Effects of a Decision Support System on the Diagnostic Accuracy of Users: A Preliminary Report

Elstein, Arthur S.; Friedman, Charles P.; Wolf, Fredric M.; Murphy, Gwendolyn C.; Miller, Jason; Fine, Paul L.; Heckerling, Paul S.; Miller, Tom; Sisson, James C.; Barlas, Sema; Biolsi, Kevin; Ng, Macy; Mei, Xiao; Franz, Timothy M.; Capitano, Amy

doi:10.1136/jamia.1996.97084515

Cited by 30 publications

(23 citation statements)

References 8 publications

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“…Second, the prediction of criminal recidivism, one of the earliest applications, has likewise grown (e.g., Hanson & Thornton, 2000). Third and finally, the use of automated decision rules and aids in medicine has been adopted with moderate enthusiasm as one aspect of "Evidence Based Medicine" (Elstein et al, 1996).…”

Section: Importance Of Paul's Ideas and Conclusion Todaymentioning

confidence: 99%

Clinical versus statistical prediction: The contribution of Paul E. Meehl

Grove

2005

J Clin Psychol

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The background of Paul E. Meehl's work on clinical versus statistical prediction is reviewed, with detailed analyses of his arguments. Meehl's four main contributions were the following: (a) he put the question, of whether clinical or statistical combinations of psychological data yielded better predictions, at center stage in applied psychology; (b) he convincingly argued, against an array of objections, that clinical versus statistical prediction was a real (not concocted) problem needing thorough study; (c) he meticulously and even-handedly dissected the logic of clinical inference from theoretical and probabilistic standpoints; and (c) he reviewed the studies available in 1954 and thereafter, which tested the validity of clinical versus statistical predictions. His early conclusion that the literature strongly favors statistical prediction has stood up extremely well, and his conceptual analyses of the prediction problem (especially his defense of applying aggregate-based probability statements to individual cases) have not been significantly improved since 1954.

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Section: Importance Of Paul's Ideas and Conclusion Todaymentioning

confidence: 99%

Clinical versus statistical prediction: The contribution of Paul E. Meehl

Grove

2005

J Clin Psychol

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“…Previous studies of these programs have focused on difficult, inpatient, or paper cases developed from a medical record after extensive clinical and laboratory data are available. [6][7][8][9] No study has assessed how these programs perform using the limited amount of data that are collected during an ED visit. The purpose of this study is to evaluate how well Quick Medical Reference (QMR Version 3.82, Knowledge Base 10-07-1998 Copyright University of Pittsburgh and The Hearst Corporation) and Iliad (Version 4.5 Copyright 1996 Applied Medical Informatics) parallel physician decision making in common ED situations.…”

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confidence: 99%

How well does decision support software perform in the emergency department?

Graber

VanScoy

2003

Emerg Med J

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Objective: To determine how well general decision support systems perform given the data collected in an emergency department (ED). Methods: A convenience sample of 25 patients was selected from those patients having a diagnostic question on presentation to the ED. All interactions with the patients were audiotaped and abstracted into a structured data form. All other data such as written notes, laboratory, and EKG results were also abstracted. All data were entered into two general diagnostic decision support programs (Quick Medical Reference (QMR Version 3.82, Knowledge Base 10-07-1998 Copyright University of Pittsburgh and The Hearst Corporation) and Iliad (Version 4.5 Copyright 1996 Applied Medical Informatics)). The diagnoses generated by the computer programs were compared with the final diagnoses of the ED attending. Results: The final ED diagnosis was found in the differential diagnosis generated by Iliad and QMR 72% and 52% of the time respectively. The final ED diagnosis was found in the top 10 diagnoses 51% and 44% of the time and in the top five diagnoses 36% and 32% of the time for each program respectively. This approximates to the performance of these programs in other clinical settings. Conclusions: Diagnostic decision support software has the same success in finding the "correct" diagnosis in the ED as in other clinical settings where more extensive clinical data are available. The accuracy is not sufficiently high to permit the use of these programs as an arbiter in any individual case. However, they may be useful, prompting additional investigation in particularly difficult cases.

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“…Hypothesis evaluation is the main stage of reasoning process, in which the data are weighted and combined to determine until one of the diagnostic hypotheses already generated can be confirmed. If not, the problem must be recycled, new hypotheses should be generated and additional data should be collected until the verification is achieved (1,(21)(22)(23). In general, an expert system should have the characteristics of high performance, sufficient and quick response time, reliability, comprehensibility and flexibility.…”

Section: Introductionmentioning

confidence: 99%

“…In general, an expert system should have the characteristics of high performance, sufficient and quick response time, reliability, comprehensibility and flexibility. Although these systems in medicine have some limitations such as communicating difficulties with patients, differences in evaluation of patients' data and its acceptability by physicians and patients, they have undeniable advantages of minimizing the cost, having accessible permanent knowledge, improving reliability and accuracy of diagnoses, minimizing human errors and solving complex problems in medicine efficiently (18,20,(23)(24)(25). The aim of this work is to develop a medical expert system for pulmonary diseases providing practitioners and medical students with the advantages of developing their ability, minimizing the error and cost in diagnosing and broadening their medical knowledge.…”

Section: Introductionmentioning

confidence: 99%