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  Vol. 2 No. 5, May 1993 TABLE OF CONTENTS
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Evaluation of Physician Decision Making With the Use of Prior Probabilities and a Decision-Analysis Model

Barry L. Carter, PharmD; C. David Butler, PharmD, MBA; John C. Rogers, MD, MPH; Richard L. Holloway, PhD

Arch Fam Med. 1993;2(5):529-534.


Abstract

Objectives
To determine whether treatment decisions could be influenced by supplying probabilities and whether these decisions would be consistent with a decision-analysis model.

Design
Survey with case scenarios and a computerized decision-analysis model.

Setting
Family practice residency program.

Participants
Forty family practice residents and faculty in the experimental group and six controls.

Interventions
Twelve case scenarios of patients with hypertension and coexisting diseases were developed. Family practice physicians were asked to rank their drugs of choice for each case. In the second phase, six case scenarios included probabilities for efficacy and adverse reactions of step 1 antihypertensives. These drug selections were compared with a computerized decision-analysis model.

Main Outcome Measures
Frequencies of matches between the drug selections of physicians and the computer model.

Results
The frequency of matches before probabilities were provided to physicians was low (45.6%) and there was a significant increase when probabilities were supplied (71.3%). Regardless of experience level, physicians increased their consistency with the computer model after probabilities were supplied.

Conclusions
This study demonstrated that physician decision making for antihypertensive therapy can be influenced by patient-specific probability estimates. Probability data can help less experienced residents make decisions that are comparable to those of attending physicians. This study was conducted in one residency program and the generalizability to the practicing physician is unknown. These findings would suggest that educational efforts in residency programs, health maintenance organizations, or group practices may benefit from patient-specific probabilities that assist with decisions for drug therapy interventions.



Author Affiliations

From Department of Family Medicine, Baylor College of Medicine, Houston, Tex (Drs Carter, Rogers, and Holloway); and Technology Information Service, the University Hospital Consortium, Oak Brook, Ill (Dr Butler). Dr Carter is now with the Department of Pharmacy Practice, College of Pharmacy, University of Illinois at Chicago, and Dr Holloway is now with the Community and Family Medicine Academic Programs, The Medical College of Wisconsin, Milwaukee.



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Arch Fam Med 1994;3:528-535.
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