A Comparison of Bayesian Methods for Profiling Hospital Performance

Medical Decision Making - Tập 22 Số 2 - Trang 163-172 - 2002
Peter C. Austin1
1Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada and the Department of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada

Tóm tắt

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.

Từ khóa


Tài liệu tham khảo

Luft HS, 1993, Annual Report of the California Hospital Outcomes Project

Pennsylvania Health Care Cost Containment Council, 1996, Focus on Heart Attack in Pennsylvania. Research Methods and Results

Tu JV, 1999, Cardiovascular Health Services in Ontario: An ICES Atlas, 83

New York State Department of Health, 1992, Coronary Artery Bypass Graft Surgery in New York State 1989-1991

10.1056/NEJM199602083340611

Zinman D., 1991, Newsday, 7

Iezzoni LI, 1994, Risk Adjustment for Measuring Health Care Outcomes

10.1016/0003-4975(94)91721-3

10.1016/0895-4356(94)00126-B

10.1161/01.CIR.85.4.1254

10.1161/01.CIR.88.2.416

10.1016/0002-9149(88)90918-6

10.1378/chest.108.1.83

10.1056/NEJM198308113090602

10.7326/0003-4819-123-10-199511150-00004

10.1097/00005650-199601000-00002

10.1177/0272989X9601600405

Scottish Office, 1994, Clinical Outcome Indicators, 1994

10.1002/(SICI)1097-0258(19971215)16:23<2645::AID-SIM696>3.0.CO;2-D

10.1016/S0140-6736(97)09362-8

10.1080/01621459.1997.10474036

10.7326/0003-4819-127-8_Part_2-199710151-00065

10.1136/bmj.316.7146.1701

10.2307/2983325

10.1046/j.1365-2753.2001.00261.x

Morris CN, 1996, Bayesian Statistics 5, 277, 10.1093/oso/9780198523567.003.0015

Tu JV, 1999, Can Med Assoc J, 161, 1257

Cox JL, 1997, Can J Cardiol, 13, 351

10.1016/S0735-1097(01)01109-3

10.1080/01621459.1985.10478148

Spiegelhalter DJ, 1996, Markov Chain Monte Carlo in Practice, 21

Gilks WR, 1996, Markov Chain Monte Carlo in Practice, 1

10.2307/2348941

Geweke J., 1992, Bayesian Statistics 4, 169, 10.1093/oso/9780198522669.003.0010

10.1287/opre.31.6.1109

10.1080/00029238.1971.11080840

10.2307/2529310