Statistical Methods for Profiling Providers of Medical Care: Issues and Applications

Abstract

Recent public debate on costs and effectiveness of health care in the United States has generated a growing emphasis on “profiling” of medical care providers. The process of profiling involves comparing resource use and quality of care among medical providers to a community or a normative standard. This is valuable for targeting quality improvement strategies. For example, hospital profiles may be used to determine whether institutions deviate in important ways in the process of care they deliver. In this article we propose a class of performance indices to profile providers. These indices are based on posterior tail probabilities of relevant model parameters that indicate the degree of poor performance by a provider. We apply our performance indices to profile hospitals on the basis of 30-day mortality rates for a cohort of elderly heart attack patients. The analysis used data from 96 acute care hospitals located in one state and accounted for patient and hospital characteristics using a hierarchical logistic regression model. We used Markov chain Monte Carlo methods to fit the model and to obtain performance indices of interest. In particular, we estimated the posterior probability that mortality at the ith hospital is 1–1/2 times the median mortality rate over all the hospitals in the state. We also calculated the posterior probability that the deviation in average risk-adjusted and “standardized” mortality at the ith hospital is “large.” We compare the results of evaluating hospitals based on our performance indices to those obtained using conventional measures. With 30-day risk-adjusted mortality rates ranging from 12% to 14%, one-quarter of the hospitals had posterior probabilities that hospital-specific mortality was 1–1/2 times the median mortality rate greater than 15%. The posterior probability of a large difference between risk-adjusted and standardized mortality rates was less than 6% for three-quarters of the hospitals we examined. Although there were differences in the evaluation of each hospital by the various criteria, one hospital consistently emerged as having the worst performance by all criteria.

Key Words:

• Acute myocardial infarction
• Excess mortality
• Gibbs sampler
• Hierarchical regression model
• Posterior inference
• Quality of care