision (i.e., the student is improving - continue the intervention, or the student is not improving - discontinue the intervention) for each evaluation method as a function of trend magnitude, variability of observations, and duration of data collection. All evaluation methods performed better when students made adequate progress. Visual analysis and decision rules performed similarly when observations were less variable. Results suggest that educators should collect data for more than six weeks, take steps to control measurement error, and visually analyze graphs when data are variable. Implications for practice and research are discussed. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Testing decision rules for categorizing species' extinction risk to help develop quantitative listing criteria for the U.S. Endangered Species Act.

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Regan, Tracey J; Taylor, Barbara L; Thompson, Grant G; Cochrane, Jean Fitts; Ralls, Katherine; Runge, Michael C; Merrick, Richard

2013-08-01

Lack of guidance for interpreting the definitions of endangered and threatened in the U.S. Endangered Species Act (ESA) has resulted in case-by-case decision making leaving the process vulnerable to being considered arbitrary or capricious. Adopting quantitative decision rules would remedy this but requires the agency to specify the relative urgency concerning extinction events over time, cutoff risk values corresponding to different levels of protection, and the importance given to different types of listing errors. We tested the performance of 3 sets of decision rules that use alternative functions for weighting the relative urgency of future extinction events: a threshold rule set, which uses a decision rule of x% probability of extinction over y years; a concave rule set, where the relative importance of future extinction events declines exponentially over time; and a shoulder rule set that uses a sigmoid shape function, where relative importance declines slowly at first and then more rapidly. We obtained decision cutoffs by interviewing several biologists and then emulated the listing process with simulations that covered a range of extinction risks typical of ESA listing decisions. We evaluated performance of the decision rules under different data quantities and qualities on the basis of the relative importance of misclassification errors. Although there was little difference between the performance of alternative decision rules for correct listings, the distribution of misclassifications differed depending on the function used. Misclassifications for the threshold and concave listing criteria resulted in more overprotection errors, particularly as uncertainty increased, whereas errors for the shoulder listing criteria were more symmetrical. We developed and tested the framework for quantitative decision rules for listing species under the U.S. ESA. If policy values can be agreed on, use of this framework would improve the implementation of the ESA by

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.

How to make the best decision. Philosophical aspects of clinical decision theory.

Science.gov (United States)

Wulff, H R

1981-01-01

An attempt is made to discuss some of the philosophical implications of the use of decision-analytic techniques. The probabilities of a decision analysis are subjective measures of belief, and it is concluded that clinicians base their subjective beliefs on both recorded observations and theoretical knowledge. The clinical decision maker also evaluates the consequences of his actions, and therefore clinical decision theory transcends medical science. A number of different schools of normative ethics are mentioned to illustrate the complexity of everyday decision making. The philosophical terminology is useful for the analysis of clinical problems, and it is argued that clinical decision making has both a teleological and a deontological component. The results of decision-analytic studies depend on such factors as the wealth of the country, the organization of the health service, and cultural norms.

Incorporation of systematic uncertainties in statistical decision rules

International Nuclear Information System (INIS)

Wichers, V.A.

1994-02-01

The influence of systematic uncertainties on statistical hypothesis testing is an underexposed subject. Systematic uncertainties cannot be incorporated in hypothesis tests, but they deteriorate the performance of these tests. A wrong treatment of systematic uncertainties in verification applications in safeguards leads to false assessment of the strength of the safeguards measure, and thus undermines the safeguards system. The effects of systematic uncertainties on decision errors in hypothesis testing are analyzed quantitatively for an example from the safeguards practice. (LEU-HEU verification of UF 6 enrichment in centrifuge enrichment plants). It is found that the only proper way to tackle systematic uncertainties is reduction to sufficiently low levels; criteria for these are proposed. Although conclusions were obtained from study of a single practical application, it is believed that they hold generally: for all sources of systematic uncertainties, all statistical decision rules, and all applications. (orig./HP)

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.

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

Injury and death in clinical trials and compensation: Rule 122 DAB

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Ravindra B Ghooi

2013-01-01

Full Text Available Three amendments to the drugs and cosmetics rules were published in quick succession in 2013. These addressed the issues of compensation of injury and death in clinical trials in addition to the role and registration of Ethics Committees. Of the three, the first and the third make an impact on the clinical research activities in India. The second amendment has codified the conduct of clinical trials, putting together rules, which appeared in different sections of Schedule Y. The first amendment deals with the compensation for injuries and deaths taking place during clinical trials while the third deals with registration of Ethics Committees. Despite the long delay in the issue of compensation rules, there appears significant room for improvement. The most problematic are conditions of injury and death in which compensation has to be paid. When compared with other countries, the Indian rules seem unduly harsh on sponsors and are at significant variance with those in UK. The rules are very generous toward subjects and compensation is likely to become an alternative to insurance in terminally ill subjects. The implementation of these rules will make clinical trials in India more expensive and hurt the industry that is already struggling through other handicaps. There is an urgent need to make the the environment more industry friendly to attract more clinical work.

The Manchester Acute Coronary Syndromes (MACS) decision rule: validation with a new automated assay for heart-type fatty acid binding protein.

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Body, Richard; Burrows, Gillian; Carley, Simon; Lewis, Philip S

2015-10-01

The Manchester Acute Coronary Syndromes (MACS) decision rule may enable acute coronary syndromes to be immediately 'ruled in' or 'ruled out' in the emergency department. The rule incorporates heart-type fatty acid binding protein (h-FABP) and high sensitivity troponin T levels. The rule was previously validated using a semiautomated h-FABP assay that was not practical for clinical implementation. We aimed to validate the rule with an automated h-FABP assay that could be used clinically. In this prospective diagnostic cohort study we included patients presenting to the emergency department with suspected cardiac chest pain. Serum drawn on arrival was tested for h-FABP using an automated immunoturbidimetric assay (Randox) and high sensitivity troponin T (Roche). The primary outcome, a diagnosis of acute myocardial infarction (AMI), was adjudicated based on 12 h troponin testing. A secondary outcome, major adverse cardiac events (MACE; death, AMI, revascularisation or new coronary stenosis), was determined at 30 days. Of the 456 patients included, 78 (17.1%) had AMI and 97 (21.3%) developed MACE. Using the automated h-FABP assay, the MACS rule had the same C-statistic for MACE as the original rule (0.91; 95% CI 0.88 to 0.92). 18.9% of patients were identified as 'very low risk' and thus eligible for immediate discharge with no missed AMIs and a 2.3% incidence of MACE (n=2, both coronary stenoses). 11.1% of patients were classed as 'high-risk' and had a 92.0% incidence of MACE. Our findings validate the performance of a refined MACS rule incorporating an automated h-FABP assay, facilitating use in clinical settings. The effectiveness of this refined rule should be verified in an interventional trial prior to implementation. UK CRN 8376. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

[Validation of a clinical prediction rule to distinguish bacterial from aseptic meningitis].

Science.gov (United States)

Agüero, Gonzalo; Davenport, María C; Del Valle, María de la P; Gallegos, Paulina; Kannemann, Ana L; Bokser, Vivian; Ferrero, Fernando

2010-02-01

Despite most meningitis are not bacterial, antibiotics are usually administered on admission because bacterial meningitis is difficult to be rule-out. Distinguishing bacterial from aseptic meningitis on admission could avoid inappropriate antibiotic use and hospitalization. We aimed to validate a clinical prediction rule to distinguish bacterial from aseptic meningitis in children, on arriving to the emergency room. This prospective study included patients aged or = 1000 cells/mm(3), CSF protein > or = 80 mg/dl, peripheral blood absolute neutrophil count > or = 10.000/mm(3), seizure = 1 point each. Sensitivity (S), specificity (E), positive and negative predictive values (PPV and NPV), positive and negative likelihood ratios (PLR and NLR) of the BMS to predict bacterial meningitis were calculated. Seventy patients with meningitis were included (14 bacterial meningitis). When BMS was calculated, 25 patients showed a BMS= 0 points, 11 BMS= 1 point, and 34 BMS > or = 2 points. A BMS = 0 showed S: 100%, E: 44%, VPP: 31%, VPN: 100%, RVP: 1,81 RVN: 0. A BMS > or = 2 predicted bacterial meningitis with S: 100%, E: 64%, VPP: 41%, VPN: 100%, PLR: 2.8, NLR:0. Using BMS was simple, and allowed identifying children with very low risk of bacterial meningitis. It could be a useful tool to assist clinical decision making.

Decision or norm: Judicial discretion as a treat to the rule of law

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Avramović Dragutin

2012-01-01

Full Text Available Principle of legality and legal certainty, as key notions even of the thinnest concept of rule of law, are largely endangered in our times by widening of judicial discretion range. That trend is more and more at hand in European states as well, due to convergence of common law and civil law legal systems. Judicial decision acquires higher and higher factual importance in European legal systems, although it is generally not considered as a source of law. After analysis of standings by leading scholars of legal realism theory, the author admits that a very high level of tension frequently exists between judicial decision and legal norm. Within that conflict often and relatively easy decision succeeds to tear off by the strict letter of the law. In application of general legal rules upon concrete case, by creative adjustment of the law to life, due to necessary general and abstract character of legal norms, judge becomes more creator of law, rather than the one who applies it. The author points to danger of subjective and prejudiced attitudes of the judges, as they, due to their wide discretion, make a decision more upon their own feeling of justice, rather than upon law itself. In that way the law transforms itself in judicial decision based upon subjective understanding of justice and fairness.

The res judicata rule in jurisdictional decisions of the international Court of justice

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Kreća Milenko

2014-01-01

Full Text Available The author discusses the effects of the res judicata rule as regards jurisdictional decisions of the International Court of Justice. He finds that there exists a special position of a judgment on preliminary objection in respect to both aspects of the res judicata rule - its binding force and finality. A perception of distinct relativity of a jurisdictional decision of the Court, expressing its interlocatory character pervades, in his opinion, the body of law regulating the Court's activity. Preliminary objections as such do not exhaust objections to the jurisdiction of the Court, as evidenced by non-preliminary objections to the jurisdiction of the Court giving rise to the application of the principle compétence de la compétence understood in the narrow sense. With regard to the binding force of a judgment on preliminary objections, it does not create legal obligations stricto sensu. The author finds that the relative character of jurisdictional decisions of the Court as compared with a judgment on the merits is justified on a number of grounds.

When none of us perform better than all of us together: the role of analogical decision rules in groups.

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Nicoleta Meslec

Full Text Available During social interactions, groups develop collective competencies that (ideally should assist groups to outperform average standalone individual members (weak cognitive synergy or the best performing member in the group (strong cognitive synergy. In two experimental studies we manipulate the type of decision rule used in group decision-making (identify the best vs. collaborative, and the way in which the decision rules are induced (direct vs. analogical and we test the effect of these two manipulations on the emergence of strong and weak cognitive synergy. Our most important results indicate that an analogically induced decision rule (imitate-the-successful heuristic in which groups have to identify the best member and build on his/her performance (take-the-best heuristic is the most conducive for strong cognitive synergy. Our studies bring evidence for the role of analogy-making in groups as well as the role of fast-and-frugal heuristics for group decision-making.

A Simple Decision Rule for Recognition of Poly(A) Tail Signal Motifs in Human Genome

KAUST Repository

AbouEisha, Hassan M.

2015-05-12

Background is the numerous attempts were made to predict motifs in genomic sequences that correspond to poly (A) tail signals. Vast portion of this effort has been directed to a plethora of nonlinear classification methods. Even when such approaches yield good discriminant results, identifying dominant features of regulatory mechanisms nevertheless remains a challenge. In this work, we look at decision rules that may help identifying such features. Findings are we present a simple decision rule for classification of candidate poly (A) tail signal motifs in human genomic sequence obtained by evaluating features during the construction of gradient boosted trees. We found that values of a single feature based on the frequency of adenine in the genomic sequence surrounding candidate signal and the number of consecutive adenine molecules in a well-defined region immediately following the motif displays good discriminative potential in classification of poly (A) tail motifs for samples covered by the rule. Conclusions is the resulting simple rule can be used as an efficient filter in construction of more complex poly(A) tail motifs classification algorithms.

Optimal Decision Rules in Repeated Games Where Players Infer an Opponent’s Mind via Simplified Belief Calculation

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Mitsuhiro Nakamura

2016-07-01

Full Text Available In strategic situations, humans infer the state of mind of others, e.g., emotions or intentions, adapting their behavior appropriately. Nonetheless, evolutionary studies of cooperation typically focus only on reaction norms, e.g., tit for tat, whereby individuals make their next decisions by only considering the observed outcome rather than focusing on their opponent’s state of mind. In this paper, we analyze repeated two-player games in which players explicitly infer their opponent’s unobservable state of mind. Using Markov decision processes, we investigate optimal decision rules and their performance in cooperation. The state-of-mind inference requires Bayesian belief calculations, which is computationally intensive. We therefore study two models in which players simplify these belief calculations. In Model 1, players adopt a heuristic to approximately infer their opponent’s state of mind, whereas in Model 2, players use information regarding their opponent’s previous state of mind, obtained from external evidence, e.g., emotional signals. We show that players in both models reach almost optimal behavior through commitment-like decision rules by which players are committed to selecting the same action regardless of their opponent’s behavior. These commitment-like decision rules can enhance or reduce cooperation depending on the opponent’s strategy.

Benefiting from deep-level diversity : How congruence between knowledge and decision rules improves team decision making and team perceptions

NARCIS (Netherlands)

Rink, Floor; Ellemers, Naomi

In two experiments we show how teams can benefit from the presence of multiple sources of deep-level task-related diversity. We manipulated differences (vs. similarities) in task information and personal decision rules in dyads (Study 1) and three-person teams (Study 2). The results indicate that

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

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...

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.

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…

Simulation of operating rules and discretional decisions using a fuzzy rule-based system integrated into a water resources management model

Science.gov (United States)

Macian-Sorribes, Hector; Pulido-Velazquez, Manuel

2013-04-01

Water resources systems are operated, mostly, using a set of pre-defined rules not regarding, usually, to an optimal allocation in terms of water use or economic benefits, but to historical and institutional reasons. These operating policies are reproduced, commonly, as hedging rules, pack rules or zone-based operations, and simulation models can be used to test their performance under a wide range of hydrological and/or socio-economic hypothesis. Despite the high degree of acceptation and testing that these models have achieved, the actual operation of water resources systems hardly follows all the time the pre-defined rules with the consequent uncertainty on the system performance. Real-world reservoir operation is very complex, affected by input uncertainty (imprecision in forecast inflow, seepage and evaporation losses, etc.), filtered by the reservoir operator's experience and natural risk-aversion, while considering the different physical and legal/institutional constraints in order to meet the different demands and system requirements. The aim of this work is to expose a fuzzy logic approach to derive and assess the historical operation of a system. This framework uses a fuzzy rule-based system to reproduce pre-defined rules and also to match as close as possible the actual decisions made by managers. After built up, the fuzzy rule-based system can be integrated in a water resources management model, making possible to assess the system performance at the basin scale. The case study of the Mijares basin (eastern Spain) is used to illustrate the method. A reservoir operating curve regulates the two main reservoir releases (operated in a conjunctive way) with the purpose of guaranteeing a high realiability of supply to the traditional irrigation districts with higher priority (more senior demands that funded the reservoir construction). A fuzzy rule-based system has been created to reproduce the operating curve's performance, defining the system state (total

A sharable cloud-based pancreaticoduodenectomy collaborative database for physicians: emphasis on security and clinical rule supporting.

Science.gov (United States)

Yu, Hwan-Jeu; Lai, Hong-Shiee; Chen, Kuo-Hsin; Chou, Hsien-Cheng; Wu, Jin-Ming; Dorjgochoo, Sarangerel; Mendjargal, Adilsaikhan; Altangerel, Erdenebaatar; Tien, Yu-Wen; Hsueh, Chih-Wen; Lai, Feipei

2013-08-01

Pancreaticoduodenectomy (PD) is a major operation with high complication rate. Thereafter, patients may develop morbidity because of the complex reconstruction and loss of pancreatic parenchyma. A well-designed database is very important to address both the short-term and long-term outcomes after PD. The objective of this research was to build an international PD database implemented with security and clinical rule supporting functions, which made the data-sharing easier and improve the accuracy of data. The proposed system is a cloud-based application. To fulfill its requirements, the system comprises four subsystems: a data management subsystem, a clinical rule supporting subsystem, a short message notification subsystem, and an information security subsystem. After completing the surgery, the physicians input the data retrospectively, which are analyzed to study factors associated with post-PD common complications (delayed gastric emptying and pancreatic fistula) to validate the clinical value of this system. Currently, this database contains data from nearly 500 subjects. Five medical centers in Taiwan and two cancer centers in Mongolia are participating in this study. A data mining model of the decision tree analysis showed that elderly patients (>76 years) with pylorus-preserving PD (PPPD) have higher proportion of delayed gastric emptying. About the pancreatic fistula, the data mining model of the decision tree analysis revealed that cases with non-pancreaticogastrostomy (PG) reconstruction - body mass index (BMI)>29.65 or PG reconstruction - BMI>23.7 - non-classic PD have higher proportion of pancreatic fistula after PD. The proposed system allows medical staff to collect and store clinical data in a cloud, sharing the data with other physicians in a secure manner to achieve collaboration in research. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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.

Implications of Derived Rule Following of Roulette Gambling for Clinical Practice.

Science.gov (United States)

Wilson, Alyssa N; Grant, Tara

2015-05-01

Problem gambling is a global concern, and behavior analytic attention has increasingly focused on reasons for why problem gambling occurs and conditions under which it is maintained. However, limited knowledge currently exists on the process to which self-generated rules maintain gambling behaviors. Therefore, the current study assessed six recreational gamblers on a roulette game before and after discrimination training to establish a self-rule to wager on red or black. Following discrimination training, all six participants altered their response allocation among red or black and consistently responded according to the newly derived self-rule. Results maintained during 1-week follow-up sessions across all participants. Implications for clinical application of self-awareness and self-generated rule following are discussed. Implications for practice • Demonstration of how stimuli such as color can alter gambling behavior • Procedures to assist clients with changing self-rules about gambling behavior • Using self-generated rule formulation for more contextually appropriate target behaviors • Highlights how self-generated rules can be altered to change clinical target behaviors.

A Belief Rule-Based (BRB) Decision Support System for Assessing Clinical Asthma Suspicion

DEFF Research Database (Denmark)

Hossain, Mohammad Shahadat; Hossain, Emran; Khalid, Md. Saifuddin

2014-01-01

conditions of uncertainty. The Belief Rule-Based Inference Methodology Using the Evidential Reasoning (RIMER) approach was adopted to develop this expert system; which is named the Belief Rule-Based Expert System (BRBES). The system can handle various types of uncertainty in knowledge representation...... and inference procedures. The knowledge base of this system was constructed by using real patient data and expert opinion. Practical case studies were used to validate the system. The system-generated results are more effective and reliable in terms of accuracy than the results generated by a manual system....

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

Transparency in Economic and Political Decision-Making: The Identification of Sunshine Rules for Transparent Lobbying

Directory of Open Access Journals (Sweden)

Laboutková Šárka

2017-09-01

Full Text Available Lobbying transparency seems to have been a challenging topic for nearly a decade. For the purposes of the article, the authors focus on a contextual analysis of rules and measures that offers both a broad as well as comprehensive view of the required transparency of lobbying activities and the environment in which decisions are made. In this regard, focusing on the sunshine principles/sunshine rules (not purely limited to laws provides a grasp of the whole issue in a broader context. From a methodological point of view, the exploratory approach was chosen and the coding procedure is mostly dichotomous. As a result, seven key areas with 70 indicators have been identified in terms of transparency of lobbying and decision-making.

Comparison of rule induction, decision trees and formal concept analysis approaches for classification

Science.gov (United States)

Kotelnikov, E. V.; Milov, V. R.

2018-05-01

Rule-based learning algorithms have higher transparency and easiness to interpret in comparison with neural networks and deep learning algorithms. These properties make it possible to effectively use such algorithms to solve descriptive tasks of data mining. The choice of an algorithm depends also on its ability to solve predictive tasks. The article compares the quality of the solution of the problems with binary and multiclass classification based on the experiments with six datasets from the UCI Machine Learning Repository. The authors investigate three algorithms: Ripper (rule induction), C4.5 (decision trees), In-Close (formal concept analysis). The results of the experiments show that In-Close demonstrates the best quality of classification in comparison with Ripper and C4.5, however the latter two generate more compact rule sets.

A clinical prediction rule for histological chorioamnionitis in preterm newborns.

Directory of Open Access Journals (Sweden)

Jasper V Been

Full Text Available BACKGROUND: Histological chorioamnionitis (HC is an intrauterine inflammatory process highly associated with preterm birth and adverse neonatal outcome. HC is often clinically silent and diagnosed postnatally by placental histology. Earlier identification could facilitate treatment individualisation to improve outcome in preterm newborns. AIM: Develop a clinical prediction rule at birth for HC and HC with fetal involvement (HCF in preterm newborns. METHODS: Clinical data and placental pathology were obtained from singleton preterm newborns (gestational age ≤ 32.0 weeks born at Erasmus UMC Rotterdam from 2001 to 2003 (derivation cohort; n = 216 or Máxima MC Veldhoven from 2009 to 2010 (validation cohort; n = 206. HC and HCF prediction rules were developed with preference for high sensitivity using clinical variables available at birth. RESULTS: HC and HCF were present in 39% and 24% in the derivation cohort and in 44% and 22% in the validation cohort, respectively. HC was predicted with 87% accuracy, yielding an area under ROC curve of 0.95 (95%CI = 0.92-0.98, a positive predictive value of 80% (95%CI = 74-84%, and a negative predictive value of 93% (95%CI = 88-96%. Corresponding figures for HCF were: accuracy 83%, area under ROC curve 0.92 (95%CI = 0.88-0.96, positive predictive value 59% (95%CI = 52-62%, and negative predictive value 97% (95%CI = 93-99%. External validation expectedly resulted in some loss of test performance, preferentially affecting positive predictive rather than negative predictive values. CONCLUSION: Using a clinical prediction rule composed of clinical variables available at birth, HC and HCF could be predicted with good test characteristics in preterm newborns. Further studies should evaluate the clinical value of these rules to guide early treatment individualisation.

Clinical decision-making: physicians' preferences and experiences

Directory of Open Access Journals (Sweden)

White Martha

2007-03-01

Full Text Available Abstract Background Shared decision-making has been advocated; however there are relatively few studies on physician preferences for, and experiences of, different styles of clinical decision-making as most research has focused on patient preferences and experiences. The objectives of this study were to determine 1 physician preferences for different styles of clinical decision-making; 2 styles of clinical decision-making physicians perceive themselves as practicing; and 3 the congruence between preferred and perceived style. In addition we sought to determine physician perceptions of the availability of time in visits, and their role in encouraging patients to look for health information. Methods Cross-sectional survey of a nationally representative sample of U.S. physicians. Results 1,050 (53% response rate physicians responded to the survey. Of these, 780 (75% preferred to share decision-making with their patients, 142 (14% preferred paternalism, and 118 (11% preferred consumerism. 87% of physicians perceived themselves as practicing their preferred style. Physicians who preferred their patients to play an active role in decision-making were more likely to report encouraging patients to look for information, and to report having enough time in visits. Conclusion Physicians tend to perceive themselves as practicing their preferred role in clinical decision-making. The direction of the association cannot be inferred from these data; however, we suggest that interventions aimed at promoting shared decision-making need to target physicians as well as patients.

The Use of a Modification of the Hurwicz’s Decision Rule in Multicriteria Decision Making under Complete Uncertainty

Directory of Open Access Journals (Sweden)

Helena Gaspars-Wieloch

2014-12-01

Full Text Available The paper concerns multicriteria decision making under uncertainty with scenario planning. This topic is explored by many researchers because almost all real-world decision problems have multiple conflicting criteria and a deterministic criteria evaluation is often impossible (e.g. mergers and acquisitions, new product development. We propose two procedures for uncertain multi-objective optimization (for dependent and independent criteria matrices which are based on the SAPO method – a modification of the Hurwicz’s rule for one-criterion problems, recently presented in another paper. The new approaches take into account the decision maker’s preference structure and attitude towards risk. It considers the frequency and the level of extreme evaluations and generates logic rankings for symmetric and asymmetric distributions. The application of the suggested tool is illustrated with an example of marketing strategies selection.

Integrating reasoning and clinical archetypes using OWL ontologies and SWRL rules.

Science.gov (United States)

Lezcano, Leonardo; Sicilia, Miguel-Angel; Rodríguez-Solano, Carlos

2011-04-01

Semantic interoperability is essential to facilitate the computerized support for alerts, workflow management and evidence-based healthcare across heterogeneous electronic health record (EHR) systems. Clinical archetypes, which are formal definitions of specific clinical concepts defined as specializations of a generic reference (information) model, provide a mechanism to express data structures in a shared and interoperable way. However, currently available archetype languages do not provide direct support for mapping to formal ontologies and then exploiting reasoning on clinical knowledge, which are key ingredients of full semantic interoperability, as stated in the SemanticHEALTH report [1]. This paper reports on an approach to translate definitions expressed in the openEHR Archetype Definition Language (ADL) to a formal representation expressed using the Ontology Web Language (OWL). The formal representations are then integrated with rules expressed with Semantic Web Rule Language (SWRL) expressions, providing an approach to apply the SWRL rules to concrete instances of clinical data. Sharing the knowledge expressed in the form of rules is consistent with the philosophy of open sharing, encouraged by archetypes. Our approach also allows the reuse of formal knowledge, expressed through ontologies, and extends reuse to propositions of declarative knowledge, such as those encoded in clinical guidelines. This paper describes the ADL-to-OWL translation approach, describes the techniques to map archetypes to formal ontologies, and demonstrates how rules can be applied to the resulting representation. We provide examples taken from a patient safety alerting system to illustrate our approach. Copyright © 2010 Elsevier Inc. All rights reserved.

A decision aid to rule out pneumonia and reduce unnecessary prescriptions of antibiotics in primary care patients with cough and fever

Directory of Open Access Journals (Sweden)

Hunziker Roger

2011-05-01

Full Text Available Abstract Background Physicians fear missing cases of pneumonia and treat many patients with signs of respiratory infection unnecessarily with antibiotics. This is an avoidable cause for the increasing worldwide problem of antibiotic resistance. We developed a user-friendly decision aid to rule out pneumonia and thus reduce the rate of needless prescriptions of antibiotics. Methods This was a prospective cohort study in which we enrolled patients older than 18 years with a new or worsened cough and fever without serious co-morbidities. Physicians recorded results of a standardized medical history and physical examination. C-reactive protein was measured and chest radiographs were obtained. We used Classification and Regression Trees to derive the decision tool. Results A total of 621 consenting eligible patients were studied, 598 were attending a primary care facility, were 48 years on average and 50% were male. Radiographic signs for pneumonia were present in 127 (20.5% of patients. Antibiotics were prescribed to 234 (48.3% of patients without pneumonia. In patients with C-reactive protein values below 10 μg/ml or patients presenting with C-reactive protein between 11 and 50 μg/ml, but without dyspnoea and daily fever, pneumonia can be ruled out. By applying this rule in clinical practice antibiotic prescription could be reduced by 9.1% (95% confidence interval (CI: 6.4 to 11.8. Conclusions Following validation and confirmation in new patient samples, this tool could help rule out pneumonia and be used to reduce unnecessary antibiotic prescriptions in patients presenting with cough and fever in primary care. The algorithm might be especially useful in those instances where taking a medical history and physical examination alone are inconclusive for ruling out pneumonia

Scheduling rules to achieve lead-time targets in outpatient appointment systems

OpenAIRE

Sivakumar, Appa Iyer; Nguyen, Thu Ba Thi; Graves, Stephen C

2015-01-01

This paper considers how to schedule appointments for outpatients, for a clinic that is subject to appointment lead-time targets for both new and returning patients. We develop heuristic rules, which are the exact and relaxed appointment scheduling rules, to schedule each new patient appointment (only) in light of uncertainty about future arrivals. The scheduling rules entail two decisions. First, the rules need to determine whether or not a patient's request can be accepted; then, if the req...

Individual versus Household Migration Decision Rules: Gender and Marital Status Differences in Intentions to Migrate in South Africa.

Science.gov (United States)

Gubhaju, Bina; De Jong, Gordon F

2009-03-01

This research tests the thesis that the neoclassical micro-economic and the new household economic theoretical assumptions on migration decision-making rules are segmented by gender, marital status, and time frame of intention to migrate. Comparative tests of both theories within the same study design are relatively rare. Utilizing data from the Causes of Migration in South Africa national migration survey, we analyze how individually held "own-future" versus alternative "household well-being" migration decision rules effect the intentions to migrate of male and female adults in South Africa. Results from the gender and marital status specific logistic regressions models show consistent support for the different gender-marital status decision rule thesis. Specifically, the "maximizing one's own future" neoclassical microeconomic theory proposition is more applicable for never married men and women, the "maximizing household income" proposition for married men with short-term migration intentions, and the "reduce household risk" proposition for longer time horizon migration intentions of married men and women. Results provide new evidence on the way household strategies and individual goals jointly affect intentions to move or stay.

Prediction of high-grade vesicoureteral reflux after pediatric urinary tract infection: external validation study of procalcitonin-based decision rule.

Directory of Open Access Journals (Sweden)

Sandrine Leroy

Full Text Available Predicting vesico-ureteral reflux (VUR ≥3 at the time of the first urinary tract infection (UTI would make it possible to restrict cystography to high-risk children. We previously derived the following clinical decision rule for that purpose: cystography should be performed in cases with ureteral dilation and a serum procalcitonin level ≥0.17 ng/mL, or without ureteral dilatation when the serum procalcitonin level ≥0.63 ng/mL. The rule yielded a 86% sensitivity with a 46% specificity. We aimed to test its reproducibility.A secondary analysis of prospective series of children with a first UTI. The rule was applied, and predictive ability was calculated.The study included 413 patients (157 boys, VUR ≥3 in 11% from eight centers in five countries. The rule offered a 46% specificity (95% CI, 41-52, not different from the one in the derivation study. However, the sensitivity significantly decreased to 64% (95%CI, 50-76, leading to a difference of 20% (95%CI, 17-36. In all, 16 (34% patients among the 47 with VUR ≥3 were misdiagnosed by the rule. This lack of reproducibility might result primarily from a difference between derivation and validation populations regarding inflammatory parameters (CRP, PCT; the validation set samples may have been collected earlier than for the derivation one.The rule built to predict VUR ≥3 had a stable specificity (ie. 46%, but a decreased sensitivity (ie. 64% because of the time variability of PCT measurement. Some refinement may be warranted.

Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs)

LENUS (Irish Health Repository)

Wallace, Emma

2011-10-14

Abstract Clinical Prediction Rules (CPRs) are tools that quantify the contribution of symptoms, clinical signs and available diagnostic tests, and in doing so stratify patients according to the probability of having a target outcome or need for a specified treatment. Most focus on the derivation stage with only a minority progressing to validation and very few undergoing impact analysis. Impact analysis studies remain the most efficient way of assessing whether incorporating CPRs into a decision making process improves patient care. However there is a lack of clear methodology for the design of high quality impact analysis studies. We have developed a sequential four-phased framework based on the literature and the collective experience of our international working group to help researchers identify and overcome the specific challenges in designing and conducting an impact analysis of a CPR. There is a need to shift emphasis from deriving new CPRs to validating and implementing existing CPRs. The proposed framework provides a structured approach to this topical and complex area of research.

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).

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

Decision making under internal uncertainty: the case of multiple-choice tests with different scoring rules.

Science.gov (United States)

Bereby-Meyer, Yoella; Meyer, Joachim; Budescu, David V

2003-02-01

This paper assesses framing effects on decision making with internal uncertainty, i.e., partial knowledge, by focusing on examinees' behavior in multiple-choice (MC) tests with different scoring rules. In two experiments participants answered a general-knowledge MC test that consisted of 34 solvable and 6 unsolvable items. Experiment 1 studied two scoring rules involving Positive (only gains) and Negative (only losses) scores. Although answering all items was the dominating strategy for both rules, the results revealed a greater tendency to answer under the Negative scoring rule. These results are in line with the predictions derived from Prospect Theory (PT) [Econometrica 47 (1979) 263]. The second experiment studied two scoring rules, which allowed respondents to exhibit partial knowledge. Under the Inclusion-scoring rule the respondents mark all answers that could be correct, and under the Exclusion-scoring rule they exclude all answers that might be incorrect. As predicted by PT, respondents took more risks under the Inclusion rule than under the Exclusion rule. The results illustrate that the basic process that underlies choice behavior under internal uncertainty and especially the effect of framing is similar to the process of choice under external uncertainty and can be described quite accurately by PT. Copyright 2002 Elsevier Science B.V.

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

The Diagnosis of Urinary Tract Infection in Young Children (DUTY) Study Clinical Rule: Economic Evaluation.

Science.gov (United States)

Hollingworth, William; Busby, John; Butler, Christopher C; O'Brien, Kathryn; Sterne, Jonathan A C; Hood, Kerenza; Little, Paul; Lawton, Michael; Birnie, Kate; Thomas-Jones, Emma; Harman, Kim; Hay, Alastair D

2017-04-01

To estimate the cost-effectiveness of a two-step clinical rule using symptoms, signs and dipstick testing to guide the diagnosis and antibiotic treatment of urinary tract infection (UTI) in acutely unwell young children presenting to primary care. Decision analytic model synthesising data from a multicentre, prospective cohort study (DUTY) and the wider literature to estimate the short-term and lifetime costs and healthcare outcomes (symptomatic days, recurrent UTI, quality adjusted life years) of eight diagnostic strategies. We compared GP clinical judgement with three strategies based on a 'coefficient score' combining seven symptoms and signs independently associated with UTI and four strategies based on weighted scores according to the presence/absence of five symptoms and signs. We compared dipstick testing versus laboratory culture in children at intermediate risk of UTI. Sampling, culture and antibiotic costs were lowest in high-specificity DUTY strategies (£1.22 and £1.08) compared to clinical judgement (£1.99). These strategies also approximately halved urine sampling (4.8% versus 9.1% in clinical judgement) without reducing sensitivity (58.2% versus 56.4%). Outcomes were very similar across all diagnostic strategies. High-specificity DUTY strategies were more cost-effective than clinical judgement in the short- (iNMB = £0.78 and £0.84) and long-term (iNMB =£2.31 and £2.50). Dipstick tests had poorer cost-effectiveness than laboratory culture in children at intermediate risk of UTI (iNMB = £-1.41). Compared to GPs' clinical judgement, high specificity clinical rules from the DUTY study could substantially reduce urine sampling, achieving lower costs and equivalent patient outcomes. Dipstick testing children for UTI is not cost-effective. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Non-clinical influences on clinical decision-making: a major challenge to evidence-based practice.

Science.gov (United States)

Hajjaj, F M; Salek, M S; Basra, M K A; Finlay, A Y

2010-05-01

This article reviews an aspect of daily clinical practice which is of critical importance in virtually every clinical consultation, but which is seldom formally considered. Non-clinical influences on clinical decision-making profoundly affect medical decisions. These influences include patient-related factors such as socioeconomic status, quality of life and patient's expectations and wishes, physician-related factors such as personal characteristics and interaction with their professional community, and features of clinical practice such as private versus public practice as well as local management policies. This review brings together the different strands of knowledge concerning non-clinical influences on clinical decision-making. This aspect of decision-making may be the biggest obstacle to the reality of practising evidence-based medicine. It needs to be understood in order to develop clinical strategies that will facilitate the practice of evidence-based medicine.

Biometric image enhancement using decision rule based image fusion techniques

Science.gov (United States)

Sagayee, G. Mary Amirtha; Arumugam, S.

2010-02-01

Introducing biometrics into information systems may result in considerable benefits. Most of the researchers confirmed that the finger print is widely used than the iris or face and more over it is the primary choice for most privacy concerned applications. For finger prints applications, choosing proper sensor is at risk. The proposed work deals about, how the image quality can be improved by introducing image fusion technique at sensor levels. The results of the images after introducing the decision rule based image fusion technique are evaluated and analyzed with its entropy levels and root mean square error.

The spatial decision-supporting system combination of RBR & CBR based on artificial neural network and association rules

Science.gov (United States)

Tian, Yangge; Bian, Fuling

2007-06-01

The technology of artificial intelligence should be imported on the basis of the geographic information system to bring up the spatial decision-supporting system (SDSS). The paper discusses the structure of SDSS, after comparing the characteristics of RBR and CBR, the paper brings up the frame of a spatial decisional system that combines RBR and CBR, which has combined the advantages of them both. And the paper discusses the CBR in agriculture spatial decisions, the application of ANN (Artificial Neural Network) in CBR, and enriching the inference rule base based on association rules, etc. And the paper tests and verifies the design of this system with the examples of the evaluation of the crops' adaptability.

A critical comparison of clinical decision instruments for computed tomographic scanning in mild closed traumatic brain injury in adolescents and adults.

Science.gov (United States)

Stein, Sherman C; Fabbri, Andrea; Servadei, Franco; Glick, Henry A

2009-02-01

A number of clinical decision aids have been introduced to limit unnecessary computed tomographic scans in patients with mild traumatic brain injury. These aids differ in the risk factors they use to recommend a scan. We compare the instruments according to their sensitivity and specificity and recommend ones based on incremental benefit of correctly classifying patients as having surgical, nonsurgical, or no intracranial lesions. We performed a secondary analysis of prospectively collected database from 7,955 patients aged 10 years or older with mild traumatic brain injury to compare sensitivity and specificity of 6 common clinical decision strategies: the Canadian CT Head Rule, the Neurotraumatology Committee of the World Federation of Neurosurgical Societies, the New Orleans, the National Emergency X-Radiography Utilization Study II (NEXUS-II), the National Institute of Clinical Excellence guideline, and the Scandinavian Neurotrauma Committee guideline. Excluded from the database were patients for whom the history of trauma was unclear, the initial Glasgow Coma Scale score was less than 14, the injury was penetrating, vital signs were unstable, or who refused diagnostic tests. Patients revisiting the emergency department within 7 days were counted only once. The percentage of scans that would have been required by applying each of the 6 aids were Canadian CT head rule (high risk only) 53%, Canadian (medium & high risk) 56%, the Neurotraumatology Committee of the World Federation of Neurosurgical Societies 56%, New Orleans 69%, NEXUS-II 56%, National Institute of Clinical Excellence 71%, and the Scandinavian 50%. The 6 decision aids' sensitivities for surgical hematomas could not be distinguished statistically (P>.05). Sensitivity was 100% (95% confidence interval [CI] 96% to 100%) for NEXUS-II, 98.1% (95% CI 93% to 100%) for National Institute of Clinical Excellence, and 99.1% (95% CI 94% to 100%) for the other 4 clinical decision instruments. Sensitivity for

Logical-Rule Models of Classification Response Times: A Synthesis of Mental-Architecture, Random-Walk, and Decision-Bound Approaches

Science.gov (United States)

Fific, Mario; Little, Daniel R.; Nosofsky, Robert M.

2010-01-01

We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli…

Elbow Room for Best Practice? Montgomery, Patients' values, and Balanced Decision-Making in Person-Centred Clinical Care.

Science.gov (United States)

Herring, Jonathan; Fulford, Kmw; Dunn, Michael; Handa, Ashoki

2017-11-01

The UK Supreme Court Montgomery judgment marks a decisive shift in the legal test of duty of care in the context of consent to treatment, from the perspective of the clinician (as represented by Bolam rules) to that of the patient. A majority of commentators on Montgomery have focused on the implications of the judgment for disclosure of risk. In this article, we set risk disclosure in context with three further elements of the judgment: benefits, options, and dialogue. These elements, we argue, taken together with risk disclosure, reflect the origins of the Montgomery ruling in a model of consent based on autonomy of patient choice through shared decision-making with their doctor. This model reflects recent developments in both law and medicine and is widely regarded (by the General Medical Council and others) as representing best practice in contemporary person-centred medicine. So understood, we suggest, the shift marked by Montgomery in the basis of duty of care is a shift in underpinning values: it is a shift from the clinician's interpretation about what would be best for patients to the values of (to what is significant or matters from the perspective of) the particular patient concerned in the decision in question. But the values of the particular patient do not thereby become paramount. The Montgomery test of duty of care requires the values of the particular patient to be balanced alongside the values of a reasonable person in the patient's position. We illustrate some of the practical challenges arising from the balance of considerations required by Montgomery with examples from surgical care. These examples show the extent to which Montgomery, in mirroring the realities of clinical decision-making, provides elbowroom for best practice in person-centred clinical care. © The Author 2017. Published by Oxford University Press; all rights reserved. For Permissions, please email: journals.permissions@oup.com.

Is it the time to rethink clinical decision-making strategies? From a single clinical outcome evaluation to a Clinical Multi-criteria Decision Assessment (CMDA).

Science.gov (United States)

Migliore, Alberto; Integlia, Davide; Bizzi, Emanuele; Piaggio, Tomaso

2015-10-01

There are plenty of different clinical, organizational and economic parameters to consider in order having a complete assessment of the total impact of a pharmaceutical treatment. In the attempt to follow, a holistic approach aimed to provide an evaluation embracing all clinical parameters in order to choose the best treatments, it is necessary to compare and weight multiple criteria. Therefore, a change is required: we need to move from a decision-making context based on the assessment of one single criteria towards a transparent and systematic framework enabling decision makers to assess all relevant parameters simultaneously in order to choose the best treatment to use. In order to apply the MCDA methodology to clinical decision making the best pharmaceutical treatment (or medical devices) to use to treat a specific pathology, we suggest a specific application of the Multiple Criteria Decision Analysis for the purpose, like a Clinical Multi-criteria Decision Assessment CMDA. In CMDA, results from both meta-analysis and observational studies are used by a clinical consensus after attributing weights to specific domains and related parameters. The decision will result from a related comparison of all consequences (i.e., efficacy, safety, adherence, administration route) existing behind the choice to use a specific pharmacological treatment. The match will yield a score (in absolute value) that link each parameter with a specific intervention, and then a final score for each treatment. The higher is the final score; the most appropriate is the intervention to treat disease considering all criteria (domain an parameters). The results will allow the physician to evaluate the best clinical treatment for his patients considering at the same time all relevant criteria such as clinical effectiveness for all parameters and administration route. The use of CMDA model will yield a clear and complete indication of the best pharmaceutical treatment to use for patients

Decision Tree Repository and Rule Set Based Mingjiang River Estuarine Wetlands Classifaction

Science.gov (United States)

Zhang, W.; Li, X.; Xiao, W.

2018-05-01

The increasing urbanization and industrialization have led to wetland losses in estuarine area of Mingjiang River over past three decades. There has been increasing attention given to produce wetland inventories using remote sensing and GIS technology. Due to inconsistency training site and training sample, traditionally pixel-based image classification methods can't achieve a comparable result within different organizations. Meanwhile, object-oriented image classification technique shows grate potential to solve this problem and Landsat moderate resolution remote sensing images are widely used to fulfill this requirement. Firstly, the standardized atmospheric correct, spectrally high fidelity texture feature enhancement was conducted before implementing the object-oriented wetland classification method in eCognition. Secondly, we performed the multi-scale segmentation procedure, taking the scale, hue, shape, compactness and smoothness of the image into account to get the appropriate parameters, using the top and down region merge algorithm from single pixel level, the optimal texture segmentation scale for different types of features is confirmed. Then, the segmented object is used as the classification unit to calculate the spectral information such as Mean value, Maximum value, Minimum value, Brightness value and the Normalized value. The Area, length, Tightness and the Shape rule of the image object Spatial features and texture features such as Mean, Variance and Entropy of image objects are used as classification features of training samples. Based on the reference images and the sampling points of on-the-spot investigation, typical training samples are selected uniformly and randomly for each type of ground objects. The spectral, texture and spatial characteristics of each type of feature in each feature layer corresponding to the range of values are used to create the decision tree repository. Finally, with the help of high resolution reference images, the

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.

Robust Management of Combined Heat and Power Systems via Linear Decision Rules

DEFF Research Database (Denmark)

Zugno, Marco; Morales González, Juan Miguel; Madsen, Henrik

2014-01-01

The heat and power outputs of Combined Heat and Power (CHP) units are jointly constrained. Hence, the optimal management of systems including CHP units is a multicommodity optimization problem. Problems of this type are stochastic, owing to the uncertainty inherent both in the demand for heat and...... linear decision rules to guarantee both tractability and a correct representation of the dynamic aspects of the problem. Numerical results from an illustrative example confirm the value of the proposed approach....

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,...

Physiotherapy clinical educators' perceptions and experiences of clinical prediction rules.

Science.gov (United States)

Knox, Grahame M; Snodgrass, Suzanne J; Rivett, Darren A

2015-12-01

Clinical prediction rules (CPRs) are widely used in medicine, but their application to physiotherapy practice is more recent and less widespread, and their implementation in physiotherapy clinical education has not been investigated. This study aimed to determine the experiences and perceptions of physiotherapy clinical educators regarding CPRs, and whether they are teaching CPRs to students on clinical placement. Cross-sectional observational survey using a modified Dillman method. Clinical educators (n=211, response rate 81%) supervising physiotherapy students from 10 universities across 5 states and territories in Australia. Half (48%) of respondents had never heard of CPRs, and a further 25% had never used CPRs. Only 27% reported using CPRs, and of these half (51%) were rarely if ever teaching CPRs to students in the clinical setting. However most respondents (81%) believed CPRs assisted in the development of clinical reasoning skills and few (9%) were opposed to teaching CPRs to students. Users of CPRs were more likely to be male (pphysiotherapy (pStudents are unlikely to be learning about CPRs on clinical placement, as few clinical educators use them. Clinical educators will require training in CPRs and assistance in teaching them if students are to better learn about implementing CPRs in physiotherapy clinical practice. Copyright © 2015 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

Clinical decision regret among critical care nurses: a qualitative analysis.

Science.gov (United States)

Arslanian-Engoren, Cynthia; Scott, Linda D

2014-01-01

Decision regret is a negative cognitive emotion associated with experiences of guilt and situations of interpersonal harm. These negative affective responses may contribute to emotional exhaustion in critical care nurses (CCNs), increased staff turnover rates and high medication error rates. Yet, little is known about clinical decision regret among CCNs or the conditions or situations (e.g., feeling sleepy) that may precipitate its occurrence. To examine decision regret among CCNs, with an emphasis on clinical decisions made when nurses were most sleepy. A content analytic approach was used to examine the narrative descriptions of clinical decisions by CCNs when sleepy. Six decision regret themes emerged that represented deviations in practice or performance behaviors that were attributed to fatigued CCNs. While 157 CCNs disclosed a clinical decision they made at work while sleepy, the prevalence may be underestimated and warrants further investigation. Copyright © 2014 Elsevier Inc. All rights reserved.

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...

Conceptualising a system for quality clinical decision-making in ...

African Journals Online (AJOL)

As a feedback mechanism to promote or improve the quality of clinical decisions in nursing, standards for quality clinical decision-making are proposed in an exemplary manner. In addition, a system for quality clinical decisionmaking in nursing capitalises on the heritage of the nursing process. Considering the changes and ...

Failsafe automation of Phase II clinical trial interim monitoring for stopping rules.

Science.gov (United States)

Day, Roger S

2010-02-01

In Phase II clinical trials in cancer, preventing the treatment of patients on a study when current data demonstrate that the treatment is insufficiently active or too toxic has obvious benefits, both in protecting patients and in reducing sponsor costs. Considerable efforts have gone into experimental designs for Phase II clinical trials with flexible sample size, usually implemented by early stopping rules. The intended benefits will not ensue, however, if the design is not followed. Despite the best intentions, failures can occur for many reasons. The main goal is to develop an automated system for interim monitoring, as a backup system supplementing the protocol team, to ensure that patients are protected. A secondary goal is to stimulate timely recording of patient assessments. We developed key concepts and performance needs, then designed, implemented, and deployed a software solution embedded in the clinical trials database system. The system has been in place since October 2007. One clinical trial tripped the automated monitor, resulting in e-mails that initiated statistician/investigator review in timely fashion. Several essential contributing activities still require human intervention, institutional policy decisions, and institutional commitment of resources. We believe that implementing the concepts presented here will provide greater assurance that interim monitoring plans are followed and that patients are protected from inadequate response or excessive toxicity. This approach may also facilitate wider acceptance and quicker implementation of new interim monitoring algorithms.

The Importance of Conditional Probability in Diagnostic Reasoning and Clinical Decision Making: A Primer for the Eye Care Practitioner.

Science.gov (United States)

Sanfilippo, Paul G; Hewitt, Alex W; Mackey, David A

2017-04-01

To outline and detail the importance of conditional probability in clinical decision making and discuss the various diagnostic measures eye care practitioners should be aware of in order to improve the scope of their clinical practice. We conducted a review of the importance of conditional probability in diagnostic testing for the eye care practitioner. Eye care practitioners use diagnostic tests on a daily basis to assist in clinical decision making and optimizing patient care and management. These tests provide probabilistic information that can enable the clinician to increase (or decrease) their level of certainty about the presence of a particular condition. While an understanding of the characteristics of diagnostic tests are essential to facilitate proper interpretation of test results and disease risk, many practitioners either confuse or misinterpret these measures. In the interests of their patients, practitioners should be aware of the basic concepts associated with diagnostic testing and the simple mathematical rule that underpins them. Importantly, the practitioner needs to recognize that the prevalence of a disease in the population greatly determines the clinical value of a diagnostic test.

Non-clinical influences on clinical decision-making: a major challenge to evidence-based practice

OpenAIRE

Hajjaj, FM; Salek, MS; Basra, MKA; Finlay, AY

2010-01-01

This article reviews an aspect of daily clinical practice which is of critical importance in virtually every clinical consultation, but which is seldom formally considered. Non-clinical influences on clinical decision-making profoundly affect medical decisions. These influences include patient-related factors such as socioeconomic status, quality of life and patient's expectations and wishes, physician-related factors such as personal characteristics and interaction with their professional co...

Factors and outcomes of decision making for cancer clinical trial participation.

Science.gov (United States)

Biedrzycki, Barbara A

2011-09-01

To describe factors and outcomes related to the decision-making process regarding participation in a cancer clinical trial. Cross-sectional, descriptive. Urban, academic, National Cancer Institute-designated comprehensive cancer center in the mid-Atlantic United States. 197 patients with advanced gastrointestinal cancer. Mailed survey using one investigator-developed instrument, eight instruments used in published research, and a medical record review. disease context, sociodemographics, hope, quality of life, trust in healthcare system, trust in health professional, preference for research decision control, understanding risks, and information. decision to accept or decline research participation and satisfaction with this decision. All of the factors within the Research Decision Making Model together predicted cancer clinical trial participation and satisfaction with this decision. The most frequently preferred decision-making style for research participation was shared (collaborative) (83%). Multiple factors affect decision making for cancer clinical trial participation and satisfaction with this decision. Shared decision making previously was an unrecognized factor and requires further investigation. Enhancing the process of research decision making may facilitate an increase in cancer clinical trial enrollment rates. Oncology nurses have unique opportunities as educators and researchers to support shared decision making by those who prefer this method for deciding whether to accept or decline cancer clinical trial participation.

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.

Integrative and distributive negotiation in small groups : Effects of task structure, decision rule, and social motive

NARCIS (Netherlands)

Beersma, Bianca; De Dreu, Carsten K W

2002-01-01

This study examined the interactive effects of task structure, decision rule, and social motive on small-group negotiation processes and outcomes. Three-person groups negotiated either within an asymmetrical task structure (in which a majority of group members have compatible interests) or within a

Accuracy of intuition in clinical decision-making among novice clinicians.

Science.gov (United States)

Price, Amanda; Zulkosky, Kristen; White, Krista; Pretz, Jean

2017-05-01

To assess the reliance on intuitive and analytical approaches during clinical decision-making among novice clinicians and whether that reliance is associated with accurate decision-making. Nurse educators and managers tend to emphasize analysis over intuition during clinical decision-making though nurses typically report some reliance on intuition in their practice. We hypothesized that under certain conditions, reliance on intuition would support accurate decision-making, even among novices. This study utilized an experimental design with clinical complication (familiar vs. novel) and decision phase (cue acquisition, diagnosis and action) as within-subjects' factors, and simulation role (observer, family, auxiliary nurse and primary nurse) as between-subjects' factor. We examined clinical decision-making accuracy among final semester pre-licensure nursing students in a simulation experience. Students recorded their reasoning about emerging clinical complications with their patient during two distinct points in the simulation; one point involved a familiar complication and the other a relatively novel complication. All data were collected during Spring 2015. Although most participants relied more heavily on analysis than on intuition, use of intuition during the familiar complication was associated with more accurate decision-making, particularly in guiding attention to relevant cues. With the novel complication, use of intuition appeared to hamper decision-making, particularly for those in an observer role. Novice clinicians should be supported by educators and nurse managers to note when their intuitions are likely to be valid. Our findings emphasize the integrated nature of intuition and analysis in clinical decision-making. © 2016 John Wiley & Sons Ltd.

Length and coverage of inhibitory decision rules

KAUST Repository

Alsolami, Fawaz

2012-01-01

Authors present algorithms for optimization of inhibitory rules relative to the length and coverage. Inhibitory rules have a relation "attribute ≠ value" on the right-hand side. The considered algorithms are based on extensions of dynamic programming. Paper contains also comparison of length and coverage of inhibitory rules constructed by a greedy algorithm and by the dynamic programming algorithm. © 2012 Springer-Verlag.

Factors influencing the clinical decision-making of midwives: a qualitative study.

Science.gov (United States)

Daemers, Darie O A; van Limbeek, Evelien B M; Wijnen, Hennie A A; Nieuwenhuijze, Marianne J; de Vries, Raymond G

2017-10-06

Although midwives make clinical decisions that have an impact on the health and well-being of mothers and babies, little is known about how they make those decisions. Wide variation in intrapartum decisions to refer women to obstetrician-led care suggests that midwives' decisions are based on more than the evidence based medicine (EBM) model - i.e. clinical evidence, midwife's expertise, and woman's values - alone. With this study we aimed to explore the factors that influence clinical decision-making of midwives who work independently. We used a qualitative approach, conducting in-depth interviews with a purposive sample of 11 Dutch primary care midwives. Data collection took place between May and September 2015. The interviews were semi-structured, using written vignettes to solicit midwives' clinical decision-making processes (Think Aloud method). We performed thematic analysis on the transcripts. We identified five themes that influenced clinical decision-making: the pregnant woman as a whole person, sources of knowledge, the midwife as a whole person, the collaboration between maternity care professionals, and the organisation of care. Regarding the midwife, her decisions were shaped not only by her experience, intuition, and personal circumstances, but also by her attitudes about physiology, woman-centredness, shared decision-making, and collaboration with other professionals. The nature of the local collaboration between maternity care professionals and locally-developed protocols dominated midwives' clinical decision-making. When midwives and obstetricians had different philosophies of care and different practice styles, their collaborative efforts were challenged. Midwives' clinical decision-making is a more varied and complex process than the EBM framework suggests. If midwives are to succeed in their role as promoters and protectors of physiological pregnancy and birth, they need to understand how clinical decisions in a multidisciplinary context are

Accuracy of clinical prediction rules in peptic ulcer perforation: an observational study

DEFF Research Database (Denmark)

Buck, David Levarett; Vester-Andersen, Morten; Møller, Morten Hylander

2012-01-01

Abstract Objective. The aim of the present study was to compare the ability of four clinical prediction rules to predict adverse outcome in perforated peptic ulcer (PPU): the Boey score, the American Society of Anesthesiologists (ASA) score, the Acute Physiology and Chronic Health Evaluation...... and breastfeeding women, non-surgically treated patients, patients with malignant ulcers, and patients with perforation of other organs were excluded. Primary outcome measure: 30-day mortality rate. Statistical analysis: the ability of four clinical prediction rules to distinguish survivors from non...

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

Transforming clinical practice guidelines and clinical pathways into fast-and-frugal decision trees to improve clinical care strategies.

Science.gov (United States)

Djulbegovic, Benjamin; Hozo, Iztok; Dale, William

2018-02-27

Contemporary delivery of health care is inappropriate in many ways, largely due to suboptimal Q5 decision-making. A typical approach to improve practitioners' decision-making is to develop evidence-based clinical practice guidelines (CPG) by guidelines panels, who are instructed to use their judgments to derive practice recommendations. However, mechanisms for the formulation of guideline judgments remains a "black-box" operation-a process with defined inputs and outputs but without sufficient knowledge of its internal workings. Increased explicitness and transparency in the process can be achieved by implementing CPG as clinical pathways (CPs) (also known as clinical algorithms or flow-charts). However, clinical recommendations thus derived are typically ad hoc and developed by experts in a theory-free environment. As any recommendation can be right (true positive or negative), or wrong (false positive or negative), the lack of theoretical structure precludes the quantitative assessment of the management strategies recommended by CPGs/CPs. To realize the full potential of CPGs/CPs, they need to be placed on more solid theoretical grounds. We believe this potential can be best realized by converting CPGs/CPs within the heuristic theory of decision-making, often implemented as fast-and-frugal (FFT) decision trees. This is possible because FFT heuristic strategy of decision-making can be linked to signal detection theory, evidence accumulation theory, and a threshold model of decision-making, which, in turn, allows quantitative analysis of the accuracy of clinical management strategies. Fast-and-frugal provides a simple and transparent, yet solid and robust, methodological framework connecting decision science to clinical care, a sorely needed missing link between CPGs/CPs and patient outcomes. We therefore advocate that all guidelines panels express their recommendations as CPs, which in turn should be converted into FFTs to guide clinical care. © 2018 John Wiley

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.

The impact of simulation sequencing on perceived clinical decision making.

Science.gov (United States)

Woda, Aimee; Hansen, Jamie; Paquette, Mary; Topp, Robert

2017-09-01

An emerging nursing education trend is to utilize simulated learning experiences as a means to optimize competency and decision making skills. The purpose of this study was to examine differences in students' perception of clinical decision making and clinical decision making-related self-confidence and anxiety based on the sequence (order) in which they participated in a block of simulated versus hospital-based learning experiences. A quasi-experimental crossover design was used. Between and within group differences were found relative to self-confidence with the decision making process. When comparing groups, at baseline the simulation followed by hospital group had significantly higher self-confidence scores, however, at 14-weeks both groups were not significantly different. Significant within group differences were found in the simulation followed by hospital group only, demonstrating a significant decrease in clinical decision making related anxiety across the semester. Finally, there were no significant difference in; perceived clinical decision making within or between the groups at the two measurement points. Preliminary findings suggest that simulated learning experiences can be offered with alternating sequences without impacting the process, anxiety or confidence with clinical decision making. This study provides beginning evidence to guide curriculum development and allow flexibility based on student needs and available resources. Copyright © 2017. Published by Elsevier Ltd.

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.

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

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

Clinical decision making in a high-risk primary care environment: a qualitative study in the UK.

Science.gov (United States)

Balla, John; Heneghan, Carl; Thompson, Matthew; Balla, Margaret

2012-01-01

Examine clinical reasoning and decision making in an out of hours (OOH) primary care setting to gain insights into how general practitioners (GPs) make clinical decisions and manage risk in this environment. Semi-structured interviews using open-ended questions. A 2-month qualitative interview study conducted in Oxfordshire, UK. 21 GPs working in OOH primary care. The most powerful themes to emerge related to dealing with urgent potentially high-risk cases, keeping patients safe and responding to their needs, while trying to keep patients out of hospital and the concept of 'fire fighting'. There were a number of well-defined characteristics that GPs reported making presentations easy or difficult to deal with. Severely ill patients were straightforward, while the older people, with complex multisystem diseases, were often difficult. GPs stopped collecting clinical information and came to clinical decisions when high-risk disease and severe illness requiring hospital attention has been excluded; they had responded directly to the patient's needs and there was a reliable safety net in place. Learning points that GPs identified as important for trainees in the OOH setting included the importance of developing rapport in spite of time pressures, learning to deal with uncertainty and learning about common presentations with a focus on critical cues to exclude severe illness. The findings support suggestions that improvements in primary care OOH could be achieved by including automated and regular timely feedback system for GPs and individual peer and expert clinician support for GPs with regular meetings to discuss recent cases. In addition, trainee support and mentoring to focus on clinical skills, knowledge and risk management issues specific to OOH is currently required. Investigating the stopping rules used for diagnostic closure may provide new insights into the root causes of clinical error in such a high-risk setting.

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

The merits of unconscious thought in rule detection.

Science.gov (United States)

Li, Jiansheng; Zhu, Yawen; Yang, Yang

2014-01-01

According to unconscious thought theory (UTT), unconscious thought is more adept at complex decision-making than is conscious thought. Related research has mainly focused on the complexity of decision-making tasks as determined by the amount of information provided. However, the complexity of the rules generating this information also influences decision making. Therefore, we examined whether unconscious thought facilitates the detection of rules during a complex decision-making task. Participants were presented with two types of letter strings. One type matched a grammatical rule, while the other did not. Participants were then divided into three groups according to whether they made decisions using conscious thought, unconscious thought, or immediate decision. The results demonstrated that the unconscious thought group was more accurate in identifying letter strings that conformed to the grammatical rule than were the conscious thought and immediate decision groups. Moreover, performance of the conscious thought and immediate decision groups was similar. We conclude that unconscious thought facilitates the detection of complex rules, which is consistent with UTT.

The merits of unconscious thought in rule detection.

Directory of Open Access Journals (Sweden)

Jiansheng Li

Full Text Available According to unconscious thought theory (UTT, unconscious thought is more adept at complex decision-making than is conscious thought. Related research has mainly focused on the complexity of decision-making tasks as determined by the amount of information provided. However, the complexity of the rules generating this information also influences decision making. Therefore, we examined whether unconscious thought facilitates the detection of rules during a complex decision-making task. Participants were presented with two types of letter strings. One type matched a grammatical rule, while the other did not. Participants were then divided into three groups according to whether they made decisions using conscious thought, unconscious thought, or immediate decision. The results demonstrated that the unconscious thought group was more accurate in identifying letter strings that conformed to the grammatical rule than were the conscious thought and immediate decision groups. Moreover, performance of the conscious thought and immediate decision groups was similar. We conclude that unconscious thought facilitates the detection of complex rules, which is consistent with UTT.

Clinical Decision Making of Nurses Working in Hospital Settings

OpenAIRE

Ida Torunn Bjørk; Glenys A. Hamilton

2011-01-01

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 d...

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).

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...

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.

Rules of thumb in life-cycle savings models

OpenAIRE

Rodepeter, Ralf; Winter, Joachim

1999-01-01

We analyze life-cycle savings decisions when households use simple heuristics, or rules of thumb, rather than solve the underlying intertemporal optimization problem. The decision rules we explore are a simple Keynesian rule where consumption follows income; a simple consumption rule where only a fraction of positive income shocks is saved; a rule that corresponds to the permanent income hypothesis; and two rules that have been found in experimental studies. Using these rules, we simulate lif...

Age-Adjusted D-Dimer in the Prediction of Pulmonary Embolism: Does a Normal Age-Adjusted D-Dimer Rule Out PE?

Directory of Open Access Journals (Sweden)

Jacob Ortiz

2017-01-01

Full Text Available Risk assessment for pulmonary embolism (PE currently relies on physician judgment, clinical decision rules (CDR, and D-dimer testing. There is still controversy regarding the role of D-dimer testing in low or intermediate risk patients. The objective of the study was to define the role of clinical decision rules and D-dimer testing in patients suspected of having a PE. Records of 894 patients referred for computed tomography pulmonary angiography (CTPA at a University medical center were analyzed. The clinical decision rules overall had an ROC of approximately 0.70, while signs of DVT had the highest ROC (0.80. A low probability CDR coupled with a negative age-adjusted D-dimer largely excluded PE. The negative predictive value (NPV of an intermediate CDR was 86–89%, while the addition of a negative D-dimer resulted in NPVs of 94%. Thus, in patients suspected of having a PE, a low or intermediate CDR does not exclude PE; however, in patients with an intermediate CDR, a normal age-adjusted D-dimer increases the NPV.

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.

A random walk rule for phase I clinical trials.

Science.gov (United States)

Durham, S D; Flournoy, N; Rosenberger, W F

1997-06-01

We describe a family of random walk rules for the sequential allocation of dose levels to patients in a dose-response study, or phase I clinical trial. Patients are sequentially assigned the next higher, same, or next lower dose level according to some probability distribution, which may be determined by ethical considerations as well as the patient's response. It is shown that one can choose these probabilities in order to center dose level assignments unimodally around any target quantile of interest. Estimation of the quantile is discussed; the maximum likelihood estimator and its variance are derived under a two-parameter logistic distribution, and the maximum likelihood estimator is compared with other nonparametric estimators. Random walk rules have clear advantages: they are simple to implement, and finite and asymptotic distribution theory is completely worked out. For a specific random walk rule, we compute finite and asymptotic properties and give examples of its use in planning studies. Having the finite distribution theory available and tractable obviates the need for elaborate simulation studies to analyze the properties of the design. The small sample properties of our rule, as determined by exact theory, compare favorably to those of the continual reassessment method, determined by simulation.

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

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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

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

Accounting standards and earnings management : The role of rules-based and principles-based accounting standards and incentives on accounting and transaction decisions

NARCIS (Netherlands)

Beest, van F.

2012-01-01

This book examines the effect that rules-based and principles-based accounting standards have on the level and nature of earnings management decisions. A cherry picking experiment is conducted to test the hypothesis that a substitution effect is expected from accounting decisions to transaction

The normalization heuristic: an untested hypothesis that may misguide medical decisions.

Science.gov (United States)

Aberegg, Scott K; O'Brien, James M

2009-06-01

Medical practice is increasingly informed by the evidence from randomized controlled trials. When such evidence is not available, clinical hypotheses based on pathophysiological reasoning and common sense guide clinical decision making. One commonly utilized general clinical hypothesis is the assumption that normalizing abnormal laboratory values and physiological parameters will lead to improved patient outcomes. We refer to the general use of this clinical hypothesis to guide medical therapeutics as the "normalization heuristic". In this paper, we operationally define this heuristic and discuss its limitations as a rule of thumb for clinical decision making. We review historical and contemporaneous examples of normalization practices as empirical evidence for the normalization heuristic and to highlight its frailty as a guide for clinical decision making.

Clinical decision-making by midwives: managing case complexity.

Science.gov (United States)

Cioffi, J; Markham, R

1997-02-01

In making clinical judgements, it is argued that midwives use 'shortcuts' or heuristics based on estimated probabilities to simplify the decision-making task. Midwives (n = 30) were given simulated patient assessment situations of high and low complexity and were required to think aloud. Analysis of verbal protocols showed that subjective probability judgements (heuristics) were used more frequently in the high than low complexity case and predominated in the last quarter of the assessment period for the high complexity case. 'Representativeness' was identified more frequently in the high than in the low case, but was the dominant heuristic in both. Reports completed after each simulation suggest that heuristics based on memory for particular conditions affect decisions. It is concluded that midwives use heuristics, derived mainly from their clinical experiences, in an attempt to save cognitive effort and to facilitate reasonably accurate decisions in the decision-making process.

Diagnostic accuracy of the STRATIFY clinical prediction rule for falls: A systematic review and meta-analysis

LENUS (Irish Health Repository)

Billington, Jennifer

2012-08-07

AbstractBackgroundThe STRATIFY score is a clinical prediction rule (CPR) derived to assist clinicians to identify patients at risk of falling. The purpose of this systematic review and meta-analysis is to determine the overall diagnostic accuracy of the STRATIFY rule across a variety of clinical settings.MethodsA literature search was performed to identify all studies that validated the STRATIFY rule. The methodological quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A STRATIFY score of ≥2 points was used to identify individuals at higher risk of falling. All included studies were combined using a bivariate random effects model to generate pooled sensitivity and specificity of STRATIFY at ≥2 points. Heterogeneity was assessed using the variance of logit transformed sensitivity and specificity.ResultsSeventeen studies were included in our meta-analysis, incorporating 11,378 patients. At a score ≥2 points, the STRATIFY rule is more useful at ruling out falls in those classified as low risk, with a greater pooled sensitivity estimate (0.67, 95% CI 0.52–0.80) than specificity (0.57, 95% CI 0.45 – 0.69). The sensitivity analysis which examined the performance of the rule in different settings and subgroups also showed broadly comparable results, indicating that the STRATIFY rule performs in a similar manner across a variety of different ‘at risk’ patient groups in different clinical settings.ConclusionThis systematic review shows that the diagnostic accuracy of the STRATIFY rule is limited and should not be used in isolation for identifying individuals at high risk of falls in clinical practice.

Enhancing clinical decision making: development of a contiguous definition and conceptual framework.

Science.gov (United States)

Tiffen, Jennifer; Corbridge, Susan J; Slimmer, Lynda

2014-01-01

Clinical decision making is a term frequently used to describe the fundamental role of the nurse practitioner; however, other terms have been used interchangeably. The purpose of this article is to begin the process of developing a definition and framework of clinical decision making. The developed definition was "Clinical decision making is a contextual, continuous, and evolving process, where data are gathered, interpreted, and evaluated in order to select an evidence-based choice of action." A contiguous framework for clinical decision making specific for nurse practitioners is also proposed. Having a clear and unique understanding of clinical decision making will allow for consistent use of the term, which is relevant given the changing educational requirements for nurse practitioners and broadening scope of practice. Copyright © 2014 Elsevier Inc. All rights reserved.

The triangular density to approximate the normal density: decision rules-of-thumb

International Nuclear Information System (INIS)

Scherer, William T.; Pomroy, Thomas A.; Fuller, Douglas N.

2003-01-01

In this paper we explore the approximation of the normal density function with the triangular density function, a density function that has extensive use in risk analysis. Such an approximation generates a simple piecewise-linear density function and a piecewise-quadratic distribution function that can be easily manipulated mathematically and that produces surprisingly accurate performance under many instances. This mathematical tractability proves useful when it enables closed-form solutions not otherwise possible, as with problems involving the embedded use of the normal density. For benchmarking purposes we compare the basic triangular approximation with two flared triangular distributions and with two simple uniform approximations; however, throughout the paper our focus is on using the triangular density to approximate the normal for reasons of parsimony. We also investigate the logical extensions of using a non-symmetric triangular density to approximate a lognormal density. Several issues associated with using a triangular density as a substitute for the normal and lognormal densities are discussed, and we explore the resulting numerical approximation errors for the normal case. Finally, we present several examples that highlight simple decision rules-of-thumb that the use of the approximation generates. Such rules-of-thumb, which are useful in risk and reliability analysis and general business analysis, can be difficult or impossible to extract without the use of approximations. These examples include uses of the approximation in generating random deviates, uses in mixture models for risk analysis, and an illustrative decision analysis problem. It is our belief that this exploratory look at the triangular approximation to the normal will provoke other practitioners to explore its possible use in various domains and applications

"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.

A Swarm Optimization approach for clinical knowledge mining.

Science.gov (United States)

Christopher, J Jabez; Nehemiah, H Khanna; Kannan, A

2015-10-01

Rule-based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems. The rules stored in the rule base have an impact on classification efficiency. Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta-heuristic approaches in order to improve the quality of the rule base. In this work, a meta-heuristic approach called Wind-driven Swarm Optimization (WSO) is used. The uniqueness of this work lies in the biological inspiration that underlies the algorithm. WSO uses Jval, a new metric, to evaluate the efficiency of a rule-based classifier. Rules are extracted from decision trees. WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data. The performance of various extensions of decision trees, namely, RIPPER, PART, FURIA and Decision Tables are analyzed. The efficiency of WSO is also compared with the traditional Particle Swarm Optimization. Experiments were carried out with six benchmark medical datasets. The traditional C4.5 algorithm yields 62.89% accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60% with 19 rules. For Heart disease dataset, C4.5 is 68.64% accurate with 98 rules where as WSO is 77.8% accurate with 34 rules. The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively. WSO provides accurate and concise rulesets. PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization. The trade-off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule-based clinical decision support system. The efficiency of a decision support system relies on the content of the rule base and classification accuracy. Copyright �

Clinical decision-making: predictors of patient participation in nursing care.

Science.gov (United States)

Florin, Jan; Ehrenberg, Anna; Ehnfors, Margareta

2008-11-01

To investigate predictors of patients' preferences for participation in clinical decision-making in inpatient nursing care. Patient participation in decision-making in nursing care is regarded as a prerequisite for good clinical practice regarding the person's autonomy and integrity. A cross-sectional survey of 428 persons, newly discharged from inpatient care. The survey was conducted using the Control Preference Scale. Multiple logistic regression analysis was used for testing the association of patient characteristics with preferences for participation. Patients, in general, preferred adopting a passive role. However, predictors for adopting an active participatory role were the patient's gender (odds ratio = 1.8), education (odds ratio = 2.2), living condition (odds ratio = 1.8) and occupational status (odds ratio = 2.0). A probability of 53% was estimated, which female senior citizens with at least a high school degree and who lived alone would prefer an active role in clinical decision-making. At the same time, a working cohabiting male with less than a high school degree had a probability of 8% for active participation in clinical decision making in nursing care. Patient preferences for participation differed considerably and are best elicited by assessment of the individual patient. Relevance to clinical practice. The nurses have a professional responsibility to act in such a way that patients can participate and make decisions according to their own values from an informed position. Access to knowledge of patients'basic assumptions and preferences for participation is of great value for nurses in the care process. There is a need for nurses to use structured methods and tools for eliciting individual patient preferences regarding participation in clinical decision-making.

A C++ Class for Rule-Base Objects

Directory of Open Access Journals (Sweden)

William J. Grenney

1992-01-01

Full Text Available A C++ class, called Tripod, was created as a tool to assist with the development of rule-base decision support systems. The Tripod class contains data structures for the rule-base and member functions for operating on the data. The rule-base is defined by three ASCII files. These files are translated by a preprocessor into a single file that is located when a rule-base object is instantiated. The Tripod class was tested as part of a proto-type decision support system (DSS for winter highway maintenance in the Intermountain West. The DSS is composed of two principal modules: the main program, called the wrapper, and a Tripod rule-base object. The wrapper is a procedural module that interfaces with remote sensors and an external meterological database. The rule-base contains the logic for advising an inexperienced user and for assisting with the decision making process.

A new intuitionistic fuzzy rule-based decision-making system for an operating system process scheduler.

Science.gov (United States)

Butt, Muhammad Arif; Akram, Muhammad

2016-01-01

We present a new intuitionistic fuzzy rule-based decision-making system based on intuitionistic fuzzy sets for a process scheduler of a batch operating system. Our proposed intuitionistic fuzzy scheduling algorithm, inputs the nice value and burst time of all available processes in the ready queue, intuitionistically fuzzify the input values, triggers appropriate rules of our intuitionistic fuzzy inference engine and finally calculates the dynamic priority (dp) of all the processes in the ready queue. Once the dp of every process is calculated the ready queue is sorted in decreasing order of dp of every process. The process with maximum dp value is sent to the central processing unit for execution. Finally, we show complete working of our algorithm on two different data sets and give comparisons with some standard non-preemptive process schedulers.

Online Rule Generation Software Process Model

OpenAIRE

Sudeep Marwaha; Alka Aroa; Satma M C; Rajni Jain; R C Goyal

2013-01-01

For production systems like expert systems, a rule generation software can facilitate the faster deployment. The software process model for rule generation using decision tree classifier refers to the various steps required to be executed for the development of a web based software model for decision rule generation. The Royce’s final waterfall model has been used in this paper to explain the software development process. The paper presents the specific output of various steps of modified wat...

Targeted training of the decision rule benefits rule-guided behavior in Parkinson's disease.

Science.gov (United States)

Ell, Shawn W

2013-12-01

The impact of Parkinson's disease (PD) on rule-guided behavior has received considerable attention in cognitive neuroscience. The majority of research has used PD as a model of dysfunction in frontostriatal networks, but very few attempts have been made to investigate the possibility of adapting common experimental techniques in an effort to identify the conditions that are most likely to facilitate successful performance. The present study investigated a targeted training paradigm designed to facilitate rule learning and application using rule-based categorization as a model task. Participants received targeted training in which there was no selective-attention demand (i.e., stimuli varied along a single, relevant dimension) or nontargeted training in which there was selective-attention demand (i.e., stimuli varied along a relevant dimension as well as an irrelevant dimension). Following training, all participants were tested on a rule-based task with selective-attention demand. During the test phase, PD patients who received targeted training performed similarly to control participants and outperformed patients who did not receive targeted training. As a preliminary test of the generalizability of the benefit of targeted training, a subset of the PD patients were tested on the Wisconsin card sorting task (WCST). PD patients who received targeted training outperformed PD patients who did not receive targeted training on several WCST performance measures. These data further characterize the contribution of frontostriatal circuitry to rule-guided behavior. Importantly, these data also suggest that PD patient impairment, on selective-attention-demanding tasks of rule-guided behavior, is not inevitable and highlight the potential benefit of targeted training.

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

[Clinical decision making and critical thinking in the nursing diagnostic process].

Science.gov (United States)

Müller-Staub, Maria

2006-10-01

The daily routine requires complex thinking processes of nurses, but clinical decision making and critical thinking are underestimated in nursing. A great demand for educational measures in clinical judgement related with the diagnostic process was found in nurses. The German literature hardly describes nursing diagnoses as clinical judgements about human reactions on health problems / life processes. Critical thinking is described as an intellectual, disciplined process of active conceptualisation, application and synthesis of information. It is gained through observation, experience, reflection and communication and leads thinking and action. Critical thinking influences the aspects of clinical decision making a) diagnostic judgement, b) therapeutic reasoning and c) ethical decision making. Human reactions are complex processes and in their course, human behavior is interpreted in the focus of health. Therefore, more attention should be given to the nursing diagnostic process. This article presents the theoretical framework of the paper "Clinical decision making: Fostering critical thinking in the nursing diagnostic process through case studies".

Surrogate decision making and intellectual virtue.

Science.gov (United States)

Bock, Gregory L

2014-01-01

Patients can be harmed by a religiously motivated surrogate decision maker whose decisions are contrary to the standard of care; therefore, surrogate decision making should be held to a high standard. Stewart Eskew and Christopher Meyers proposed a two-part rule for deciding which religiously based decisions to honor: (1) a secular reason condition and (2) a rationality condition. The second condition is based on a coherence theory of rationality, which they claim is accessible, generous, and culturally sensitive. In this article, I will propose strengthening the rationality condition by grounding it in a theory of intellectual virtue, which is both rigorous and culturally sensitive. Copyright 2014 The Journal of Clinical Ethics. All rights reserved.

Optimal offering and operating strategies for wind-storage systems with linear decision rules

DEFF Research Database (Denmark)

Ding, Huajie; Pinson, Pierre; Hu, Zechun

2016-01-01

The participation of wind farm-energy storage systems (WF-ESS) in electricity markets calls for an integrated view of day-ahead offering strategies and real-time operation policies. Such an integrated strategy is proposed here by co-optimizing offering at the day-ahead stage and operation policy...... to be used at the balancing stage. Linear decision rules are seen as a natural approach to model and optimize the real-time operation policy. These allow enhancing profits from balancing markets based on updated information on prices and wind power generation. Our integrated strategies for WF...

Clinical decision making in dermatology: observation of consultations and the patients' perspectives.

Science.gov (United States)

Hajjaj, F M; Salek, M S; Basra, M K A; Finlay, A Y

2010-01-01

Clinical decision making is a complex process and might be influenced by a wide range of clinical and non-clinical factors. Little is known about this process in dermatology. The aim of this study was to explore the different types of management decisions made in dermatology and to identify factors influencing those decisions from observation of consultations and interviews with the patients. 61 patient consultations were observed by a physician with experience in dermatology. The patients were interviewed immediately after each consultation. Consultations and interviews were audio recorded, transcribed and their content analysed using thematic content analysis. The most common management decisions made during the consultations included: follow-up, carrying out laboratory investigation, starting new topical treatment, renewal of systemic treatment, renewal of topical treatment, discharging patients and starting new systemic treatment. Common influences on those decisions included: clinical factors such as ineffectiveness of previous therapy, adherence to prescribing guidelines, side-effects of medications, previous experience with the treatment, deterioration or improvement in the skin condition, and chronicity of skin condition. Non-clinical factors included: patient's quality of life, patient's friends or relatives, patient's time commitment, travel or transportation difficulties, treatment-related costs, availability of consultant, and availability of treatment. The study has shown that patients are aware that management decisions in dermatology are influenced by a wide range of clinical and non-clinical factors. Education programmes should be developed to improve the quality of decision making. Copyright © 2010 S. Karger AG, Basel.

Clinical decision-making of rural novice nurses.

Science.gov (United States)

Seright, T J

2011-01-01

Nurses in rural settings are often the first to assess and interpret the patient's clinical presentations. Therefore, an understanding of how nurses experience decision-making is important in terms of educational preparation, resource allocation to rural areas, institutional cultures, and patient outcomes. Theory development was based on the in-depth investigation of 12 novice nurses practicing in rural critical access hospitals in a north central state. This grounded theory study consisted of face-to-face interviews with 12 registered nurses, nine of whom were observed during their work day. The participants were interviewed a second time, as a method of member checking, and during this interview they reviewed their transcripts, the emerging themes and categories. Directors of nursing from both the research sites and rural hospitals not involved in the study, experienced researchers, and nurse educators facilitated triangulation of the findings. 'Sociocentric rationalizing' emerged as the central phenomenon and referred to the sense of belonging and agency which impacted the decision-making in this small group of novice nurses in rural critical access hospitals. The observed consequences, which were conceptualized during the axial coding process and were derived from observations and interviews of the 12 novice nurses in this study include: (1) gathering information before making a decision included assessment of: the credibility of co-workers, patients' subjective and objective data, and one's own past and current experiences; (2) conferring with co-workers as a direct method of confirming/denying decisions being made was considered more realistic and expedient than policy books and decision trees; (3) rural practicum clinical experiences, along with support after orientation, provide for transition to the rural nurse role; (4) involved directors of nursing served as both models and protectors of novice nurses placed in high accountability positions early in

From data mining rules to medical logical modules and medical advices.

Science.gov (United States)

Gomoi, Valentin; Vida, Mihaela; Robu, Raul; Stoicu-Tivadar, Vasile; Bernad, Elena; Lupşe, Oana

2013-01-01

Using data mining in collaboration with Clinical Decision Support Systems adds new knowledge as support for medical diagnosis. The current work presents a tool which translates data mining rules supporting generation of medical advices to Arden Syntax formalism. The developed system was tested with data related to 2326 births that took place in 2010 at the Bega Obstetrics - Gynaecology Hospital, Timişoara. Based on processing these data, 14 medical rules regarding the Apgar score were generated and then translated in Arden Syntax language.

Validating a decision tree for serious infection: diagnostic accuracy in acutely ill children in ambulatory care.

Science.gov (United States)

Verbakel, Jan Y; Lemiengre, Marieke B; De Burghgraeve, Tine; De Sutter, An; Aertgeerts, Bert; Bullens, Dominique M A; Shinkins, Bethany; Van den Bruel, Ann; Buntinx, Frank

2015-08-07

Acute infection is the most common presentation of children in primary care with only few having a serious infection (eg, sepsis, meningitis, pneumonia). To avoid complications or death, early recognition and adequate referral are essential. Clinical prediction rules have the potential to improve diagnostic decision-making for rare but serious conditions. In this study, we aimed to validate a recently developed decision tree in a new but similar population. Diagnostic accuracy study validating a clinical prediction rule. Acutely ill children presenting to ambulatory care in Flanders, Belgium, consisting of general practice and paediatric assessment in outpatient clinics or the emergency department. Physicians were asked to score the decision tree in every child. The outcome of interest was hospital admission for at least 24 h with a serious infection within 5 days after initial presentation. We report the diagnostic accuracy of the decision tree in sensitivity, specificity, likelihood ratios and predictive values. In total, 8962 acute illness episodes were included, of which 283 lead to admission to hospital with a serious infection. Sensitivity of the decision tree was 100% (95% CI 71.5% to 100%) at a specificity of 83.6% (95% CI 82.3% to 84.9%) in the general practitioner setting with 17% of children testing positive. In the paediatric outpatient and emergency department setting, sensitivities were below 92%, with specificities below 44.8%. In an independent validation cohort, this clinical prediction rule has shown to be extremely sensitive to identify children at risk of hospital admission for a serious infection in general practice, making it suitable for ruling out. NCT02024282. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

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.

Optimization of decision rule complexity for decision tables with many-valued decisions

KAUST Repository

Azad, Mohammad; Chikalov, Igor; Moshkov, Mikhail

2013-01-01

compare our results with optimal result obtained by dynamic programming algorithms. The average percentage of relative difference between length (coverage) of constructed and optimal rules is at most 6.89% (15.89%, respectively) for leaders which seems

Judgment and decision making.

Science.gov (United States)

Mellers, B A; Schwartz, A; Cooke, A D

1998-01-01

For many decades, research in judgment and decision making has examined behavioral violations of rational choice theory. In that framework, rationality is expressed as a single correct decision shared by experimenters and subjects that satisfies internal coherence within a set of preferences and beliefs. Outside of psychology, social scientists are now debating the need to modify rational choice theory with behavioral assumptions. Within psychology, researchers are debating assumptions about errors for many different definitions of rationality. Alternative frameworks are being proposed. These frameworks view decisions as more reasonable and adaptive that previously thought. For example, "rule following." Rule following, which occurs when a rule or norm is applied to a situation, often minimizes effort and provides satisfying solutions that are "good enough," though not necessarily the best. When rules are ambiguous, people look for reasons to guide their decisions. They may also let their emotions take charge. This chapter presents recent research on judgment and decision making from traditional and alternative frameworks.

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

Development and evaluation of learning module on clinical decision-making in Prosthodontics.

Science.gov (United States)

Deshpande, Saee; Lambade, Dipti; Chahande, Jayashree

2015-01-01

Best practice strategies for helping students learn the reasoning skills of problem solving and critical thinking (CT) remain a source of conjecture, particularly with regard to CT. The dental education literature is fundamentally devoid of research on the cognitive components of clinical decision-making. This study was aimed to develop and evaluate the impact of blended learning module on clinical decision-making skills of dental graduates for planning prosthodontics rehabilitation. An interactive teaching module consisting of didactic lectures on clinical decision-making and a computer-assisted case-based treatment planning software was developed Its impact on cognitive knowledge gain in clinical decision-making was evaluated using an assessment involving problem-based multiple choice questions and paper-based case scenarios. Mean test scores were: Pretest (17 ± 1), posttest 1 (21 ± 2) and posttest 2 (43 ± 3). Comparison of mean scores was done with one-way ANOVA test. There was overall significant difference in between mean scores at all the three points (P posttest 1 > pretest. Blended teaching methods employing didactic lectures on the clinical decision-making as well as computer assisted case-based learning can be used to improve quality of clinical decision-making in prosthodontic rehabilitation for dental graduates.

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

Decision-theoretic planning of clinical patient management

OpenAIRE

Peek, Niels Bastiaan

2000-01-01

When a doctor is treating a patient, he is constantly facing decisions. From the externally visible signs and symptoms he must establish a hypothesis of what might be wrong with the patient; then he must decide whether additional diagnostic procedures are required to verify this hypothesis, whether therapeutic action is necessary, and which post-therapeutic trajectory is to be followed. All these bedside decisions are related to each other, and the whole task of clinical patient management ca...

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

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

Decision Analysis for Metric Selection on a Clinical Quality Scorecard.

Science.gov (United States)

Guth, Rebecca M; Storey, Patricia E; Vitale, Michael; Markan-Aurora, Sumita; Gordon, Randolph; Prevost, Traci Q; Dunagan, Wm Claiborne; Woeltje, Keith F

2016-09-01

Clinical quality scorecards are used by health care institutions to monitor clinical performance and drive quality improvement. Because of the rapid proliferation of quality metrics in health care, BJC HealthCare found it increasingly difficult to select the most impactful scorecard metrics while still monitoring metrics for regulatory purposes. A 7-step measure selection process was implemented incorporating Kepner-Tregoe Decision Analysis, which is a systematic process that considers key criteria that must be satisfied in order to make the best decision. The decision analysis process evaluates what metrics will most appropriately fulfill these criteria, as well as identifies potential risks associated with a particular metric in order to identify threats to its implementation. Using this process, a list of 750 potential metrics was narrowed to 25 that were selected for scorecard inclusion. This decision analysis process created a more transparent, reproducible approach for selecting quality metrics for clinical quality scorecards. © The Author(s) 2015.

Interaction rules underlying group decisions in homing pigeons

Science.gov (United States)

Pettit, Benjamin; Perna, Andrea; Biro, Dora; Sumpter, David J. T.

2013-01-01

Travelling in groups gives animals opportunities to share route information by following cues from each other's movement. The outcome of group navigation will depend on how individuals respond to each other within a flock, school, swarm or herd. Despite the abundance of modelling studies, only recently have researchers developed techniques to determine the interaction rules among real animals. Here, we use high-resolution GPS (global positioning system) tracking to study these interactions in pairs of pigeons flying home from a familiar site. Momentary changes in velocity indicate alignment with the neighbour's direction, as well as attraction or avoidance depending on distance. Responses were stronger when the neighbour was in front. From the flocking behaviour, we develop a model to predict features of group navigation. Specifically, we show that the interactions between pigeons stabilize a side-by-side configuration, promoting bidirectional information transfer and reducing the risk of separation. However, if one bird gets in front it will lead directional choices. Our model further predicts, and observations confirm, that a faster bird (as measured from solo flights) will fly slightly in front and thus dominate the choice of homing route. Our results explain how group decisions emerge from individual differences in homing flight behaviour. PMID:24068173

Sepsis and meningitis in hospitalized children: performance of clinical signs and their prediction rules in a case-control study.

Science.gov (United States)

Verbakel, Jan Y; MacFaul, Roderick; Aertgeerts, Bert; Buntinx, Frank; Thompson, Matthew

2014-06-01

Feverish illness is a common presentation to acute pediatric services. Clinical staff faces the challenge of differentiating the few children with meningitis or sepsis from the majority with self-limiting illness. We aimed to determine the diagnostic value of clinical features and their prediction rules (CPR) for identifying children with sepsis or meningitis among those children admitted to a District General Hospital with acute febrile illness. Acutely ill children admitted to a District General Hospital in England were included in this case-control study between 2000 and 2005. We examined the diagnostic accuracy of individual clinical signs and 6 CPRs, including the National Institute for Clinical Excellence "traffic light" system, to determine clinical utility in identifying children with a diagnosis of sepsis or meningitis. Loss of consciousness, prolonged capillary refill, decreased alertness, respiratory effort, and the physician's illness assessment had high positive likelihood ratios (9-114), although with wide confidence intervals, to rule in sepsis or meningitis. The National Institute for Clinical Excellence traffic light system, the modified Yale Observation Scale, and the Pediatric Advanced Warning Score performed poorly with positive likelihood ratios ranging from 1 to 3. The pediatrician's overall illness assessment was the most useful feature to rule in sepsis or meningitis in these hospitalized children. Clinical prediction rules did not effectively rule in sepsis or meningitis. The modified Yale Observation Scale should be used with caution. Single clinical signs could complement these scores to rule in sepsis or meningitis. Further research is needed to validate these CPRs.

Viewpoint: Decision-making in committees

OpenAIRE

Li Hao; Wing Suen

2009-01-01

This article reviews recent developments in the theory of committee decision-making. A committee consists of self-interested members who make a public decision by aggregating imperfect information dispersed among them according to a pre-specified decision rule. We focus on costly information acquisition, strategic information aggregation, and rules and processes that enhance the quality of the committee decision. Seeming inefficiencies of the committee decision-making process such as over-cau...

Phonological reduplication in sign language: rules rule

Directory of Open Access Journals (Sweden)

Iris eBerent

2014-06-01

Full Text Available Productivity—the hallmark of linguistic competence—is typically attributed to algebraic rules that support broad generalizations. Past research on spoken language has documented such generalizations in both adults and infants. But whether algebraic rules form part of the linguistic competence of signers remains unknown. To address this question, here we gauge the generalization afforded by American Sign Language (ASL. As a case study, we examine reduplication (X→XX—a rule that, inter alia, generates ASL nouns from verbs. If signers encode this rule, then they should freely extend it to novel syllables, including ones with features that are unattested in ASL. And since reduplicated disyllables are preferred in ASL, such rule should favor novel reduplicated signs. Novel reduplicated signs should thus be preferred to nonreduplicative controls (in rating, and consequently, such stimuli should also be harder to classify as nonsigns (in the lexical decision task. The results of four experiments support this prediction. These findings suggest that the phonological knowledge of signers includes powerful algebraic rules. The convergence between these conclusions and previous evidence for phonological rules in spoken language suggests that the architecture of the phonological mind is partly amodal.

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.

A clinical data warehouse-based process for refining medication orders alerts.

Science.gov (United States)

Boussadi, Abdelali; Caruba, Thibaut; Zapletal, Eric; Sabatier, Brigitte; Durieux, Pierre; Degoulet, Patrice

2012-01-01

The objective of this case report is to evaluate the use of a clinical data warehouse coupled with a clinical information system to test and refine alerts for medication orders control before they were fully implemented. A clinical decision rule refinement process was used to assess alerts. The criteria assessed were the frequencies of alerts for initial prescriptions of 10 medications whose dosage levels depend on renal function thresholds. In the first iteration of the process, the frequency of the 'exceeds maximum daily dose' alerts was 7.10% (617/8692), while that of the 'under dose' alerts was 3.14% (273/8692). Indicators were presented to the experts. During the different iterations of the process, 45 (16.07%) decision rules were removed, 105 (37.5%) were changed and 136 new rules were introduced. Extensive retrospective analysis of physicians' medication orders stored in a clinical data warehouse facilitates alert optimization toward the goal of maximizing the safety of the patient and minimizing overridden alerts.

Effects of reflection on clinical decision-making of intensive care unit nurses.

Science.gov (United States)

Razieh, Shahrokhi; Somayeh, Ghafari; Fariba, Haghani

2018-07-01

Nurses are one of the most influential factors in overcoming the main challenges faced by health systems throughout the world. Every health system should, hence, empower nurses in clinical judgment and decision-making skills. This study evaluated the effects of implementing Tanner's reflection method on clinical decision-making of nurses working in an intensive care unit (ICU). This study used an experimental, pretest, posttest design. The setting was the intensive care unit of Amin Hospital Isfahan, Iran. The convenience sample included 60 nurses working in the ICU of Amin Hospital (Isfahan, Iran). This clinical trial was performed on 60 nurses working in the ICU of Amin Hospital (Isfahan, Iran). The nurses were selected by census sampling and randomly allocated to either the case or the control group. Data were collected using a questionnaire containing demographic characteristics and the clinical decision-making scale developed by Laurie and Salantera (NDMI-14). The questionnaire was completed before and one week after the intervention. The data were analyzed using SPSS 21.0. The two groups were not significantly different in terms of the level and mean scores of clinical decision-making before the intervention (P = 0.786). Based on the results of independent t-test, the mean score of clinical decision-making one week after the intervention was significantly higher in the case group than in the control group (P = 0.009; t = -2.69). The results of Mann Whitney test showed that one week after the intervention, the nurses' level of clinical decision-making in the case group rose to the next level (P = 0.001). Reflection could improve the clinical decision-making of ICU nurses. It is, thus, recommended to incorporate this method into the nursing curriculum and care practices. Copyright © 2018. Published by Elsevier Ltd.

Risk perception and clinical decision making in primary care

DEFF Research Database (Denmark)

Barfoed, Benedicte Marie Lind

2015-01-01

Objectives We aim to present new knowledge about different perspectives of health care professionals’ risk perceptions and clinical decision making. Furthermore, we intend to discuss differences between professional and personal risk perceptions and the impact on decisions in terms of both short...... and long-term outcomes. Background Insight into healthcare professionals’ perception of risk is a cornerstone for understanding their strategies for practising preventive care. The way people perceive risk can be seen as part of a general personality trait influenced by a mixture of individual...... considerations and the specific context. Most research has been focused on understanding of the concepts of risk. However healthcare professionals’ risk perception and personal attitudes also affect their clinical decision-making and risk communication. The differences between health care professionals’ personal...

Forms of Knowledge Incorporated in Clinical Decision-making among Newly-Graduated Nurses: A Metasynthesis

DEFF Research Database (Denmark)

Voldbjerg, Siri; Elgaard Sørensen, Erik; Grønkjær, Mette

2014-01-01

Clinical-decision-making is of decisive importance to how evidence-based practice is put into practice. Schools of Nursing have a responsibility to teach and train nursing students to make clinical decisions within a frame of evidence-based practice. Clinical decision-making among nurses has been...... explored from numerous angles using a diversity of methodologies. Existing research has mainly focused on promoting and inhibiting factors for implementation of evidence-based practice and incorporation of research evidence in the clinical-decision. Little attention has been given to the nurses' behavior......, including the knowledge that actually informs the newly graduated nurses’ clinical decision. The aim of the study is to combine and synthesize results from qualitative research. Noblit and Hare’s meta-ethnographic approach is used to conduct a metasynthesis of qualitative research that has studied...

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.

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.

Knowledge Translation of the PERC Rule for Suspected Pulmonary Embolism: A Blueprint for Reducing the Number of CT Pulmonary Angiograms.

Science.gov (United States)

Drescher, Michael J; Fried, Jeremy; Brass, Ryan; Medoro, Amanda; Murphy, Timothy; Delgado, João

2017-10-01

Computerized decision support decreases the number of computed tomography pulmonary angiograms (CTPA) for pulmonary embolism (PE) ordered in emergency departments, but it is not always well accepted by emergency physicians. We studied a department-endorsed, evidence-based clinical protocol that included the PE rule-out criteria (PERC) rule, multi-modal education using principles of knowledge translation (KT), and clinical decision support embedded in our order entry system, to decrease the number of unnecessary CTPA ordered. We performed a historically controlled observational before-after study for one year pre- and post-implementation of a departmentally-endorsed protocol. We included patients > 18 in whom providers suspected PE and who did not have a contraindication to CTPA. Providers entered clinical information into a diagnostic pathway via computerized order entry. Prior to protocol implementation, we provided education to ordering providers. The primary outcome measure was the number of CTPA ordered per 1,000 visits one year before vs. after implementation. CTPA declined from 1,033 scans for 98,028 annual visits (10.53 per 1,000 patient visits (95% CI [9.9-11.2]) to 892 scans for 101,172 annual visits (8.81 per 1,000 patient visits (95% CI [8.3-9.4]) pPatient characteristics were similar for both periods. Knowledge translation clinical decision support using the PERC rule significantly reduced the number of CTPA ordered.

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...

The role of emotions in clinical reasoning and decision making.

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Marcum, James A

2013-10-01

What role, if any, should emotions play in clinical reasoning and decision making? Traditionally, emotions have been excluded from clinical reasoning and decision making, but with recent advances in cognitive neuropsychology they are now considered an important component of them. Today, cognition is thought to be a set of complex processes relying on multiple types of intelligences. The role of mathematical logic (hypothetico-deductive thinking) or verbal linguistic intelligence in cognition, for example, is well documented and accepted; however, the role of emotional intelligence has received less attention-especially because its nature and function are not well understood. In this paper, I argue for the inclusion of emotions in clinical reasoning and decision making. To that end, developments in contemporary cognitive neuropsychology are initially examined and analyzed, followed by a review of the medical literature discussing the role of emotions in clinical practice. Next, a published clinical case is reconstructed and used to illustrate the recognition and regulation of emotions played during a series of clinical consultations, which resulted in a positive medical outcome. The paper's main thesis is that emotions, particularly in terms of emotional intelligence as a practical form of intelligence, afford clinical practitioners a robust cognitive resource for providing quality medical care.

Clinical trial or standard treatment? Shared decision making at the department of oncology

DEFF Research Database (Denmark)

Gregersen, Trine Ammentorp; Birkelund, Regner; Ammentorp, Jette

2016-01-01

Title: Clinical trial or standard treatment? Shared decision making at the department of oncology. Authors: Ph.d. student, Trine A. Gregersen. Trine.gregersen@rsyd.dk. Department of Oncology. Health Services Research Unit Lillebaelt Hospital / IRS University of Southern Denmark. Professor, Regner...... are involved in difficult treatment decisions including participation in clinical trials. The literature indicates that the decision is very often based on little knowledge about the treatment and that many patients who have consented to participate in a clinical trial are not always aware...... that they are participating in a trial. This place great demand on the healthcare providers’ ability to involve and advise patients in the decisions. The aim of this study is to investigate the characteristics of the communication when decisions about participation in clinical oncology trial are made and the patients...

Ethically-based clinical decision-making in physical therapy: process and issues.

Science.gov (United States)

Finch, Elspeth; Geddes, E Lynne; Larin, Hélène

2005-01-01

The identification and consideration of relevant ethical issues in clinical decision-making, and the education of health care professionals (HCPs) in these skills are key factors in providing quality health care. This qualitative study explores the way in which physical therapists (PTs) integrate ethical issues into clinical practice decisions and identifies ethical themes used by PTs. A purposive sample of eight PTs was asked to describe a recent ethically-based clinical decision. Transcribed interviews were coded and themes identified related to the following categories: 1) the integration of ethical issues in the clinical decision-making process, 2) patient welfare, 3) professional ethos of the PT, and 4) health care economics and business practices. Participants readily described clinical situations involving ethical issues but rarely identified specific conflicting ethical issues in their description. Ethical dilemmas were more frequently resolved when there were fewer emotional sequelae associated with the dilemma, and the PT had a clear understanding of professional ethos, valued patient autonomy, and explored a variety of alternative actions before implementing one. HCP students need to develop a clear professional ethos and an increased understanding of the economic factors that will present ethical issues in practice.

Geriatric Fever Score: a new decision rule for geriatric care.

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Min-Hsien Chung

Full Text Available Evaluating geriatric patients with fever is time-consuming and challenging. We investigated independent mortality predictors of geriatric patients with fever and developed a prediction rule for emergency care, critical care, and geriatric care physicians to classify patients into mortality risk and disposition groups.Consecutive geriatric patients (≥65 years old visiting the emergency department (ED of a university-affiliated medical center between June 1 and July 21, 2010, were enrolled when they met the criteria of fever: a tympanic temperature ≥37.2°C or a baseline temperature elevated ≥1.3°C. Thirty-day mortality was the primary endpoint. Internal validation with bootstrap re-sampling was done.Three hundred thirty geriatric patients were enrolled. We found three independent mortality predictors: Leukocytosis (WBC >12,000 cells/mm3, Severe coma (GCS ≤ 8, and Thrombocytopenia (platelets <150 10(3/mm3 (LST. After assigning weights to each predictor, we developed a Geriatric Fever Score that stratifies patients into two mortality-risk and disposition groups: low (4.0% (95% CI: 2.3-6.9%: a general ward or treatment in the ED then discharge and high (30.3% (95% CI: 17.4-47.3%: consider the intensive care unit. The area under the curve for the rule was 0.73.We found that the Geriatric Fever Score is a simple and rapid rule for predicting 30-day mortality and classifying mortality risk and disposition in geriatric patients with fever, although external validation should be performed to confirm its usefulness in other clinical settings. It might help preserve medical resources for patients in greater need.

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

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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.

A predictive approach to selecting the size of a clinical trial, based on subjective clinical opinion.

Science.gov (United States)

Spiegelhalter, D J; Freedman, L S

1986-01-01

The 'textbook' approach to determining sample size in a clinical trial has some fundamental weaknesses which we discuss. We describe a new predictive method which takes account of prior clinical opinion about the treatment difference. The method adopts the point of clinical equivalence (determined by interviewing the clinical participants) as the null hypothesis. Decision rules at the end of the study are based on whether the interval estimate of the treatment difference (classical or Bayesian) includes the null hypothesis. The prior distribution is used to predict the probabilities of making the decisions to use one or other treatment or to reserve final judgement. It is recommended that sample size be chosen to control the predicted probability of the last of these decisions. An example is given from a multi-centre trial of superficial bladder cancer.

Estimating the re-identification risk of clinical data sets

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Dankar Fida

2012-07-01

Full Text Available Abstract Background De-identification is a common way to protect patient privacy when disclosing clinical data for secondary purposes, such as research. One type of attack that de-identification protects against is linking the disclosed patient data with public and semi-public registries. Uniqueness is a commonly used measure of re-identification risk under this attack. If uniqueness can be measured accurately then the risk from this kind of attack can be managed. In practice, it is often not possible to measure uniqueness directly, therefore it must be estimated. Methods We evaluated the accuracy of uniqueness estimators on clinically relevant data sets. Four candidate estimators were identified because they were evaluated in the past and found to have good accuracy or because they were new and not evaluated comparatively before: the Zayatz estimator, slide negative binomial estimator, Pitman’s estimator, and mu-argus. A Monte Carlo simulation was performed to evaluate the uniqueness estimators on six clinically relevant data sets. We varied the sampling fraction and the uniqueness in the population (the value being estimated. The median relative error and inter-quartile range of the uniqueness estimates was measured across 1000 runs. Results There was no single estimator that performed well across all of the conditions. We developed a decision rule which selected between the Pitman, slide negative binomial and Zayatz estimators depending on the sampling fraction and the difference between estimates. This decision rule had the best consistent median relative error across multiple conditions and data sets. Conclusion This study identified an accurate decision rule that can be used by health privacy researchers and disclosure control professionals to estimate uniqueness in clinical data sets. The decision rule provides a reliable way to measure re-identification risk.

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.

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...

Rationale, design, and implementation protocol of an electronic health record integrated clinical prediction rule (iCPR randomized trial in primary care

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Wisnivesky Juan

2011-09-01

Full Text Available Abstract Background Clinical prediction rules (CPRs represent well-validated but underutilized evidence-based medicine tools at the point-of-care. To date, an inability to integrate these rules into an electronic health record (EHR has been a major limitation and we are not aware of a study demonstrating the use of CPR's in an ambulatory EHR setting. The integrated clinical prediction rule (iCPR trial integrates two CPR's in an EHR and assesses both the usability and the effect on evidence-based practice in the primary care setting. Methods A multi-disciplinary design team was assembled to develop a prototype iCPR for validated streptococcal pharyngitis and bacterial pneumonia CPRs. The iCPR tool was built as an active Clinical Decision Support (CDS tool that can be triggered by user action during typical workflow. Using the EHR CDS toolkit, the iCPR risk score calculator was linked to tailored ordered sets, documentation, and patient instructions. The team subsequently conducted two levels of 'real world' usability testing with eight providers per group. Usability data were used to refine and create a production tool. Participating primary care providers (n = 149 were randomized and intervention providers were trained in the use of the new iCPR tool. Rates of iCPR tool triggering in the intervention and control (simulated groups are monitored and subsequent use of the various components of the iCPR tool among intervention encounters is also tracked. The primary outcome is the difference in antibiotic prescribing rates (strep and pneumonia iCPR's encounters and chest x-rays (pneumonia iCPR only between intervention and control providers. Discussion Using iterative usability testing and development paired with provider training, the iCPR CDS tool leverages user-centered design principles to overcome pervasive underutilization of EBM and support evidence-based practice at the point-of-care. The ongoing trial will determine if this collaborative

Are patient decision aids the best way to improve clinical decision making? Report of the IPDAS Symposium.

Science.gov (United States)

Holmes-Rovner, Margaret; Nelson, Wendy L; Pignone, Michael; Elwyn, Glyn; Rovner, David R; O'Connor, Annette M; Coulter, Angela; Correa-de-Araujo, Rosaly

2007-01-01

This article reports on the International Patient Decision Aid Standards Symposium held in 2006 at the annual meeting of the Society for Medical Decision Making in Cambridge, Massachusetts. The symposium featured a debate regarding the proposition that "decision aids are the best way to improve clinical decision making.'' The formal debate addressed the theoretical problem of the appropriate gold standard for an improved decision, efficacy of decision aids, and prospects for implementation. Audience comments and questions focused on both theory and practice: the often unacknowledged roots of decision aids in expected utility theory and the practical problems of limited patient decision aid implementation in health care. The participants' vote on the proposition was approximately half for and half against.

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

Postnatal Psychosocial Assessment and Clinical Decision-Making, a Descriptive Study.

Science.gov (United States)

Sims, Deborah; Fowler, Cathrine

2018-05-18

The aim of this study is to describe experienced child and family health nurses' clinical decision-making during a postnatal psychosocial assessment. Maternal emotional wellbeing in the postnatal year optimises parenting and promotes infant development. Psychosocial assessment potentially enables early intervention and reduces the risk of a mental disorder occurring during this time of change. Assessment accuracy, and the interventions used are determined by the standard of nursing decision-making. A qualitative methodology was employed to explore decision-making behaviour when conducting a postnatal psychosocial assessment. This study was conducted in an Australian early parenting organisation. Twelve experienced child and family health nurses were interviewed. A detailed description of a postnatal psychosocial assessment process was obtained using a critical incident technique. Template analysis was used to determine the information domains the nurses accessed, and content analysis was used to determine the nurses' thinking strategies, to make clinical decisions from this assessment. The nurses described 24 domains of information and used 17 thinking strategies, in a variety of combinations. The four information domains most commonly used were parenting, assessment tools, women-determined issues and sleep. The seven thinking strategies most commonly used were searching for information, forming relationships between the information, recognising a pattern, drawing a conclusion, setting priorities, providing explanations for the information and judging the value of the information. The variety and complexity of the clinical decision-making involved in postnatal psychosocial assessment confirms that the nurses use information appropriately and within their scope of nursing practice. The standard of clinical decision-making determines the results of the assessment and the optimal access to care. Knowledge of the information domains and the decision-making strategies

Decision fusion recognition based on modified evidence rule

Institute of Scientific and Technical Information of China (English)

黎湘; 刘永祥; 付耀文; 庄钊文

2001-01-01

A modified evidence combination rule with a combination parameter λ is proposed to solve some problems in D-S theory by considering the correlation and complement among the evidences as well as the size and intersection of subsets in evidence. It can get reasonable results even the evidences are conflicting. Applying this rule to the real infrared/millimetre wave fusion system, a satisfactory result has been obtained.

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

39 CFR 3001.39 - Intermediate decisions.

Science.gov (United States)

2010-07-01

... 39 Postal Service 1 2010-07-01 2010-07-01 false Intermediate decisions. 3001.39 Section 3001.39 Postal Service POSTAL REGULATORY COMMISSION PERSONNEL RULES OF PRACTICE AND PROCEDURE Rules of General Applicability § 3001.39 Intermediate decisions. (a) Initial decision by presiding officer. In any proceedings in...

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.

[Cancer screening in clinical practice: the value of shared decision-making].

Science.gov (United States)

Cornuz, Jacques; Junod, Noëlle; Pasche, Olivier; Guessous, Idris

2010-07-14

Shared decision-making approach to uncertain clinical situations such as cancer screening seems more appropriate than ever. Shared decision making can be defined as an interactive process where physician and patient share all the stages of the decision making process. For patients who wish to be implicated in the management of their health conditions, physicians might express difficulty to do so. Use of patient decision aids appears to improve such process of shared decision making.

Optimization of inhibitory decision rules relative to length and coverage

KAUST Repository

Alsolami, Fawaz; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2012-01-01

The paper is devoted to the study of algorithms for optimization of inhibitory rules relative to the length and coverage. In contrast with usual rules that have on the right-hand side a relation "attribute ≠ value", inhibitory rules have a relation

Action Rules Mining

CERN Document Server

Dardzinska, Agnieszka

2013-01-01

We are surrounded by data, numerical, categorical and otherwise, which must to be analyzed and processed to convert it into information that instructs, answers or aids understanding and decision making. Data analysts in many disciplines such as business, education or medicine, are frequently asked to analyze new data sets which are often composed of numerous tables possessing different properties. They try to find completely new correlations between attributes and show new possibilities for users.   Action rules mining discusses some of data mining and knowledge discovery principles and then describe representative concepts, methods and algorithms connected with action. The author introduces the formal definition of action rule, notion of a simple association action rule and a representative action rule, the cost of association action rule, and gives a strategy how to construct simple association action rules of a lowest cost. A new approach for generating action rules from datasets with numerical attributes...

ForEx++: A New Framework for Knowledge Discovery from Decision Forests

Directory of Open Access Journals (Sweden)

Md Nasim Adnan

2017-11-01

Full Text Available Decision trees are popularly used in a wide range of real world problems for both prediction and classification (logic rules discovery. A decision forest is an ensemble of decision trees and it is often built for achieving better predictive performance compared to a single decision tree. Besides improving predictive performance, a decision forest can be seen as a pool of logic rules (rules with great potential for knowledge discovery. However, a standard-sized decision forest usually generates a large number of rules that a user may not able to manage for effective knowledge analysis. In this paper, we propose a new, data set independent framework for extracting those rules that are comparatively more accurate, generalized and concise than others. We apply the proposed framework on rules generated by two different decision forest algorithms from some publicly available medical related data sets on dementia and heart disease. We then compare the quality of rules extracted by the proposed framework with rules generated from a single J48 decision tree and rules extracted by another recent method. The results reported in this paper demonstrate the effectiveness of the proposed framework.

Learning a New Selection Rule in Visual and Frontal Cortex

NARCIS (Netherlands)

van der Togt, Chris; Stănişor, Liviu; Pooresmaeili, Arezoo; Albantakis, Larissa; Deco, Gustavo; Roelfsema, Pieter R

2016-01-01

How do you make a decision if you do not know the rules of the game? Models of sensory decision-making suggest that choices are slow if evidence is weak, but they may only apply if the subject knows the task rules. Here, we asked how the learning of a new rule influences neuronal activity in the

Non-ad-hoc decision rule for the Dempster-Shafer method of evidential reasoning

Science.gov (United States)

Cheaito, Ali; Lecours, Michael; Bosse, Eloi

1998-03-01

This paper is concerned with the fusion of identity information through the use of statistical analysis rooted in Dempster-Shafer theory of evidence to provide automatic identification aboard a platform. An identity information process for a baseline Multi-Source Data Fusion (MSDF) system is defined. The MSDF system is applied to information sources which include a number of radars, IFF systems, an ESM system, and a remote track source. We use a comprehensive Platform Data Base (PDB) containing all the possible identity values that the potential target may take, and we use the fuzzy logic strategies which enable the fusion of subjective attribute information from sensor and the PDB to make the derivation of target identity more quickly, more precisely, and with statistically quantifiable measures of confidence. The conventional Dempster-Shafer lacks a formal basis upon which decision can be made in the face of ambiguity. We define a non-ad hoc decision rule based on the expected utility interval for pruning the `unessential' propositions which would otherwise overload the real-time data fusion systems. An example has been selected to demonstrate the implementation of our modified Dempster-Shafer method of evidential reasoning.

Newly graduated nurses' use of knowledge sources in clinical decision-making

DEFF Research Database (Denmark)

Voldbjerg, Siri Lygum; Grønkjaer, Mette; Wiechula, Rick

2017-01-01

AIMS AND OBJECTIVES: To explore which knowledge sources newly graduated nurses' use in clinical decision-making and why and how they are used. BACKGROUND: In spite of an increased educational focus on skills and competencies within evidence based practice newly graduated nurses' ability to use...... approaches to strengthen the knowledgebase used in clinical decision-making. DESIGN AND METHODS: Ethnographic study using participant-observation and individual semi-structured interviews of nine Danish newly graduated nurses in medical and surgical hospital settings. RESULTS: Newly graduates use...... in clinical decision-making. If newly graduates are to be supported in an articulate and reflective use of a variety of sources, they have to be allocated to experienced nurses who model a reflective, articulate and balanced use of knowledge sources. This article is protected by copyright. All rights reserved....

Pooled individual patient data from five countries were used to derive a clinical prediction rule for coronary artery disease in primary care.

Science.gov (United States)

Aerts, Marc; Minalu, Girma; Bösner, Stefan; Buntinx, Frank; Burnand, Bernard; Haasenritter, Jörg; Herzig, Lilli; Knottnerus, J André; Nilsson, Staffan; Renier, Walter; Sox, Carol; Sox, Harold; Donner-Banzhoff, Norbert

2017-01-01

To construct a clinical prediction rule for coronary artery disease (CAD) presenting with chest pain in primary care. Meta-Analysis using 3,099 patients from five studies. To identify candidate predictors, we used random forest trees, multiple imputation of missing values, and logistic regression within individual studies. To generate a prediction rule on the pooled data, we applied a regression model that took account of the differing standard data sets collected by the five studies. The most parsimonious rule included six equally weighted predictors: age ≥55 (males) or ≥65 (females) (+1); attending physician suspected a serious diagnosis (+1); history of CAD (+1); pain brought on by exertion (+1); pain feels like "pressure" (+1); pain reproducible by palpation (-1). CAD was considered absent if the prediction score is data sets based on electronic health records from diverse sites create opportunities for improving their internal and external validity. Our patient-level meta-analysis from five primary care sites should improve external validity. Our strategy for addressing site-to-site systematic variation in missing data should improve internal validity. Using principles derived from decision theory, we also discuss the problem of setting the cutoff prediction score for taking action. Copyright © 2016 Elsevier Inc. All rights reserved.

The thinking doctor: clinical decision making in contemporary medicine.

Science.gov (United States)

Trimble, Michael; Hamilton, Paul

2016-08-01

Diagnostic errors are responsible for a significant number of adverse events. Logical reasoning and good decision-making skills are key factors in reducing such errors, but little emphasis has traditionally been placed on how these thought processes occur, and how errors could be minimised. In this article, we explore key cognitive ideas that underpin clinical decision making and suggest that by employing some simple strategies, physicians might be better able to understand how they make decisions and how the process might be optimised. © 2016 Royal College of Physicians.

Experiential and rational decision making: a survey to determine how emergency physicians make clinical decisions.

Science.gov (United States)

Calder, Lisa A; Forster, Alan J; Stiell, Ian G; Carr, Laura K; Brehaut, Jamie C; Perry, Jeffrey J; Vaillancourt, Christian; Croskerry, Patrick

2012-10-01

Dual-process psychological theories argue that clinical decision making is achieved through a combination of experiential (fast and intuitive) and rational (slower and systematic) cognitive processes. To determine whether emergency physicians perceived their clinical decisions in general to be more experiential or rational and how this compared with other physicians. A validated psychometric tool, the Rational Experiential Inventory (REI-40), was sent through postal mail to all emergency physicians registered with the College of Physicians and Surgeons of Ontario, according to their website in November 2009. Forty statements were ranked on a Likert scale from 1 (Definitely False) to 5 (Definitely True). An initial survey was sent out, followed by reminder cards and a second survey to non-respondents. Analysis included descriptive statistics, Student t tests, analysis of variance and comparison of mean scores with those of cardiologists from New Zealand. The response rate in this study was 46.9% (434/925). The respondents' median age was 41-50 years; they were mostly men (72.6%) and most had more than 10 years of clinical experience (66.8%). The mean REI-40 rational scores were higher than the experiential scores (3.93/5 (SD 0.35) vs 3.33/5 (SD 0.49), prational 3.93/5, mean experiential 3.05/5). The mean experiential scores were significantly higher for female respondents than for male respondents (3.40/5 (SD 0.49) vs 3.30/5 (SD 0.48), p=0.003). Overall, emergency physicians favoured rational decision making rather than experiential decision making; however, female emergency physicians had higher experiential scores than male emergency physicians. This has important implications for future knowledge translation and decision support efforts among emergency physicians.

The participative construction of the criminal decision in democratic states ruled by the law: the guaranty of participation of the parties, through confrontation, in the composition of a fair and legitimate decision

Directory of Open Access Journals (Sweden)

Flávio da Silva Andrade

2017-10-01

Full Text Available This article concerns a topic that is not new, but it remains current:  the participatory construction of the criminal decision in a democratic State ruled by law. Starting from the concepts of Rule of Law, of Guarantism and of Democracy, it seeks to renew the importance of the equal and dialectical participation of the parties, through the adversarial system, for the composition of a fair and legitimate criminal judicial decision. It is argued, from this perspective, that the parties should take the role of protagonists in the procedural scenario, since the decision should be built in a participatory way, i.e., based on the arguments and evidence presented, thus reducing the gaps that favor judicial discretion and decisionism. It is proposed, therefore, that the solution to the concrete case (acceptance or dismissal of the information or indictment, grant or rejection of a criminal precautionary measure, conviction or acquittal should be elaborated with support on the contribution of the litigants, from the contrast of their arguments and of the evidence produced, in adversarial proceedings, in the regular course of the process.

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

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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).

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

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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.

When to trust our learners? Clinical teachers' perceptions of decision variables in the entrustment process.

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Duijn, Chantal C M A; Welink, Lisanne S; Bok, Harold G J; Ten Cate, Olle T J

2018-06-01

Clinical training programs increasingly use entrustable professional activities (EPAs) as focus of assessment. However, questions remain about which information should ground decisions to trust learners. This qualitative study aimed to identify decision variables in the workplace that clinical teachers find relevant in the elaboration of the entrustment decision processes. The findings can substantiate entrustment decision-making in the clinical workplace. Focus groups were conducted with medical and veterinary clinical teachers, using the structured consensus method of the Nominal Group Technique to generate decision variables. A ranking was made based on a relevance score assigned by the clinical teachers to the different decision variables. Field notes, audio recordings and flip chart lists were analyzed and subsequently translated and, as a form of axial coding, merged into one list, combining the decision variables that were similar in their meaning. A list of 11 and 17 decision variables were acknowledged as relevant by the medical and veterinary teacher groups, respectively. The focus groups yielded 21 unique decision variables that were considered relevant to inform readiness to perform a clinical task on a designated level of supervision. The decision variables consisted of skills, generic qualities, characteristics, previous performance or other information. We were able to group the decision variables into five categories: ability, humility, integrity, reliability and adequate exposure. To entrust a learner to perform a task at a specific level of supervision, a supervisor needs information to support such a judgement. This trust cannot be credited on a single case at a single moment of assessment, but requires different variables and multiple sources of information. This study provides an overview of decision variables giving evidence to justify the multifactorial process of making an entrustment decision.

Science and intuition: do both have a place in clinical decision making?

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Pearson, Helen

Intuition is widely used in clinical decision making yet its use is underestimated compared to scientific decision-making methods. Information processing is used within scientific decision making and is methodical and analytical, whereas intuition relies more on a practitioner's perception. Intuition is an unconscious process and may be referred to as a 'sixth sense', 'hunch' or 'gut feeling'. It is not underpinned by valid and reliable measures. Expert health professionals use a rapid, automatic process to recognise familiar problems instantly. Intuition could therefore involve pattern recognition, where experts draw on experiences, so could be perceived as a cognitive skill rather than a perception or knowing without knowing how. The NHS places great importance on evidence-based practice but intuition is seemingly becoming an acceptable way of thinking and knowing in clinical decision making. Recognising nursing as an art allows intuition to be used and the environment or situation to be interpreted to help inform decision making. Intuition can be used in conjunction with evidence-based practice and to achieve good outcomes and deserves to be acknowledged within clinical practice.

Clinical classification in low back pain: best-evidence diagnostic rules based on systematic reviews.

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Petersen, Tom; Laslett, Mark; Juhl, Carsten

2017-05-12

Clinical examination findings are used in primary care to give an initial diagnosis to patients with low back pain and related leg symptoms. The purpose of this study was to develop best evidence Clinical Diagnostic Rules (CDR] for the identification of the most common patho-anatomical disorders in the lumbar spine; i.e. intervertebral discs, sacroiliac joints, facet joints, bone, muscles, nerve roots, muscles, peripheral nerve tissue, and central nervous system sensitization. A sensitive electronic search strategy using MEDLINE, EMBASE and CINAHL databases was combined with hand searching and citation tracking to identify eligible studies. Criteria for inclusion were: persons with low back pain with or without related leg symptoms, history or physical examination findings suitable for use in primary care, comparison with acceptable reference standards, and statistical reporting permitting calculation of diagnostic value. Quality assessments were made independently by two reviewers using the Quality Assessment of Diagnostic Accuracy Studies tool. Clinical examination findings that were investigated by at least two studies were included and results that met our predefined threshold of positive likelihood ratio ≥ 2 or negative likelihood ratio ≤ 0.5 were considered for the CDR. Sixty-four studies satisfied our eligible criteria. We were able to construct promising CDRs for symptomatic intervertebral disc, sacroiliac joint, spondylolisthesis, disc herniation with nerve root involvement, and spinal stenosis. Single clinical test appear not to be as useful as clusters of tests that are more closely in line with clinical decision making. This is the first comprehensive systematic review of diagnostic accuracy studies that evaluate clinical examination findings for their ability to identify the most common patho-anatomical disorders in the lumbar spine. In some diagnostic categories we have sufficient evidence to recommend a CDR. In others, we have only

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

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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

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

How to Assist Formalization of NL Regulations: Lessons from Business Rules Acquisition Experiments

OpenAIRE

Nazarenko , Adeline

2013-01-01

International audience; Decision systems usually rely on a set of business rules that describe the expected behavior of a system or an organization and that determine the decisions to be taken in different situations. However, rule acquisition is often the bottleneck that hinders the development of decision systems. When these rules are based on regulations written in Natural Language (NL), one solution is to derive formal business rules from the source documents. This approach also allows ch...

The Utility of the Frailty Index in Clinical Decision Making.

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Khatry, K; Peel, N M; Gray, L C; Hubbard, R E

2018-01-01

Using clinical vignettes, this study aimed to determine if a measure of patient frailty would impact management decisions made by geriatricians regarding commonly encountered clinical situations. Electronic surveys consisting of three vignettes derived from cases commonly seen in an acute inpatient ward were distributed to geriatricians. Vignettes included patients being considered for intensive care treatment, rehabilitation, or coronary artery bypass surgery. A frailty index was generated through Comprehensive electronic Geriatric Assessment. For each vignette, respondents were asked to make a recommendation for management, based on either a brief or detailed amount of clinical information and to reconsider their decision after the addition of the frailty index. The study suggests that quantification of frailty might aid the clinical judgment now employed daily to proceed with usual care, or to modify it based on the vulnerability of the person to whom it is aimed.

Optimization of inhibitory decision rules relative to length and coverage

KAUST Repository

Alsolami, Fawaz

2012-01-01

The paper is devoted to the study of algorithms for optimization of inhibitory rules relative to the length and coverage. In contrast with usual rules that have on the right-hand side a relation "attribute ≠ value", inhibitory rules have a relation "attribute = value" on the right-hand side. The considered algorithms are based on extensions of dynamic programming. © 2012 Springer-Verlag.

Complex contexts and relationships affect clinical decisions in group therapy.

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Tasca, Giorgio A; Mcquaid, Nancy; Balfour, Louise

2016-09-01

Clinical errors tend to be underreported even though examining them can provide important training and professional development opportunities. The group therapy context may be prone to clinician errors because of the added complexity within which therapists work and patients receive treatment. We discuss clinical errors that occurred within a group therapy in which a patient for whom group was not appropriate was admitted to the treatment and then was not removed by the clinicians. This was countertherapeutic for both patient and group. Two clinicians were involved: a clinical supervisor who initially assessed and admitted the patient to the group, and a group therapist. To complicate matters, the group therapy occurred within the context of a clinical research trial. The errors, possible solutions, and recommendations are discussed within Reason's Organizational Accident Model (Reason, 2000). In particular, we discuss clinician errors in the context of countertransference and clinician heuristics, group therapy as a local work condition that complicates clinical decision-making, and the impact of the research context as a latent organizational factor. We also present clinical vignettes from the pregroup preparation, group therapy, and supervision. Group therapists are more likely to avoid errors in clinical decisions if they engage in reflective practice about their internal experiences and about the impact of the context in which they work. Therapists must keep in mind the various levels of group functioning, especially related to the group-as-a-whole (i.e., group composition, cohesion, group climate, and safety) when making complex clinical decisions in order to optimize patient outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

A social-technological epistemology of clinical decision-making as mediated by imaging.

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van Baalen, Sophie; Carusi, Annamaria; Sabroe, Ian; Kiely, David G

2017-10-01

In recent years there has been growing attention to the epistemology of clinical decision-making, but most studies have taken the individual physicians as the central object of analysis. In this paper we argue that knowing in current medical practice has an inherently social character and that imaging plays a mediating role in these practices. We have analyzed clinical decision-making within a medical expert team involved in diagnosis and treatment of patients with pulmonary hypertension (PH), a rare disease requiring multidisciplinary team involvement in diagnosis and management. Within our field study, we conducted observations, interviews, video tasks, and a panel discussion. Decision-making in the PH clinic involves combining evidence from heterogeneous sources into a cohesive framing of a patient, in which interpretations of the different sources can be made consistent with each other. Because pieces of evidence are generated by people with different expertise and interpretation and adjustments take place in interaction between different experts, we argue that this process is socially distributed. Multidisciplinary team meetings are an important place where information is shared, discussed, interpreted, and adjusted, allowing for a collective way of seeing and a shared language to be developed. We demonstrate this with an example of image processing in the PH service, an instance in which knowledge is distributed over multiple people who play a crucial role in generating an evaluation of right heart function. Finally, we argue that images fulfill a mediating role in distributed knowing in 3 ways: first, as enablers or tools in acquiring information; second, as communication facilitators; and third, as pervasively framing the epistemic domain. With this study of clinical decision-making in diagnosis and treatment of PH, we have shown that clinical decision-making is highly social and mediated by technologies. The epistemology of clinical decision-making needs

Using whole disease modeling to inform resource allocation decisions: economic evaluation of a clinical guideline for colorectal cancer using a single model.

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Tappenden, Paul; Chilcott, Jim; Brennan, Alan; Squires, Hazel; Glynne-Jones, Rob; Tappenden, Janine

2013-06-01

To assess the feasibility and value of simulating whole disease and treatment pathways within a single model to provide a common economic basis for informing resource allocation decisions. A patient-level simulation model was developed with the intention of being capable of evaluating multiple topics within National Institute for Health and Clinical Excellence's colorectal cancer clinical guideline. The model simulates disease and treatment pathways from preclinical disease through to detection, diagnosis, adjuvant/neoadjuvant treatments, follow-up, curative/palliative treatments for metastases, supportive care, and eventual death. The model parameters were informed by meta-analyses, randomized trials, observational studies, health utility studies, audit data, costing sources, and expert opinion. Unobservable natural history parameters were calibrated against external data using Bayesian Markov chain Monte Carlo methods. Economic analysis was undertaken using conventional cost-utility decision rules within each guideline topic and constrained maximization rules across multiple topics. Under usual processes for guideline development, piecewise economic modeling would have been used to evaluate between one and three topics. The Whole Disease Model was capable of evaluating 11 of 15 guideline topics, ranging from alternative diagnostic technologies through to treatments for metastatic disease. The constrained maximization analysis identified a configuration of colorectal services that is expected to maximize quality-adjusted life-year gains without exceeding current expenditure levels. This study indicates that Whole Disease Model development is feasible and can allow for the economic analysis of most interventions across a disease service within a consistent conceptual and mathematical infrastructure. This disease-level modeling approach may be of particular value in providing an economic basis to support other clinical guidelines. Copyright © 2013 International

Clinical use of patient decision-making aids for stone patients.

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Lim, Amy H; Streeper, Necole M; Best, Sara L; Penniston, Kristina L; Nakada, Stephen Y

2017-08-01

Patient decision-making aids (PDMAs) help patients make informed healthcare decisions and improve patient satisfaction. The utility of PDMAs for patients considering treatments for urolithiasis has not yet been published. We report our experience using PDMAs developed at our institution in the outpatient clinical setting in patients considering a variety of treatment options for stones. Patients with radiographically confirmed urolithiasis were given PDMAs regarding treatment options for their stone(s) based on their clinical profile. We assessed patients' satisfaction, involvedness, and feeling of making a more informed decision with utilization of the PDMAs using a Likert Scale Questionnaire. Information was also collected regarding previous stone passage, history and type of surgical intervention for urolithiasis, and level of education. Patients (n = 43; 18 males, 23 females and two unknown) 53 +/- 14years old were included. Patients reported that they understood the advantages and disadvantages outlined in the PDMAs (97%), that the PDMAs helped them make a more informed decision (83%) and felt more involved in the decision making process (88%). Patients reported that the aids were presented in a balanced manner and used up-to-date scientific information (100%, 84% respectively). Finally, a majority of the patients prefer an expert's opinion when making a treatment decision (98%) with 73% of patients preferring to form their own opinion based on available information. Previous stone surgery was associated with patients feeling more involved with the decision making process (p = 0.0465). PDMAs have a promising role in shared decision-making in the setting of treatment options for nephrolithiasis.

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

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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:

A NEW FRAMEWORK FOR GEOSPATIAL SITE SELECTION USING ARTIFICIAL NEURAL NETWORKS AS DECISION RULES: A CASE STUDY ON LANDFILL SITES

Directory of Open Access Journals (Sweden)

S. K. M. Abujayyab

2015-10-01

Full Text Available This paper briefly introduced the theory and framework of geospatial site selection (GSS and discussed the application and framework of artificial neural networks (ANNs. The related literature on the use of ANNs as decision rules in GSS is scarce from 2000 till 2015. As this study found, ANNs are not only adaptable to dynamic changes but also capable of improving the objectivity of acquisition in GSS, reducing time consumption, and providing high validation. ANNs make for a powerful tool for solving geospatial decision-making problems by enabling geospatial decision makers to implement their constraints and imprecise concepts. This tool offers a way to represent and handle uncertainty. Specifically, ANNs are decision rules implemented to enhance conventional GSS frameworks. The main assumption in implementing ANNs in GSS is that the current characteristics of existing sites are indicative of the degree of suitability of new locations with similar characteristics. GSS requires several input criteria that embody specific requirements and the desired site characteristics, which could contribute to geospatial sites. In this study, the proposed framework consists of four stages for implementing ANNs in GSS. A multilayer feed-forward network with a backpropagation algorithm was used to train the networks from prior sites to assess, generalize, and evaluate the outputs on the basis of the inputs for the new sites. Two metrics, namely, confusion matrix and receiver operating characteristic tests, were utilized to achieve high accuracy and validation. Results proved that ANNs provide reasonable and efficient results as an accurate and inexpensive quantitative technique for GSS.

The Cat and the Pigeons: Some General Comments on (TP) Tax Rulings and State Aid After the Starbucks and Fiat Decisions

NARCIS (Netherlands)

Wattel, P.J.; Richelle, I.; Schön, W.; Traversa, E.

2016-01-01

The Commission State aid decisions on individual tax rulings have created legal uncertainty, which may have been one of their goals. This article comments on their political and policy merits and effects, it wonders whether EU law requires member States to have—and apply in a certain manner—specific

Clinical Decision Support to Implement CYP2D6 Drug-Gene Interaction.

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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.

Knowledge Translation of the PERC Rule for Suspected Pulmonary Embolism: A Blueprint for Reducing the Number of CT Pulmonary Angiograms

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Michael J. Drescher

2017-09-01

Full Text Available Introduction: Computerized decision support decreases the number of computed tomography pulmonary angiograms (CTPA for pulmonary embolism (PE ordered in emergency departments, but it is not always well accepted by emergency physicians. We studied a department-endorsed, evidence-based clinical protocol that included the PE rule-out criteria (PERC rule, multi-modal education using principles of knowledge translation (KT, and clinical decision support embedded in our order entry system, to decrease the number of unnecessary CTPA ordered. Methods: We performed a historically controlled observational before-after study for one year pre- and post-implementation of a departmentally-endorsed protocol. We included patients > 18 in whom providers suspected PE and who did not have a contraindication to CTPA. Providers entered clinical information into a diagnostic pathway via computerized order entry. Prior to protocol implementation, we provided education to ordering providers. The primary outcome measure was the number of CTPA ordered per 1,000 visits one year before vs. after implementation. Results: CTPA declined from 1,033 scans for 98,028 annual visits (10.53 per 1,000 patient visits (95% CI [9.9–11.2] to 892 scans for 101,172 annual visits (8.81 per 1,000 patient visits (95% CI [8.3–9.4] p<0.001. The absolute reduction in PACT ordered was 1.72 per 1,000 visits (a 16% reduction. Patient characteristics were similar for both periods. Conclusion: Knowledge translation clinical decision support using the PERC rule significantly reduced the number of CTPA ordered.

Patients' perceptions of sharing in decisions: a systematic review of interventions to enhance shared decision making in routine clinical practice.

Science.gov (United States)

Légaré, France; Turcotte, Stéphane; Stacey, Dawn; Ratté, Stéphane; Kryworuchko, Jennifer; Graham, Ian D

2012-01-01

Shared decision making is the process in which a healthcare choice is made jointly by the health professional and the patient. Little is known about what patients view as effective or ineffective strategies to implement shared decision making in routine clinical practice. This systematic review evaluates the effectiveness of interventions to improve health professionals' adoption of shared decision making in routine clinical practice, as seen by patients. We searched electronic databases (PubMed, the Cochrane Library, EMBASE, CINAHL, and PsycINFO) from their inception to mid-March 2009. We found additional material by reviewing the reference lists of the studies found in the databases; systematic reviews of studies on shared decision making; the proceedings of various editions of the International Shared Decision Making Conference; and the transcripts of the Society for Medical Decision Making's meetings. In our study selection, we included randomized controlled trials, controlled clinical trials, controlled before-and-after studies, and interrupted time series analyses in which patients evaluated interventions to improve health professionals' adoption of shared decision making. The interventions in question consisted of the distribution of printed educational material; educational meetings; audit and feedback; reminders; and patient-mediated initiatives (e.g. patient decision aids). Two reviewers independently screened the studies and extracted data. Statistical analyses considered categorical and continuous process measures. We computed the standardized effect size for each outcome at the 95% confidence interval. The primary outcome of interest was health professionals' adoption of shared decision making as reported by patients in a self-administered questionnaire. Of the 6764 search results, 21 studies reported 35 relevant comparisons. Overall, the quality of the studies ranged from 0% to 83%. Only three of the 21 studies reported a clinically significant effect

"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

[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.

Physiotherapy students' perceptions and experiences of clinical prediction rules.

Science.gov (United States)

Knox, Grahame M; Snodgrass, Suzanne J; Stanton, Tasha R; Kelly, David H; Vicenzino, Bill; Wand, Benedict M; Rivett, Darren A

2017-09-01

Clinical reasoning can be difficult to teach to pre-professional physiotherapy students due to their lack of clinical experience. It may be that tools such as clinical prediction rules (CPRs) could aid the process, but there has been little investigation into their use in physiotherapy clinical education. This study aimed to determine the perceptions and experiences of physiotherapy students regarding CPRs, and whether they are learning about CPRs on clinical placement. Cross-sectional survey using a paper-based questionnaire. Final year pre-professional physiotherapy students (n=371, response rate 77%) from five universities across five states of Australia. Sixty percent of respondents had not heard of CPRs, and a further 19% had not clinically used CPRs. Only 21% reported using CPRs, and of these nearly three-quarters were rarely, if ever, learning about CPRs in the clinical setting. However most of those who used CPRs (78%) believed CPRs assisted in the development of clinical reasoning skills and none (0%) was opposed to the teaching of CPRs to students. The CPRs most commonly recognised and used by students were those for determining the need for an X-ray following injuries to the ankle and foot (67%), and for identifying deep venous thrombosis (63%). The large majority of students in this sample knew little, if anything, about CPRs and few had learned about, experienced or practiced them on clinical placement. However, students who were aware of CPRs found them helpful for their clinical reasoning and were in favour of learning more about them. Copyright © 2016 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

Syncope: risk stratification and clinical decision making.

Science.gov (United States)

Peeters, Suzanne Y G; Hoek, Amber E; Mollink, Susan M; Huff, J Stephen

2014-04-01

Syncope is a common occurrence in the emergency department, accounting for approximately 1% to 3% of presentations. Syncope is best defined as a brief loss of consciousness and postural tone followed by spontaneous and complete recovery. The spectrum of etiologies ranges from benign to life threatening, and a structured approach to evaluating these patients is key to providing care that is thorough, yet cost-effective. This issue reviews the most relevant evidence for managing and risk stratifying the syncope patient, beginning with a focused history, physical examination, electrocardiogram, and tailored diagnostic testing. Several risk stratification decision rules are compared for performance in various scenarios, including how age and associated comorbidities may predict short-term and long-term adverse events. An algorithm for structured, evidence-based care of the syncope patient is included to ensure that patients requiring hospitalization are managed appropriately and those with benign causes are discharged safely.

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.

A clinical return-to-work rule for patients with back pain.

Science.gov (United States)

Dionne, Clermont E; Bourbonnais, Renée; Frémont, Pierre; Rossignol, Michel; Stock, Susan R; Larocque, Isabelle

2005-06-07

Tools for early identification of workers with back pain who are at high risk of adverse occupational outcome would help concentrate clinical attention on the patients who need it most, while helping reduce unnecessary interventions (and costs) among the others. This study was conducted to develop and validate clinical rules to predict the 2-year work disability status of people consulting for nonspecific back pain in primary care settings. This was a 2-year prospective cohort study conducted in 7 primary care settings in the Quebec City area. The study enrolled 1007 workers (participation, 68.4% of potential participants expected to be eligible) aged 18-64 years who consulted for nonspecific back pain associated with at least 1 day's absence from work. The majority (86%) completed 5 telephone interviews documenting a large array of variables. Clinical information was abstracted from the medical files. The outcome measure was "return to work in good health" at 2 years, a variable that combined patients' occupational status, functional limitations and recurrences of work absence. Predictive models of 2-year outcome were developed with a recursive partitioning approach on a 40% random sample of our study subjects, then validated on the rest. The best predictive model included 7 baseline variables (patient's recovery expectations, radiating pain, previous back surgery, pain intensity, frequent change of position because of back pain, irritability and bad temper, and difficulty sleeping) and was particularly efficient at identifying patients with no adverse occupational outcome (negative predictive value 78%- 94%). A clinical prediction rule accurately identified a large proportion of workers with back pain consulting in a primary care setting who were at a low risk of an adverse occupational outcome.

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

Integrated Case Based and Rule Based Reasoning for Decision Support

OpenAIRE

Eshete, Azeb Bekele

2009-01-01

This project is a continuation of my specialization project which was focused on studying theoretical concepts related to case based reasoning method, rule based reasoning method and integration of them. The integration of rule-based and case-based reasoning methods has shown a substantial improvement with regards to performance over the individual methods. Verdande Technology As wants to try integrating the rule based reasoning method with an existing case based system. This project focu...

Complexity perspectives on clinical decision making in an intensive care unit

NARCIS (Netherlands)

De Bock, Ben A.; Willems, Dick L.; Weinstein, Henry C.

2017-01-01

How to clarify the implications of complexity thinking for decision making in the intensive care unit (ICU)? Retrospective qualitative empirical research. Practitioners in an ICU were interviewed on how their decisions were made regarding a particular patient in a difficult, clinical situation.

Clinical decision-making and therapeutic approaches in osteopathy - a qualitative grounded theory study.

Science.gov (United States)

Thomson, Oliver P; Petty, Nicola J; Moore, Ann P

2014-02-01

There is limited understanding of how osteopaths make decisions in relation to clinical practice. The aim of this research was to construct an explanatory theory of the clinical decision-making and therapeutic approaches of experienced osteopaths in the UK. Twelve UK registered osteopaths participated in this constructivist grounded theory qualitative study. Purposive and theoretical sampling was used to select participants. Data was collected using semi-structured interviews which were audio-recorded and transcribed. As the study approached theoretical sufficiency, participants were observed and video-recorded during a patient appointment, which was followed by a video-prompted interview. Constant comparative analysis was used to analyse and code data. Data analysis resulted in the construction of three qualitatively different therapeutic approaches which characterised participants and their clinical practice, termed; Treater, Communicator and Educator. Participants' therapeutic approach influenced their approach to clinical decision-making, the level of patient involvement, their interaction with patients, and therapeutic goals. Participants' overall conception of practice lay on a continuum ranging from technical rationality to professional artistry, and contributed to their therapeutic approach. A range of factors were identified which influenced participants' conception of practice. The findings indicate that there is variation in osteopaths' therapeutic approaches to practice and clinical decision-making, which are influenced by their overall conception of practice. This study provides the first explanatory theory of the clinical decision-making and therapeutic approaches of osteopaths. Copyright © 2013 Elsevier Ltd. All rights reserved.

Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach.

Science.gov (United States)

Bennett, Casey C; Hauser, Kris

2013-01-01

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor". This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record. The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal

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.

Modified Dempster-Shafer approach using an expected utility interval decision rule

Science.gov (United States)

Cheaito, Ali; Lecours, Michael; Bosse, Eloi

1999-03-01

The combination operation of the conventional Dempster- Shafer algorithm has a tendency to increase exponentially the number of propositions involved in bodies of evidence by creating new ones. The aim of this paper is to explore a 'modified Dempster-Shafer' approach of fusing identity declarations emanating form different sources which include a number of radars, IFF and ESM systems in order to limit the explosion of the number of propositions. We use a non-ad hoc decision rule based on the expected utility interval to select the most probable object in a comprehensive Platform Data Base containing all the possible identity values that a potential target may take. We study the effect of the redistribution of the confidence levels of the eliminated propositions which otherwise overload the real-time data fusion system; these eliminated confidence levels can in particular be assigned to ignorance, or uniformly added to the remaining propositions and to ignorance. A scenario has been selected to demonstrate the performance of our modified Dempster-Shafer method of evidential reasoning.

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

The role of emotion in clinical decision making: an integrative literature review.

Science.gov (United States)

Kozlowski, Desirée; Hutchinson, Marie; Hurley, John; Rowley, Joanne; Sutherland, Joanna

2017-12-15

Traditionally, clinical decision making has been perceived as a purely rational and cognitive process. Recently, a number of authors have linked emotional intelligence (EI) to clinical decision making (CDM) and calls have been made for an increased focus on EI skills for clinicians. The objective of this integrative literature review was to identify and synthesise the empirical evidence for a role of emotion in CDM. A systematic search of the bibliographic databases PubMed, PsychINFO, and CINAHL (EBSCO) was conducted to identify empirical studies of clinician populations. Search terms were focused to identify studies reporting clinician emotion OR clinician emotional intelligence OR emotional competence AND clinical decision making OR clinical reasoning. Twenty three papers were retained for synthesis. These represented empirical work from qualitative, quantitative, and mixed-methods approaches and comprised work with a focus on experienced emotion and on skills associated with emotional intelligence. The studies examined nurses (10), physicians (7), occupational therapists (1), physiotherapists (1), mixed clinician samples (3), and unspecified infectious disease experts (1). We identified two main themes in the context of clinical decision making: the subjective experience of emotion; and, the application of emotion and cognition in CDM. Sub-themes under the subjective experience of emotion were: emotional response to contextual pressures; emotional responses to others; and, intentional exclusion of emotion from CDM. Under the application of emotion and cognition in CDM, sub-themes were: compassionate emotional labour - responsiveness to patient emotion within CDM; interdisciplinary tension regarding the significance and meaning of emotion in CDM; and, emotion and moral judgement. Clinicians' experienced emotions can and do affect clinical decision making, although acknowledgement of that is far from universal. Importantly, this occurs in the in the absence of a

Rule Induction-Based Knowledge Discovery for Energy Efficiency

OpenAIRE

Chen, Qipeng; Fan, Zhong; Kaleshi, Dritan; Armour, Simon M D

2015-01-01

Rule induction is a practical approach to knowledge discovery. Provided that a problem is developed, rule induction is able to return the knowledge that addresses the goal of this problem as if-then rules. The primary goals of knowledge discovery are for prediction and description. The rule format knowledge representation is easily understandable so as to enable users to make decisions. This paper presents the potential of rule induction for energy efficiency. In particular, three rule induct...

The use of emotional intelligence capabilities in clinical reasoning and decision-making: A qualitative, exploratory study.

Science.gov (United States)

Hutchinson, Marie; Hurley, John; Kozlowski, Desirée; Whitehair, Leeann

2018-02-01

To explore clinical nurses' experiences of using emotional intelligence capabilities during clinical reasoning and decision-making. There has been little research exploring whether, or how, nurses employ emotional intelligence (EI) in clinical reasoning and decision-making. Qualitative phase of a larger mixed-methods study. Semistructured qualitative interviews with a purposive sample of registered nurses (n = 12) following EI training and coaching. Constructivist thematic analysis was employed to analyse the narrative transcripts. Three themes emerged: the sensibility to engage EI capabilities in clinical contexts, motivation to actively engage with emotions in clinical decision-making and incorporating emotional and technical perspectives in decision-making. Continuing to separate cognition and emotion in research, theorising and scholarship on clinical reasoning is counterproductive. Understanding more about nurses' use of EI has the potential to improve the calibre of decisions, and the safety and quality of care delivered. © 2017 John Wiley & Sons Ltd.

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

20 CFR 418.1355 - What are the rules for reopening a decision by an administrative law judge of the Office of...

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false What are the rules for reopening a decision by an administrative law judge of the Office of Medicare Hearings and Appeals (OMHA) or by the Medicare Appeals Council (MAC)? 418.1355 Section 418.1355 Employees' Benefits SOCIAL SECURITY ADMINISTRATION MEDICARE SUBSIDIES Medicare Part B...

Clinical value of the Ottawa ankle rules for diagnosis of fractures in acute ankle injuries.

Directory of Open Access Journals (Sweden)

Xin Wang

Full Text Available BACKGROUND: The Ottawa ankle rules (OAR are clinical decision guidelines used to identify whether patients with ankle injuries need to undergo radiography. The OAR have been proven that their application reduces unnecessary radiography. They have nearly perfect sensitivity for identifying clinically significant ankle fractures. OBJECTIVES: The purpose of this study was to assess the applicability of the OAR in China, to examine their accuracy for the diagnosis of fractures in patients with acute ankle sprains, and to assess their clinical utility for the detection of occult fractures. METHODS: In this prospective study, patients with acute ankle injuries were enrolled during a 6-month period. The eligible patients were examined by emergency orthopedic specialists using the OAR, and then underwent ankle radiography. The results of examination using the OAR were compared with the radiographic results to assess the accuracy of the OAR for ankle fractures. Patients with OAR results highly suggestive of fracture, but no evidence of a fracture on radiographs, were advised to undergo 3-dimensional computed tomography (3D-CT. RESULTS: 183 patients with ankle injuries were enrolled in the study and 63 of these injuries involved fractures. The pooled sensitivity, specificity, positive predictive value and negative predictive value of the OAR for detection of fractures of the ankle were 96.8%, 45.8%, 48.4% and 96.5%, respectively. Our results suggest that clinical application of the OAR could decrease unnecessary radiographs by 31.1%. Of the 21 patients with positive OAR results and negative radiographic findings who underwent 3D-CT examination, five had occult fractures of the lateral malleolus. CONCLUSIONS: The OAR are applicable in the Chinese population, and have high sensitivity and modest specificity for the diagnosis of fractures associated with acute ankle injury. They may detect some occult fractures of the malleoli that are not visible on

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

Continuous quality improvement for the clinical decision unit.

Science.gov (United States)

Mace, Sharon E

2004-01-01

Clinical decision units (CDUs) are a relatively new and growing area of medicine in which patients undergo rapid evaluation and treatment. Continuous quality improvement (CQI) is important for the establishment and functioning of CDUs. CQI in CDUs has many advantages: better CDU functioning, fulfillment of Joint Commission on Accreditation of Healthcare Organizations mandates, greater efficiency/productivity, increased job satisfaction, better performance improvement, data availability, and benchmarking. Key elements include a database with volume indicators, operational policies, clinical practice protocols (diagnosis specific/condition specific), monitors, benchmarks, and clinical pathways. Examples of these important parameters are given. The CQI process should be individualized for each CDU and hospital.

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 ...

Is expected utility theory normative for medical decision making?

Science.gov (United States)

Cohen, B J

1996-01-01

Expected utility theory is felt by its proponents to be a normative theory of decision making under uncertainty. The theory starts with some simple axioms that are held to be rules that any rational person would follow. It can be shown that if one adheres to these axioms, a numerical quantity, generally referred to as utility, can be assigned to each possible outcome, with the preferred course of action being that which has the highest expected utility. One of these axioms, the independence principle, is controversial, and is frequently violated in experimental situations. Proponents of the theory hold that these violations are irrational. The independence principle is simply an axiom dictating consistency among preferences, in that it dictates that a rational agent should hold a specified preference given another stated preference. When applied to preferences between lotteries, the independence principle can be demonstrated to be a rule that is followed only when preferences are formed in a particular way. The logic of expected utility theory is that this demonstration proves that preferences should be formed in this way. An alternative interpretation is that this demonstrates that the independence principle is not a valid general rule of consistency, but in particular, is a rule that must be followed if one is to consistently apply the decision rule "choose the lottery that has the highest expected utility." This decision rule must be justified on its own terms as a valid rule of rationality by demonstration that violation would lead to decisions that conflict with the decision maker's goals. This rule does not appear to be suitable for medical decisions because often these are one-time decisions in which expectation, a long-run property of a random variable, would not seem to be applicable. This is particularly true for those decisions involving a non-trivial risk of death.

Constructing diagnostic likelihood: clinical decisions using subjective versus statistical probability.

Science.gov (United States)

Kinnear, John; Jackson, Ruth

2017-07-01

Although physicians are highly trained in the application of evidence-based medicine, and are assumed to make rational decisions, there is evidence that their decision making is prone to biases. One of the biases that has been shown to affect accuracy of judgements is that of representativeness and base-rate neglect, where the saliency of a person's features leads to overestimation of their likelihood of belonging to a group. This results in the substitution of 'subjective' probability for statistical probability. This study examines clinicians' propensity to make estimations of subjective probability when presented with clinical information that is considered typical of a medical condition. The strength of the representativeness bias is tested by presenting choices in textual and graphic form. Understanding of statistical probability is also tested by omitting all clinical information. For the questions that included clinical information, 46.7% and 45.5% of clinicians made judgements of statistical probability, respectively. Where the question omitted clinical information, 79.9% of clinicians made a judgement consistent with statistical probability. There was a statistically significant difference in responses to the questions with and without representativeness information (χ2 (1, n=254)=54.45, pprobability. One of the causes for this representativeness bias may be the way clinical medicine is taught where stereotypic presentations are emphasised in diagnostic decision making. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Making reasonable decisions: a qualitative study of medical decision making in the care of patients with a clinically significant haemoglobin disorder.

Science.gov (United States)

Crowther, Helen J; Kerridge, Ian

2015-10-01

Therapies utilized in patients with clinically significant haemoglobin disorders appear to vary between clinicians and units. This study aimed to investigate the processes of evidence implementation and medical decision making in the care of such patients in NSW, Australia. Using semi-structured interviews, 11 haematologists discussed their medical decision-making processes with particular attention paid to the use of published evidence. Transcripts were thematically analysed by a single investigator on a line-by-line basis. Decision making surrounding the care of patients with significant haemoglobin disorders varied and was deeply contextual. Three main determinants of clinical decision making were identified - factors relating to the patient and to their illness, factors specific to the clinician and the institution in which they were practising and factors related to the notion of evidence and to utility and role of evidence-based medicine in clinical practice. Clinicians pay considerable attention to medical decision making and evidence incorporation and attempt to tailor these to particular patient contexts. However, the patient context is often inferred and when discordant with the clinician's own contexture can lead to discomfort with decision recommendations. Clinicians strive to improve comfort through the use of experience and trustworthy evidence. © 2015 John Wiley & Sons, Ltd.

Comprehensible knowledge model creation for cancer treatment decision making.

Science.gov (United States)

Afzal, Muhammad; Hussain, Maqbool; Ali Khan, Wajahat; Ali, Taqdir; Lee, Sungyoung; Huh, Eui-Nam; Farooq Ahmad, Hafiz; Jamshed, Arif; Iqbal, Hassan; Irfan, Muhammad; Abbas Hydari, Manzar

2017-03-01

A wealth of clinical data exists in clinical documents in the form of electronic health records (EHRs). This data can be used for developing knowledge-based recommendation systems that can assist clinicians in clinical decision making and education. One of the big hurdles in developing such systems is the lack of automated mechanisms for knowledge acquisition to enable and educate clinicians in informed decision making. An automated knowledge acquisition methodology with a comprehensible knowledge model for cancer treatment (CKM-CT) is proposed. With the CKM-CT, clinical data are acquired automatically from documents. Quality of data is ensured by correcting errors and transforming various formats into a standard data format. Data preprocessing involves dimensionality reduction and missing value imputation. Predictive algorithm selection is performed on the basis of the ranking score of the weighted sum model. The knowledge builder prepares knowledge for knowledge-based services: clinical decisions and education support. Data is acquired from 13,788 head and neck cancer (HNC) documents for 3447 patients, including 1526 patients of the oral cavity site. In the data quality task, 160 staging values are corrected. In the preprocessing task, 20 attributes and 106 records are eliminated from the dataset. The Classification and Regression Trees (CRT) algorithm is selected and provides 69.0% classification accuracy in predicting HNC treatment plans, consisting of 11 decision paths that yield 11 decision rules. Our proposed methodology, CKM-CT, is helpful to find hidden knowledge in clinical documents. In CKM-CT, the prediction models are developed to assist and educate clinicians for informed decision making. The proposed methodology is generalizable to apply to data of other domains such as breast cancer with a similar objective to assist clinicians in decision making and education. Copyright © 2017 Elsevier Ltd. All rights reserved.

A prediction rule for the development of delirium among patients in medical wards: Chi-Square Automatic Interaction Detector (CHAID) decision tree analysis model.

Science.gov (United States)

Kobayashi, Daiki; Takahashi, Osamu; Arioka, Hiroko; Koga, Shinichiro; Fukui, Tsuguya

2013-10-01

To predict development of delirium among patients in medical wards by a Chi-Square Automatic Interaction Detector (CHAID) decision tree model. This was a retrospective cohort study of all adult patients admitted to medical wards at a large community hospital. The subject patients were randomly assigned to either a derivation or validation group (2:1) by computed random number generation. Baseline data and clinically relevant factors were collected from the electronic chart. Primary outcome was the development of delirium during hospitalization. All potential predictors were included in a forward stepwise logistic regression model. CHAID decision tree analysis was also performed to make another prediction model with the same group of patients. Receiver operating characteristic curves were drawn, and the area under the curves (AUCs) were calculated for both models. In the validation group, these receiver operating characteristic curves and AUCs were calculated based on the rules from derivation. A total of 3,570 patients were admitted: 2,400 patients assigned to the derivation group and 1,170 to the validation group. A total of 91 and 51 patients, respectively, developed delirium. Statistically significant predictors were delirium history, age, underlying malignancy, and activities of daily living impairment in CHAID decision tree model, resulting in six distinctive groups by the level of risk. AUC was 0.82 in derivation and 0.82 in validation with CHAID model and 0.78 in derivation and 0.79 in validation with logistic model. We propose a validated CHAID decision tree prediction model to predict the development of delirium among medical patients. Copyright © 2013 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

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.

Methodological approaches based on business rules

Directory of Open Access Journals (Sweden)

Anca Ioana ANDREESCU

2008-01-01

Full Text Available Business rules and business processes are essential artifacts in defining the requirements of a software system. Business processes capture business behavior, while rules connect processes and thus control processes and business behavior. Traditionally, rules are scattered inside application code. This approach makes it very difficult to change rules and shorten the life cycle of the software system. Because rules change more quickly than the application itself, it is desirable to externalize the rules and move them outside the application. This paper analyzes and evaluates three well-known business rules approaches. It also outlines some critical factors that have to be taken into account in the decision to introduce business rules facilities in a software system. Based on the concept of explicit manipulation of business rules in a software system, the need for a general approach based on business rules is discussed.

Identifying design considerations for a shared decision aid for use at the point of outpatient clinical care: An ethnographic study at an inner city clinic.

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Hajizadeh, Negin; Perez Figueroa, Rafael E; Uhler, Lauren M; Chiou, Erin; Perchonok, Jennifer E; Montague, Enid

2013-03-06

Computerized decision aids could facilitate shared decision-making at the point of outpatient clinical care. The objective of this study was to investigate whether a computerized shared decision aid would be feasible to implement in an inner-city clinic by evaluating the current practices in shared decision-making, clinicians' use of computers, patient and clinicians' attitudes and beliefs toward computerized decision aids, and the influence of time on shared decision-making. Qualitative data analysis of observations and semi-structured interviews with patients and clinicians at an inner-city outpatient clinic. The findings provided an exploratory look at the prevalence of shared decision-making and attitudes about health information technology and decision aids. A prominent barrier to clinicians engaging in shared decision-making was a lack of perceived patient understanding of medical information. Some patients preferred their clinicians make recommendations for them rather than engage in formal shared decision-making. Health information technology was an integral part of the clinic visit and welcomed by most clinicians and patients. Some patients expressed the desire to engage with health information technology such as viewing their medical information on the computer screen with their clinicians. All participants were receptive to the idea of a decision aid integrated within the clinic visit although some clinicians were concerned about the accuracy of prognostic estimates for complex medical problems. We identified several important considerations for the design and implementation of a computerized decision aid including opportunities to: bridge clinician-patient communication about medical information while taking into account individual patients' decision-making preferences, complement expert clinician judgment with prognostic estimates, take advantage of patient waiting times, and make tasks involved during the clinic visit more efficient. These findings

The role of emotion in clinical decision making: an integrative literature review

OpenAIRE

Kozlowski, Desirée; Hutchinson, Marie; Hurley, John; Rowley, Joanne; Sutherland, Joanna

2017-01-01

Background Traditionally, clinical decision making has been perceived as a purely rational and cognitive process. Recently, a number of authors have linked emotional intelligence (EI) to clinical decision making (CDM) and calls have been made for an increased focus on EI skills for clinicians. The objective of this integrative literature review was to identify and synthesise the empirical evidence for a role of emotion in CDM. Methods A systematic search of the bibliographic databases PubMed,...

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

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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

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

Clinical decision making-a functional medicine perspective.

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Pizzorno, Joseph E

2012-09-01

As 21st century health care moves from a disease-based approach to a more patient-centric system that can address biochemical individuality to improve health and function, clinical decision making becomes more complex. Accentuating the problem is the lack of a clear standard for this more complex functional medicine approach. While there is relatively broad agreement in Western medicine for what constitutes competent assessment of disease and identification of related treatment approaches, the complex functional medicine model posits multiple and individualized diagnostic and therapeutic approaches, most or many of which have reasonable underlying science and principles, but which have not been rigorously tested in a research or clinical setting. This has led to non-rigorous thinking and sometimes to uncritical acceptance of both poorly documented diagnostic procedures and ineffective therapies, resulting in less than optimal clinical care.

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

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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.

Students' stereotypes of patients as barriers to clinical decision-making.

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Johnson, S M; Kurtz, M E; Tomlinson, T; Howe, K R

1986-09-01

The ability to formulate quick, accurate clinical judgments is stressed in medical training. Speed is usually an asset when a physician sorts through his biomedical knowledge, but it is often a liability when the physician assesses the sociocultural context of a clinical encounter. At the Michigan State University College of Osteopathic Medicine, a study was designed which graphically illustrated to beginning students that unconscious sociocultural stereotypes may influence clinical decision-making. Three entering classes of students were shown a videotape depicting five simulated patients (attractive black woman, attractive white woman, professional man, middle-aged housewife, and elderly man), each presenting with the same physical complaint. Elements of positive and negative stereotypes were incorporated into each of the portrayals, and the students rated these patients on positive and negative characteristics. The results suggested that the students attributed both positive and negative characteristics to patients on the basis of irrelevant characteristics, such as attractiveness, and with little further justification for their attributions. Such stereotypic generalizations held by students may become barriers to the students' objective clinical decision-making.

Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert.

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MacRae, Jayden; Love, Tom; Baker, Michael G; Dowell, Anthony; Carnachan, Matthew; Stubbe, Maria; McBain, Lynn

2015-10-06

We designed and validated a rule-based expert system to identify influenza like illness (ILI) from routinely recorded general practice clinical narrative to aid a larger retrospective research study into the impact of the 2009 influenza pandemic in New Zealand. Rules were assessed using pattern matching heuristics on routine clinical narrative. The system was trained using data from 623 clinical encounters and validated using a clinical expert as a gold standard against a mutually exclusive set of 901 records. We calculated a 98.2 % specificity and 90.2 % sensitivity across an ILI incidence of 12.4 % measured against clinical expert classification. Peak problem list identification of ILI by clinical coding in any month was 9.2 % of all detected ILI presentations. Our system addressed an unusual problem domain for clinical narrative classification; using notational, unstructured, clinician entered information in a community care setting. It performed well compared with other approaches and domains. It has potential applications in real-time surveillance of disease, and in assisted problem list coding for clinicians. Our system identified ILI presentation with sufficient accuracy for use at a population level in the wider research study. The peak coding of 9.2 % illustrated the need for automated coding of unstructured narrative in our study.

Clinical decisions for anterior restorations: the concept of restorative volume.

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Cardoso, Jorge André; Almeida, Paulo Júlio; Fischer, Alex; Phaxay, Somano Luang

2012-12-01

The choice of the most appropriate restoration for anterior teeth is often a difficult decision. Numerous clinical and technical factors play an important role in selecting the treatment option that best suits the patient and the restorative team. Experienced clinicians have developed decision processes that are often more complex than may seem. Less experienced professionals may find difficulties making treatment decisions because of the widely varied restorative materials available and often numerous similar products offered by different manufacturers. The authors reviewed available evidence and integrated their clinical experience to select relevant factors that could provide a logical and practical guideline for restorative decisions in anterior teeth. The presented concept of restorative volume is based on structural, optical, and periodontal factors. Each of these factors will influence the short- and long-term behavior of restorations in terms of esthetics, biology, and function. Despite the marked evolution of esthetic restorative techniques and materials, significant limitations still exist, which should be addressed by researchers. The presented guidelines must be regarded as a mere orientation for risk analysis. A comprehensive individual approach should always be the core of restorative esthetic treatments. The complex decision process for anterior esthetic restorations can be clarified by a systematized examination of structural, optical, and periodontal factors. The basis for the proposed thought process is the concept of restorative volume that is a contemporary interpretation of restoration categories and their application. © 2012 Wiley Periodicals, Inc.

Clinical intuition in the nursing process and decision-making-A mixed-studies review.

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Melin-Johansson, Christina; Palmqvist, Rebecca; Rönnberg, Linda

2017-12-01

To review what is characteristic of registered nurses' intuition in clinical settings, in relationships and in the nursing process. Intuition is a controversial concept and nurses believe that there are difficulties in how they should explain their nursing actions or decisions based on intuition. Much of the evidence from the body of research indicates that nurses value their intuition in a variety of clinical settings. More information on how nurses integrate intuition as a core element in daily clinical work would contribute to an improved understanding on how they go about this. Intuition deserves a place in evidence-based activities, where intuition is an important component associated with the nursing process. An integrative review strengthened with a mixed-studies review. Literature searches were conducted in the databases CINAHL, PubMed and PsycINFO, and literature published 1985-2016 were included. The findings in the studies were analysed with content analysis, and the synthesis process entailed a reasoning between the authors. After a quality assessment, 16 studies were included. The analysis and synthesis resulted in three categories. The characteristics of intuition in the nurse's daily clinical activities include application, assertiveness and experiences; in the relationships with patients' intuition include unique connections, mental and bodily responses, and personal qualities; and in the nursing process include support and guidance, component and clues in decision-making, and validating decisions. Intuition is more than simply a "gut feeling," and it is a process based on knowledge and care experience and has a place beside research-based evidence. Nurses integrate both analysis and synthesis of intuition alongside objective data when making decisions. They should rely on their intuition and use this knowledge in clinical practice as a support in decision-making, which increases the quality and safety of patient care. We find that intuition plays a

IBM’s Health Analytics and Clinical Decision Support

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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

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...

Whole mind and shared mind in clinical decision-making.

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Epstein, Ronald Mark

2013-02-01

To review the theory, research evidence and ethical implications regarding "whole mind" and "shared mind" in clinical practice in the context of chronic and serious illnesses. Selective critical review of the intersection of classical and naturalistic decision-making theories, cognitive neuroscience, communication research and ethics as they apply to decision-making and autonomy. Decision-making involves analytic thinking as well as affect and intuition ("whole mind") and sharing cognitive and affective schemas of two or more individuals ("shared mind"). Social relationships can help processing of complex information that otherwise would overwhelm individuals' cognitive capacities. Medical decision-making research, teaching and practice should consider both analytic and non-analytic cognitive processes. Further, research should consider that decisions emerge not only from the individual perspectives of patients, their families and clinicians, but also the perspectives that emerge from the interactions among them. Social interactions have the potential to enhance individual autonomy, as well as to promote relational autonomy based on shared frames of reference. Shared mind has the potential to result in wiser decisions, greater autonomy and self-determination; yet, clinicians and patients should be vigilant for the potential of hierarchical relationships to foster coercion or silencing of the patient's voice. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Clinical decision-making and secondary findings in systems medicine.

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Fischer, T; Brothers, K B; Erdmann, P; Langanke, M

2016-05-21

Systems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology (especially systems biology); "big data" statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems medicine knowledge and clinicians working to apply it. This article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through "big data" approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the "death computer," to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their

Studying the effect of clinical uncertainty on physicians' decision-making using ILIAD.

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Anderson, J D; Jay, S J; Weng, H C; Anderson, M M

1995-01-01

The influence of uncertainty on physicians' practice behavior is not well understood. In this research, ILIAD, a diagnostic expert system, has been used to study physicians' responses to uncertainty and how their responses affected clinical performance. The simulation mode of ILIAD was used to standardize the presentation and scoring of two cases to 46 residents in emergency medicine, internal medicine, family practice and transitional medicine at Methodist Hospital of Indiana. A questionnaire was used to collect additional data on how physicians respond to clinical uncertainty. A structural equation model was developed, estimated, and tested. The results indicate that stress that physicians experience in dealing with clinical uncertainty has a negative effect on their clinical performance. Moreover, the way that physicians respond to uncertainty has positive and negative effects on their performance. Open discussions with patients about clinical decisions and the use of practice guidelines improves performance. However, when the physician's clinical decisions are influenced by patient demands or their peers, their performance scores decline.

Medical students, clinical preventive services, and shared decision-making.

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Keefe, Carole W; Thompson, Margaret E; Noel, Mary Margaret

2002-11-01

Improving access to preventive care requires addressing patient, provider, and systems barriers. Patients often lack knowledge or are skeptical about the importance of prevention. Physicians feel that they have too little time, are not trained to deliver preventive services, and are concerned about the effectiveness of prevention. We have implemented an educational module in the required family practice clerkship (1) to enhance medical student learning about common clinical preventive services and (2) to teach students how to inform and involve patients in shared decision making about those services. Students are asked to examine available evidence-based information for preventive screening services. They are encouraged to look at the recommendations of various organizations and use such resources as reports from the U.S. Preventive Services Task Force to determine recommendations they want to be knowledgeable about in talking with their patients. For learning shared decision making, students are trained to use a model adapted from Braddock and colleagues(1) to discuss specific screening services and to engage patients in the process of making informed decisions about what is best for their own health. The shared decision making is presented and modeled by faculty, discussed in small groups, and students practice using Web-based cases and simulations. The students are evaluated using formative and summative performance-based assessments as they interact with simulated patients about (1) screening for high blood cholesterol and other lipid abnormalities, (2) screening for colorectal cancer, (3) screening for prostate cancer, and (4) screening for breast cancer. The final student evaluation is a ten-minute, videotaped discussion with a simulated patient about screening for colorectal cancer that is graded against a checklist that focuses primarily on the elements of shared decision making. Our medical students appear quite willing to accept shared decision making as

An integrated review of the correlation between critical thinking ability and clinical decision-making in nursing.

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Lee, Daphne Sk; Abdullah, Khatijah Lim; Subramanian, Pathmawathi; Bachmann, Robert Thomas; Ong, Swee Leong

2017-12-01

To explore whether there is a correlation between critical thinking ability and clinical decision-making among nurses. Critical thinking is currently considered as an essential component of nurses' professional judgement and clinical decision-making. If confirmed, nursing curricula may be revised emphasising on critical thinking with the expectation to improve clinical decision-making and thus better health care. Integrated literature review. The integrative review was carried out after a comprehensive literature search using electronic databases Ovid, EBESCO MEDLINE, EBESCO CINAHL, PROQuest and Internet search engine Google Scholar. Two hundred and 22 articles from January 1980 to end of 2015 were retrieved. All studies evaluating the relationship between critical thinking and clinical decision-making, published in English language with nurses or nursing students as the study population, were included. No qualitative studies were found investigating the relationship between critical thinking and clinical decision-making, while 10 quantitative studies met the inclusion criteria and were further evaluated using the Quality Assessment and Validity Tool. As a result, one study was excluded due to a low-quality score, with the remaining nine accepted for this review. Four of nine studies established a positive relationship between critical thinking and clinical decision-making. Another five studies did not demonstrate a significant correlation. The lack of refinement in studies' design and instrumentation were arguably the main reasons for the inconsistent results. Research studies yielded contradictory results as regard to the relationship between critical thinking and clinical decision-making; therefore, the evidence is not convincing. Future quantitative studies should have representative sample size, use critical thinking measurement tools related to the healthcare sector and evaluate the predisposition of test takers towards their willingness and ability to think

Do educational interventions improve nurses' clinical decision making and judgement? A systematic review.

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Thompson, Carl; Stapley, Sally

2011-07-01

Despite the growing popularity of decision making in nursing curricula, the effectiveness of educational interventions to improve nursing judgement and decision making is unknown. We sought to synthesise and summarise the comparative evidence for educational interventions to improve nursing judgements and clinical decisions. A systematic review. Electronic databases: Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, CINAHL and PsycINFO, Social Sciences Citation Index, OpenSIGLE conference proceedings and hand searching nursing journals. Studies published since 1960, reporting any educational intervention that aimed to improve nurses' clinical judgements or decision making were included. Studies were assessed for relevance and quality. Data extracted included study design; educational setting; the nature of participants; whether the study was concerned with the clinical application of skills or the application of theory; the type of decision targeted by the intervention (e.g. diagnostic reasoning) and whether the evaluation of the intervention focused on efficacy or effectiveness. A narrative approach to study synthesis was used due to heterogeneity in interventions, study samples, outcomes and settings and incomplete reporting of effect sizes. From 5262 initial citations 24 studies were included in the review. A variety of educational approaches were reported. Study quality and content reporting was generally poor. Pedagogical theories were widely used but use of decision theory (with the exception of subjective expected utility theory implicit in decision analysis) was rare. The effectiveness and efficacy of interventions was mixed. Educational interventions to improve nurses' judgements and decisions are complex and the evidence from comparative studies does little to reduce the uncertainty about 'what works'. Nurse educators need to pay attention to decision, as well as pedagogical, theory in the design of interventions. Study design and

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

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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.

Future Costs, Fixed Healthcare Budgets, and the Decision Rules of Cost-Effectiveness Analysis.

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van Baal, Pieter; Meltzer, David; Brouwer, Werner

2016-02-01

Life-saving medical technologies result in additional demand for health care due to increased life expectancy. However, most economic evaluations do not include all medical costs that may result from this additional demand in health care and include only future costs of related illnesses. Although there has been much debate regarding the question to which extent future costs should be included from a societal perspective, the appropriate role of future medical costs in the widely adopted but more narrow healthcare perspective has been neglected. Using a theoretical model, we demonstrate that optimal decision rules for cost-effectiveness analyses assuming fixed healthcare budgets dictate that future costs of both related and unrelated medical care should be included. Practical relevance of including the costs of future unrelated medical care is illustrated using the example of transcatheter aortic valve implantation. Our findings suggest that guidelines should prescribe inclusion of these costs. Copyright © 2014 John Wiley & Sons, Ltd.

Mixture-based gatekeeping procedures in adaptive clinical trials.

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Kordzakhia, George; Dmitrienko, Alex; Ishida, Eiji

2018-01-01

Clinical trials with data-driven decision rules often pursue multiple clinical objectives such as the evaluation of several endpoints or several doses of an experimental treatment. These complex analysis strategies give rise to "multivariate" multiplicity problems with several components or sources of multiplicity. A general framework for defining gatekeeping procedures in clinical trials with adaptive multistage designs is proposed in this paper. The mixture method is applied to build a gatekeeping procedure at each stage and inferences at each decision point (interim or final analysis) are performed using the combination function approach. An advantage of utilizing the mixture method is that it enables powerful gatekeeping procedures applicable to a broad class of settings with complex logical relationships among the hypotheses of interest. Further, the combination function approach supports flexible data-driven decisions such as a decision to increase the sample size or remove a treatment arm. The paper concludes with a clinical trial example that illustrates the methodology by applying it to develop an adaptive two-stage design with a mixture-based gatekeeping procedure.

Ethical decision making

OpenAIRE

Zsolnai, László

2011-01-01

The self-centeredness of modern organizations leads to environmental destruction and human deprivation. The principle of responsibility developed by Hans Jonas requires caring for the beings affected by our decisions and actions. Ethical decision-making creates a synthesis of reverence for ethical norms, rationality in goal achievement, and respect for the stakeholders. The maximin rule selects the "least worst alternative" in the multidimensional decision space of deontologica...

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

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Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

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

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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.

Improving the anesthetic process by a fuzzy rule based medical decision system.

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Mendez, Juan Albino; Leon, Ana; Marrero, Ayoze; Gonzalez-Cava, Jose M; Reboso, Jose Antonio; Estevez, Jose Ignacio; Gomez-Gonzalez, José F

2018-01-01

The main objective of this research is the design and implementation of a new fuzzy logic tool for automatic drug delivery in patients undergoing general anesthesia. The aim is to adjust the drug dose to the real patient needs using heuristic knowledge provided by clinicians. A two-level computer decision system is proposed. The idea is to release the clinician from routine tasks so that he can focus on other variables of the patient. The controller uses the Bispectral Index (BIS) to assess the hypnotic state of the patient. Fuzzy controller was included in a closed-loop system to reach the BIS target and reject disturbances. BIS was measured using a BIS VISTA monitor, a device capable of calculating the hypnosis level of the patient through EEG information. An infusion pump with propofol 1% is used to supply the drug to the patient. The inputs to the fuzzy inference system are BIS error and BIS rate. The output is infusion rate increment. The mapping of the input information and the appropriate output is given by a rule-base based on knowledge of clinicians. To evaluate the performance of the fuzzy closed-loop system proposed, an observational study was carried out. Eighty one patients scheduled for ambulatory surgery were randomly distributed in 2 groups: one group using a fuzzy logic based closed-loop system (FCL) to automate the administration of propofol (42 cases); the second group using manual delivering of the drug (39 cases). In both groups, the BIS target was 50. The FCL, designed with intuitive logic rules based on the clinician experience, performed satisfactorily and outperformed the manual administration in patients in terms of accuracy through the maintenance stage. Copyright © 2018 Elsevier B.V. All rights reserved.

Assessing an Adolescent's Capacity for Autonomous Decision-Making in Clinical Care.

Science.gov (United States)

Michaud, Pierre-André; Blum, Robert Wm; Benaroyo, Lazare; Zermatten, Jean; Baltag, Valentina

2015-10-01

The purpose of this article is to provide policy guidance on how to assess the capacity of minor adolescents for autonomous decision-making without a third party authorization, in the field of clinical care. In June 2014, a two-day meeting gathered 20 professionals from all continents, working in the field of adolescent medicine, neurosciences, developmental and clinical psychology, sociology, ethics, and law. Formal presentations and discussions were based on a literature search and the participants' experience. The assessment of adolescent decision-making capacity includes the following: (1) a review of the legal context consistent with the principles of the Convention on the Rights of the Child; (2) an empathetic relationship between the adolescent and the health care professional/team; (3) the respect of the adolescent's developmental stage and capacities; (4) the inclusion, if relevant, of relatives, peers, teachers, or social and mental health providers with the adolescent's consent; (5) the control of coercion and other social forces that influence decision-making; and (6) a deliberative stepwise appraisal of the adolescent's decision-making process. This stepwise approach, already used among adults with psychiatric disorders, includes understanding the different facets of the given situation, reasoning on the involved issues, appreciating the outcomes linked with the decision(s), and expressing a choice. Contextual and psychosocial factors play pivotal roles in the assessment of adolescents' decision-making capacity. The evaluation must be guided by a well-established procedure, and health professionals should be trained accordingly. These proposals are the first to have been developed by a multicultural, multidisciplinary expert panel. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Strategies to facilitate shared decision-making about pediatric oncology clinical trial enrollment: A systematic review.

Science.gov (United States)

Robertson, Eden G; Wakefield, Claire E; Signorelli, Christina; Cohn, Richard J; Patenaude, Andrea; Foster, Claire; Pettit, Tristan; Fardell, Joanna E

2018-07-01

We conducted a systematic review to identify the strategies that have been recommended in the literature to facilitate shared decision-making regarding enrolment in pediatric oncology clinical trials. We searched seven databases for peer-reviewed literature, published 1990-2017. Of 924 articles identified, 17 studies were eligible for the review. We assessed study quality using the 'Mixed-Methods Appraisal Tool'. We coded the results and discussions of papers line-by-line using nVivo software. We categorized strategies thematically. Five main themes emerged: 1) decision-making as a process, 2) individuality of the process; 3) information provision, 4) the role of communication, or 5) decision and psychosocial support. Families should have adequate time to make a decision. HCPs should elicit parents' and patients' preferences for level of information and decision involvement. Information should be clear and provided in multiple modalities. Articles also recommended providing training for healthcare professionals and access to psychosocial support for families. High quality, individually-tailored information, open communication and psychosocial support appear vital in supporting decision-making regarding enrollment in clinical trials. These data will usefully inform future decision-making interventions/tools to support families making clinical trial decisions. A solid evidence-base for effective strategies which facilitate shared decision-making is needed. Copyright © 2018 Elsevier B.V. All rights reserved.

Fuzzy Sets-based Control Rules for Terminating Algorithms

Directory of Open Access Journals (Sweden)

Jose L. VERDEGAY

2002-01-01

Full Text Available In this paper some problems arising in the interface between two different areas, Decision Support Systems and Fuzzy Sets and Systems, are considered. The Model-Base Management System of a Decision Support System which involves some fuzziness is considered, and in that context the questions on the management of the fuzziness in some optimisation models, and then of using fuzzy rules for terminating conventional algorithms are presented, discussed and analyzed. Finally, for the concrete case of the Travelling Salesman Problem, and as an illustration of determination, management and using the fuzzy rules, a new algorithm easy to implement in the Model-Base Management System of any oriented Decision Support System is shown.

Appreciative inquiry enhances cardiology nurses’ clinical decision making when using a clinical guideline on delirium

DEFF Research Database (Denmark)

Vedsegaard, Helle; Schrader, Anne-Marie; Rom, Gitte

2016-01-01

The current study responds to implementation challenges with translating evidence-based knowledge into practice. We explore how appreciative inquiry can be used in in-house learning sessions for nurses to enhance their knowledge in using a guideline on delirium as part of clinical decision making...... and axial coding drawing on the principles of grounded theory. The study shows that appreciative inquiry was meaningful to cardiology nurses in providing them with knowledge of using a guideline on delirium in clinical decision making, the main reasons being a) data on a current patient were included, b....... Through 18 sessions with 3–12 nurses, an appreciative inquiry approach was used. Specialist nurses from the Heart Centre of Copenhagen and senior lecturers from the Department of Nursing at Metropolitan University College facilitated the sessions. Field notes from the sessions were analysed using open...

76 FR 24802 - Eliminating the Decision Review Board

Science.gov (United States)

2011-05-03

... 0960-AG80 Eliminating the Decision Review Board AGENCY: Social Security Administration. ACTION: Final rules. SUMMARY: We are eliminating the Decision Review Board (DRB) portions of part 405 of our rules...-level process. DSI also eliminated review by the Appeals Council, the final step in our administrative...

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.

Inside the black box: Starting to uncover the underlying decision rules used in a one-by-one expert assessment of occupational exposure in case-control studies

NARCIS (Netherlands)

Wheeler, D.C.; Burstyn, I.; Vermeulen, R.; Yu, K.; Shortreed, S.M.; Pronk, A.; Stewart, P.A.; Colt, J.S.; Baris, D.; Karagas, M.R.; Schwenn, M.; Johnson, A.; Silverman, D.T.; Friesen, M.C.

2013-01-01

Objectives Evaluating occupational exposures in population-based case-control studies often requires exposure assessors to review each study participant's reported occupational information job-by-job to derive exposure estimates. Although such assessments likely have underlying decision rules, they

Decision criteria in PSA applications

International Nuclear Information System (INIS)

Holmberg, J.E.; Pulkkinen, U.; Rosqvist, T.; Simola, K.

2001-11-01

Along with the adoption of risk informed decision making principles, the need for formal probabilistic decision rule or criteria has been risen. However, there are many practical and theoretical problems in the application of probabilistic criteria. One has to think what is the proper way to apply probabilistic rules together with deterministic ones and how the criteria are weighted with respect to each other. In this report, we approach the above questions from the decision theoretic point of view. We give a short review of the most well known probabilistic criteria, and discuss examples of their use. We present a decision analytic framework for evaluating the criteria, and we analyse how the different criteria behave under incompleteness or uncertainty of the PSA model. As the conclusion of our analysis we give recommendations on the application of the criteria in different decision situations. (au)

Preferred information sources for clinical decision making: critical care nurses' perceptions of information accessibility and usefulness.

Science.gov (United States)

Marshall, Andrea P; West, Sandra H; Aitken, Leanne M

2011-12-01

Variability in clinical practice may result from the use of diverse information sources to guide clinical decisions. In routine clinical practice, nurses privilege information from colleagues over more formal information sources. It is not clear whether similar information-seeking behaviour is exhibited when critical care nurses make decisions about a specific clinical practice, where extensive practice variability exists alongside a developing research base. This study explored the preferred sources of information intensive care nurses used and their perceptions of the accessibility and usefulness of this information for making decisions in clinically uncertain situations specific to enteral feeding practice. An instrumental case study design, incorporating concurrent verbal protocols, Q methodology and focus groups, was used to determine intensive care nurses' perspectives of information use in the resolution of clinical uncertainty. A preference for information from colleagues to support clinical decisions was observed. People as information sources were considered most useful and most accessible in the clinical setting. Text and electronic information sources were seen as less accessible, mainly because of the time required to access the information within the documents. When faced with clinical uncertainty, obtaining information from colleagues allows information to be quickly accessed and applied within the context of a specific clinical presentation. Seeking information from others also provides opportunities for shared decision-making and potential validation of clinical judgment, although differing views may exacerbate clinical uncertainty. The social exchange of clinical information may meet the needs of nurses working in a complex, time-pressured environment but the extent of the evidence base for information passed through verbal communication is unclear. The perceived usefulness and accessibility of information is premised on the ease of use and access

Business rules formalisation for information systems

Directory of Open Access Journals (Sweden)

Ivana Rábová

2010-01-01

Full Text Available The article deals with relation business rules and business applications and describes a number of structures for support of information systems implementation and customization. Particular formats of structure are different according to different type of business rules. We arise from model of enterprise architecture that is a significant document of all what happens in business and serves for blueprint and facilitates of managers decisions. Most complicated part of enterprise architecture is business rule. When we gain its accurate formulation and when we achieve to formalize and to store business rule in special repository we can manage it actualize it and use it for many reasons. The article emphasizes formats of business rule formalization and its reference to business applications implementation.

Deepening the quality of clinical reasoning and decision-making in rural hospital nursing practice.

Science.gov (United States)

Sedgwick, M G; Grigg, L; Dersch, S

2014-01-01

Rural acute care nursing requires an extensive breadth and depth of knowledge as well as the ability to quickly reason through problems in order to make sound clinical decisions. This reasoning often occurs within an environment that has minimal medical or ancillary support. Registered nurses (RN) new to rural nursing, and employers, have raised concerns about patient safety while new nurses make the transition into rural practice. In addition, feeling unprepared for the rigors of rural hospital nursing practice is a central issue influencing RN recruitment and retention. Understanding how rural RNs reason is a key element for identifying professional development needs and may support recruitment and retention of skilled rural nurses. The purpose of this study was to explore how rural RNs reason through clinical problems as well as to assess the quality of such reasoning. This study used a non-traditional approach for data collection. Fifteen rural acute care nurses with varying years of experience working in southern Alberta, Canada, were observed while they provided care to patients of varying acuity within a simulated rural setting. Following the simulation, semi-structured interviews were conducted using a substantive approach to critical thinking. Findings revealed that the ability to engage in deep clinical reasoning varied considerably among participants despite being given the same information under the same circumstances. Furthermore, the number of years of experience did not seem to be directly linked to the ability to engage in sound clinical reasoning. Novice nurses, however, did rely heavily on others in their decision making in order to ensure they were making the right decision. Hence, their relationships with other staff members influenced their ability to engage in clinical reasoning and decision making. In situations where the patient's condition was deteriorating quickly, regardless of years of experience, all of the participants depended on

Designing business rules for mediation : a process towards agent-mediated business coordination

OpenAIRE

Zhao, Z.; Dignum, M.V.; Dignum, F.P.M.

2008-01-01

Business process integration is a very active research area, in which mediation is one of the fundamental architectural choices. Mediators have difficulties to design mediation services that meet the requirements of the different stakeholders. Business rules play an important role in the decision process of mediation. In this paper, we analyze the role of business rules in the decision process, and use some examples to illustrate how business rules should be designed in order to help the deci...

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

Science.gov (United States)

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Decision-making models in the analysis of portal films: a clinical pilot study

International Nuclear Information System (INIS)

See, A.; Johansen, J.; Hamilton, C.; Bydder, S.A.; Hawkins, J.; Roff, M.; Denham, J.; Kron, T.

2000-01-01

Portal films continue to play an important role in the verification of radiotherapy treatment. There is still some discussion, however, as to what action should be taken after a port film has shown a radiation field deviation from the prescribed volume. It was the aim of the present pilot study to investigate the performance of three decision-making models ('Amsterdam', 'Quebec' and 'Newcastle') and an expert panel basing their decision on intuition rather than formal rules after portal film acquisition in a clinical setting. Portal films were acquired on every day during the first week of treatment for five head and neck and five prostate cancer patients (diagnostic phase). If required, the field position was modified according to our normal practice following the recommendation of the expert panel. In order to analyse the results of the models, however, additional port films were taken in the following 3 treatment weeks with the patient moved as required by the different models (intervention phase). The portal films were taken over 4 consecutive days, positioning the patient according to each of the different models on one day each. None of the models diagnosed a field misplacement in the head and neck patients, while the 'Amsterdam' and 'Quebec' models predicted a move in one prostate patient. The 'Newcastle' model, which is based on Hotelling's T 2 statistic, proved to be more sensitive and diagnosed a systematic displacement for three prostate patients. The intervention phase confirmed the diagnosis of the model, even if the three portal films taken with the patient position adjusted as required by the model proved to be insufficient to demonstrate an improvement. The 'Newcastle' model does not rely on assumptions about the random movement of patients and requires five portal films before a decision can be reached. This approach lends itself well to incorporation into electronic portal imaging 'packages', where repeated image acquisitions present no logistical

Dual Criteria Decisions

DEFF Research Database (Denmark)

Andersen, Steffen; Harrison, Glenn W.; Lau, Morten Igel

2014-01-01

The most popular models of decision making use a single criterion to evaluate projects or lotteries. However, decision makers may actually consider multiple criteria when evaluating projects. We consider a dual criteria model from psychology. This model integrates the familiar tradeoffs between...... to the clear role that income thresholds play in such decision making, but does not rule out a role for tradeoffs between risk and utility or probability weighting....

The interaction of patient race, provider bias, and clinical ambiguity on pain management decisions.

Science.gov (United States)

Hirsh, Adam T; Hollingshead, Nicole A; Ashburn-Nardo, Leslie; Kroenke, Kurt

2015-06-01

Although racial disparities in pain care are widely reported, much remains to be known about the role of provider and contextual factors. We used computer-simulated patients to examine the influence of patient race, provider racial bias, and clinical ambiguity on pain decisions. One hundred twenty-nine medical residents/fellows made assessment (pain intensity) and treatment (opioid and nonopioid analgesics) decisions for 12 virtual patients with acute pain. Race (black/white) and clinical ambiguity (high/low) were manipulated across vignettes. Participants completed the Implicit Association Test and feeling thermometers, which assess implicit and explicit racial biases, respectively. Individual- and group-level analyses indicated that race and ambiguity had an interactive effect on providers' decisions, such that decisions varied as a function of ambiguity for white but not for black patients. Individual differences across providers were observed for the effect of race and ambiguity on decisions; however, providers' implicit and explicit biases did not account for this variability. These data highlight the complexity of racial disparities and suggest that differences in care between white and black patients are, in part, attributable to the nature (ie, ambiguity) of the clinical scenario. The current study suggests that interventions to reduce disparities should differentially target patient, provider, and contextual factors. This study examined the unique and collective influence of patient race, provider racial bias, and clinical ambiguity on providers' pain management decisions. These results could inform the development of interventions aimed at reducing disparities and improving pain care. Copyright © 2015 American Pain Society. Published by Elsevier Inc. All rights reserved.

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.

Variation in clinical decision-making for induction of labour: a qualitative study.

Science.gov (United States)

Nippita, Tanya A; Porter, Maree; Seeho, Sean K; Morris, Jonathan M; Roberts, Christine L

2017-09-22

Unexplained variation in induction of labour (IOL) rates exist between hospitals, even after accounting for casemix and hospital differences. We aimed to explore factors that influence clinical decision-making for IOL that may be contributing to the variation in IOL rates between hospitals. We undertook a qualitative study involving semi-structured, audio-recorded interviews with obstetricians and midwives. Using purposive sampling, participants known to have diverse opinions on IOL were selected from ten Australian maternity hospitals (based on differences in hospital IOL rate, size, location and case-mix complexities). Transcripts were indexed, coded, and analysed using the Framework Approach to identify main themes and subthemes. Forty-five participants were interviewed (21 midwives, 24 obstetric medical staff). Variations in decision-making for IOL were based on the obstetrician's perception of medical risk in the pregnancy (influenced by the obstetrician's personality and knowledge), their care relationship with the woman, how they involved the woman in decision-making, and resource availability. The role of a 'gatekeeper' in the procedural aspects of arranging an IOL also influenced decision-making. There was wide variation in the clinical decision-making practices of obstetricians and less accountability for decision-making in hospitals with a high IOL rate, with the converse occurring in hospitals with low IOL rates. Improved communication, standardised risk assessment and accountability for IOL offer potential for reducing variation in hospital IOL rates.

How Politics Shapes the Growth of Rules

DEFF Research Database (Denmark)

Jakobsen, Mads Leth Felsager; Mortensen, Peter Bjerre

2015-01-01

when, why, and how political factors shape changes in the stock of rules. Furthermore, we test these hypotheses on a unique, new data set based on all Danish primary legislation and administrative rules from 1989 to 2011 categorized into 20 different policy domains. The analysis shows......This article examines the impact of politics on governmental rule production. Traditionally, explanations of rule dynamics have focused on nonpolitical factors such as the self-evolvement of rules, environmental factors, and decision maker attributes. This article develops a set of hypotheses about...... that the traditional Weberian “rules breed rules” explanations must be supplemented with political explanations that take party ideology and changes in the political agenda into account. Moreover, the effect of political factors is indistinguishable across changes in primary laws and changes in administrative rules...

Prediction value of the Canadian CT head rule and the New Orleans criteria for positive head CT scan and acute neurosurgical procedures in minor head trauma: a multicenter external validation study.

Science.gov (United States)

Bouida, Wahid; Marghli, Soudani; Souissi, Sami; Ksibi, Hichem; Methammem, Mehdi; Haguiga, Habib; Khedher, Sonia; Boubaker, Hamdi; Beltaief, Kaouthar; Grissa, Mohamed Habib; Trimech, Mohamed Naceur; Kerkeni, Wiem; Chebili, Nawfel; Halila, Imen; Rejeb, Imen; Boukef, Riadh; Rekik, Noureddine; Bouhaja, Bechir; Letaief, Mondher; Nouira, Semir

2013-05-01

The New Orleans Criteria and the Canadian CT Head Rule have been developed to decrease the number of normal computed tomography (CT) results in mild head injury. We compare the performance of both decision rules for identifying patients with intracranial traumatic lesions and those who require an urgent neurosurgical intervention after mild head injury. This was an observational cohort study performed between 2008 and 2011 on patients with mild head injury who were aged 10 years or older. We collected prospectively clinical head CT scan findings and outcome. Primary outcome was need for neurosurgical intervention, defined as either death or craniotomy, or the need of intubation within 15 days of the traumatic event. Secondary outcome was the presence of traumatic lesions on head CT scan. New Orleans Criteria and Canadian CT Head Rule decision rules were compared by using sensitivity specifications and positive and negative predictive value. We enrolled 1,582 patients. Neurosurgical intervention was performed in 34 patients (2.1%) and positive CT findings were demonstrated in 218 patients (13.8%). Sensitivity and specificity for need for neurosurgical intervention were 100% (95% confidence interval [CI] 90% to 100%) and 60% (95% CI 44% to 76%) for the Canadian CT Head Rule and 82% (95% CI 69% to 95%) and 26% (95% CI 24% to 28%) for the New Orleans Criteria. Negative predictive values for the above-mentioned clinical decision rules were 100% and 99% and positive values were 5% and 2%, respectively, for the Canadian CT Head Rule and New Orleans Criteria. Sensitivity and specificity for clinical significant head CT findings were 95% (95% CI 92% to 98%) and 65% (95% CI 62% to 68%) for the Canadian CT Head Rule and 86% (95% CI 81% to 91%) and 28% (95% CI 26% to 30%) for the New Orleans Criteria. A similar trend of results was found in the subgroup of patients with a Glasgow Coma Scale score of 15. For patients with mild head injury, the Canadian CT Head Rule had higher

mHealth for Clinical Decision-Making in Sub-Saharan Africa : A Scoping Review

NARCIS (Netherlands)

Adepoju, Ibukun-Oluwa Omolade; Albersen, Bregje Joanna Antonia; De Brouwere, Vincent; van Roosmalen, Jos; Zweekhorst, M.B.M.

2017-01-01

BACKGROUND: In a bid to deliver quality health services in resource-poor settings, mobile health (mHealth) is increasingly being adopted. The role of mHealth in facilitating evidence-based clinical decision-making through data collection, decision algorithms, and evidence-based guidelines, for

Exploring the use of Option Grid™ patient decision aids in a sample of clinics in Poland.

Science.gov (United States)

Scalia, Peter; Elwyn, Glyn; Barr, Paul; Song, Julia; Zisman-Ilani, Yaara; Lesniak, Monika; Mullin, Sarah; Kurek, Krzysztof; Bushell, Matt; Durand, Marie-Anne

2018-05-29

Research on the implementation of patient decision aids to facilitate shared decision making in clinical settings has steadily increased across Western countries. A study which implements decision aids and measures their impact on shared decision making has yet to be conducted in the Eastern part of Europe. To study the use of Option Grid TM patient decision aids in a sample of Grupa LUX MED clinics in Warsaw, Poland, and measure their impact on shared decision making. We conducted a pre-post interventional study. Following a three-month period of usual care, clinicians from three Grupa LUX MED clinics received a one-hour training session on how to use three Option Grid TM decision aids and were provided with copies for use for four months. Throughout the study, all eligible patients were asked to complete the three-item CollaboRATE patient-reported measure of shared decision making after their clinical encounter. CollaboRATE enables patients to assess the efforts clinicians make to: (i) inform them about their health issues; (ii) listen to 'what matters most'; (iii) integrate their treatment preference in future plans. A Hierarchical Logistic Regression model was performed to understand which variables had an effect on CollaboRATE. 2,048 patients participated in the baseline phase; 1,889 patients participated in the intervention phase. Five of the thirteen study clinicians had a statistically significant increase in their CollaboRATE scores (pOption Grid TM helped some clinicians practice shared decision making as reflected in CollaboRATE scores, but most clinicians did not have a significant increase in their scores. Our study indicates that the effect of these interventions may be dependent on clinic contexts and clinician engagement. Copyright © 2018. Published by Elsevier GmbH.

Enhancing decision making about participation in cancer clinical trials: development of a question prompt list

Science.gov (United States)

Brown, Richard F.; Shuk, Elyse; Leighl, Natasha; Butow, Phyllis; Ostroff, Jamie; Edgerson, Shawna; Tattersall, Martin

2016-01-01

Purpose Slow accrual to cancer clinical trials impedes the progress of effective new cancer treatments. Poor physician–patient communication has been identified as a key contributor to low trial accrual. Question prompt lists (QPLs) have demonstrated a significant promise in facilitating communication in general, surgical, and palliative oncology settings. These simple patient interventions have not been tested in the oncology clinical trial setting. We aimed to develop a targeted QPL for clinical trials (QPL-CT). Method Lung, breast, and prostate cancer patients who either had (trial experienced) or had not (trial naive) participated in a clinical trial were invited to join focus groups to help develop and explore the acceptability of a QPL-CT. Focus groups were audio-recorded and transcribed. A research team, including a qualitative data expert, analyzed these data to explore patients’ decision-making processes and views about the utility of the QPL-CT prompt to aid in trial decision making. Results Decision making was influenced by the outcome of patients’ comparative assessment of perceived risks versus benefits of a trial, and the level of trust patients had in their doctors’ recommendation about the trial. Severity of a patient’s disease influenced trial decision making only for trial-naive patients. Conclusion Although patients were likely to prefer a paternalistic decision-making style, they expressed valuation of the QPL as an aid to decision making. QPL-CT utility extended beyond the actual consultation to include roles both before and after the clinical trial discussion. PMID:20593202

78 FR 36434 - Revisions to Rules of Practice

Science.gov (United States)

2013-06-18

... federal holidays, make grammatical corrections, and remove the reference to part-day holidays. Rule 3001... section, the following categories of persons are designated ``decision-making personnel'': (i) The.... The following categories of person are designated ``non-decision-making personnel'': (i) All...

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.

Adaptive clinical trial designs with pre-specified rules for modifying the sample size: understanding efficient types of adaptation.

Science.gov (United States)

Levin, Gregory P; Emerson, Sarah C; Emerson, Scott S

2013-04-15

Adaptive clinical trial design has been proposed as a promising new approach that may improve the drug discovery process. Proponents of adaptive sample size re-estimation promote its ability to avoid 'up-front' commitment of resources, better address the complicated decisions faced by data monitoring committees, and minimize accrual to studies having delayed ascertainment of outcomes. We investigate aspects of adaptation rules, such as timing of the adaptation analysis and magnitude of sample size adjustment, that lead to greater or lesser statistical efficiency. Owing in part to the recent Food and Drug Administration guidance that promotes the use of pre-specified sampling plans, we evaluate alternative approaches in the context of well-defined, pre-specified adaptation. We quantify the relative costs and benefits of fixed sample, group sequential, and pre-specified adaptive designs with respect to standard operating characteristics such as type I error, maximal sample size, power, and expected sample size under a range of alternatives. Our results build on others' prior research by demonstrating in realistic settings that simple and easily implemented pre-specified adaptive designs provide only very small efficiency gains over group sequential designs with the same number of analyses. In addition, we describe optimal rules for modifying the sample size, providing efficient adaptation boundaries on a variety of scales for the interim test statistic for adaptation analyses occurring at several different stages of the trial. We thus provide insight into what are good and bad choices of adaptive sampling plans when the added flexibility of adaptive designs is desired. Copyright © 2012 John Wiley & Sons, Ltd.

Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets.

Science.gov (United States)

Chen, Jonathan H; Alagappan, Muthuraman; Goldstein, Mary K; Asch, Steven M; Altman, Russ B

2017-06-01

Determine how varying longitudinal historical training data can impact prediction of future clinical decisions. Estimate the "decay rate" of clinical data source relevance. We trained a clinical order recommender system, analogous to Netflix or Amazon's "Customers who bought A also bought B..." product recommenders, based on a tertiary academic hospital's structured electronic health record data. We used this system to predict future (2013) admission orders based on different subsets of historical training data (2009 through 2012), relative to existing human-authored order sets. Predicting future (2013) inpatient orders is more accurate with models trained on just one month of recent (2012) data than with 12 months of older (2009) data (ROC AUC 0.91 vs. 0.88, precision 27% vs. 22%, recall 52% vs. 43%, all P<10 -10 ). Algorithmically learned models from even the older (2009) data was still more effective than existing human-authored order sets (ROC AUC 0.81, precision 16% recall 35%). Training with more longitudinal data (2009-2012) was no better than using only the most recent (2012) data, unless applying a decaying weighting scheme with a "half-life" of data relevance about 4 months. Clinical practice patterns (automatically) learned from electronic health record data can vary substantially across years. Gold standards for clinical decision support are elusive moving targets, reinforcing the need for automated methods that can adapt to evolving information. Prioritizing small amounts of recent data is more effective than using larger amounts of older data towards future clinical predictions. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Age-related variance in decisions under ambiguity is explained by changes in reasoning, executive functions, and decision-making under risk.

Science.gov (United States)

Schiebener, Johannes; Brand, Matthias

2017-06-01

Previous literature has explained older individuals' disadvantageous decision-making under ambiguity in the Iowa Gambling Task (IGT) by reduced emotional warning signals preceding decisions. We argue that age-related reductions in IGT performance may also be explained by reductions in certain cognitive abilities (reasoning, executive functions). In 210 participants (18-86 years), we found that the age-related variance on IGT performance occurred only in the last 60 trials. The effect was mediated by cognitive abilities and their relation with decision-making performance under risk with explicit rules (Game of Dice Task). Thus, reductions in cognitive functions in older age may be associated with both a reduced ability to gain explicit insight into the rules of the ambiguous decision situation and with failure to choose the less risky options consequently after the rules have been understood explicitly. Previous literature may have underestimated the relevance of cognitive functions for age-related decline in decision-making performance under ambiguity.

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

Towards patient-centered colorectal cancer surgery : focus on risks, decisions and clinical auditing

NARCIS (Netherlands)

Snijders, Heleen Simone

2014-01-01

The aim of this thesis was to explore several aspects of both clinical decision making and quality assessment in colorectal cancer surgery. Part one focusses on benefits and risks of treatment options, preoperative information provision and Shared Decision Making (SDM); part two investigates changes

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

Clinical decision making and mental health service use in people with severe mental illness across Europe

OpenAIRE

Cosh, S.; Zentner, N.; Ay, E.; Loos, S.; Slade, Mike; Maj, Mario; Salzano, A.; Berecz, R.; Glaub, T.; Munk-Jørgensen, Povl; Krogsgaard Bording, M.; Rössler, Wulf; Kawohl, Wolfram; Puschner, Bernd

2017-01-01

Objective: This study aims to explore relationships between preferred and experienced clinical decision making with service use, and associated costs, by people with severe mental illness.\\ud Methods: Prospective observational study of mental healthcare in six European countries: Germany, UK, Italy Hungary, Denmark and Switzerland. Patients (N = 588) and treating clinicians (N = 213) reported preferred and experienced decision making at baseline using the Clinical Decision Making Style Scale ...

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...

Shared decision making in mental health: the importance for current clinical practice.

Science.gov (United States)

Alguera-Lara, Victoria; Dowsey, Michelle M; Ride, Jemimah; Kinder, Skye; Castle, David

2017-12-01

We reviewed the literature on shared decision making (regarding treatments in psychiatry), with a view to informing our understanding of the decision making process and the barriers that exist in clinical practice. Narrative review of published English-language articles. After culling, 18 relevant articles were included. Themes identified included models of psychiatric care, benefits for patients, and barriers. There is a paucity of published studies specifically related to antipsychotic medications. Shared decision making is a central part of the recovery paradigm and is of increasing importance in mental health service delivery. The field needs to better understand the basis on which decisions are reached regarding psychiatric treatments. Discrete choice experiments might be useful to inform the development of tools to assist shared decision making in psychiatry.

Grooming a CAT: customizing CAT administration rules to increase response efficiency in specific research and clinical settings.