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Competing Practice Guidelines: Using Cost-Effectiveness Analysis To Make Optimal Decisions

Attilio V. Granata, MD, MBA; and Alan L. Hillman, MD, MBA
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From Yale School of Medicine, New Haven, Connecticut; and University of Pennsylvania School of Medicine and the Wharton School, Philadelphia, Pennsylvania. Disclaimer: This paper, including the optimization model, was developed before Dr. Granata's affiliation with Oxford Health Plans, solely as an academic project to further research in cost-effectiveness analysis and health policy. There is no direct or indirect relation between the development or content of the paper and Oxford Health Plans or any other medical care system. Grant Support: During this project, Dr. Granata was supported in part by Bridgeport Hospital (Bridgeport, Connecticut) while at the Wharton Executive MBA Program from 1992 to 1994. Requests for Reprints: Attilio V. Granata, MD, MBA, Oxford Health Plans, 48 Monroe Turnpike, Trumbull, CT 06611. Current Author Addresses: Dr. Granata: Oxford Health Plans, 48 Monroe Turnpike, Trumbull, CT 06611.

Copyright ©2004 by the American College of Physicians

Ann Intern Med. 1998;128(1):56-63. doi:10.7326/0003-4819-128-1-199801010-00009
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In this paper, cost and effectiveness data for six clinical interventions are applied simultaneously to a hypothetical population of 100 000 patients to show how selecting guidelines to maximize overall population benefit compares with selecting the best guidelines for individual patients.By entering effectiveness (added survival) and cost information from recent prevention, screening, diagnostic, and therapeutic guidelines into a computer-based optimization model, the options that maximized overall population effectiveness while keeping additional cost within varying specified constraints were identified.

In 57% of selection opportunities, the clusters of guidelines that yielded maximum population benefit differed from those that maximized benefit for individual patients.Some choices were more stable than others over ranges of cost constraints.

Clinical practice guidelines chosen to maximize cost-effectiveness for individual patients often do not maximize cost-effectiveness for populations of patients. To allocate resources as efficiently as possible, decision makers should consider other sources of information in addition to the recommendations of specific practice guidelines. “Robust” guidelines that simultaneously address both individual and societal health benefit should be sought.


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Appendix Figure 1. Input data and clinical options selected in all cost-contained trials of the optimization model. *The most cost-effective option for each individual clinical subgroup is given in boldface. †Estimated demand for each option during 12 months for 100 000 patients similar in age and sex to the U.S. population in 1991. ‡Values for each alternative as compared with baseline: cost in 1991 U.S. dollars, effectiveness in years of life (both discounted at 5% per year). §Absence of shading in a cell indicates that the baseline option was chosen for the clinical subgroup during the trial. AICD = automatic implantable cardioverter-defibrillator; angio = coronary angiography; C/E = incremental cost per year of life; ETT = exercise treadmill test; FDA = U.S. Food and Drug Administration; FOBT = fecal occult blood testing; MD = physician; NA = not applicable; Rx = treatment; sig = flexible sigmoidoscopy; ST = ST segment.
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