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A Comparison of Bayesian Methods for Profiling Hospital Performance

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  • Peter C. Austin

    (Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada and the Department of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada)

Abstract

There is a growing interest in the use of Bayesian methods for profiling institutional performance. In the literature, several studies have compared different frequentist methods for classifying hospitals as performance outliers. The purpose of this study was to compare 4 different Bayesian methods for classifying hospitals as outcomes outliers, using 30-day hospital-level mortality rates for a cohort of acute myocardial infarction patients as a test case. The 1st Bayesian method involved determining the probability that a hospital’s mortality rate for an average patient exceeded a specified threshold. The 2nd method involved ranking hospitals according to their mortality rate for an average patient. The 3rd method involved determining the probability that a hospital’s standardized mortality ratio exceeded a specified threshold. The 4th method involved ranking hospitals according to their standardized mortality ratio. In most of the scenarios examined, there was only marginal agreement between the different methods. In only 4 of 19 comparisons, was there good agreement between the different methods (0.40 kappa 0.75). Methods based on ranking institutions were relatively insensitive to differences between hospitals. These inconsistencies raise questions about the choice of methods for classifying hospital performance, and they suggest a need for urgent research into which methods are best able to discriminate between institutions and which are most meaningful to decision makers.

Suggested Citation

  • Peter C. Austin, 2002. "A Comparison of Bayesian Methods for Profiling Hospital Performance," Medical Decision Making, , vol. 22(2), pages 163-172, April.
  • Handle: RePEc:sae:medema:v:22:y:2002:i:2:p:163-172
    DOI: 10.1177/0272989X0202200213
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    1. Lisa I. Iezzoni & Michael Shwartz & Arlene S. Ash & Yevgenia D. Mackiernan, 1996. "Predicting In-hospital Mortality for Stroke Patients," Medical Decision Making, , vol. 16(4), pages 348-356, October.
    2. Harvey Goldstein & David J. Spiegelhalter, 1996. "League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 385-409, May.
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    Cited by:

    1. Duen-Yian Yeh & Ching-Hsue Cheng, 2016. "Performance Management of Taiwan’s National Hospitals," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 187-213, January.
    2. Johannes Hengelbrock & Johannes Rauh & Jona Cederbaum & Maximilian Kähler & Michael Höhle, 2023. "Hospital profiling using Bayesian decision theory," Biometrics, The International Biometric Society, vol. 79(3), pages 2757-2769, September.
    3. David I. Ohlssen & Linda D. Sharples & David J. Spiegelhalter, 2007. "A hierarchical modelling framework for identifying unusual performance in health care providers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 865-890, October.

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