Bayesian and Frequentist Methods for Provider Profiling Using Risk-Adjusted Assessments of Medical Outcomes

Abstract

“Provider profiling” is the evaluation of the performance of hospitals, doctors, and other medical practitioners to enhance the quality of medical care. We propose a new method and compare conventional and Bayesian methodologies that are used or proposed for use for such “report cards.” Conventional statistical approaches to these provider assessments use likelihood-based frequentist methodologies, and the new Bayesian method is patterned after these. For each of three models, we compare the frequentist and Bayesian approaches using data used by the New York State Department of Health for its annually released reports that profile hospitals permitted to perform coronary artery bypass graft surgery. We use additional, constructed data sets to sharpen our conclusions. Comparisons across methods associated with different models are important because of current proposals to use random-effects (exchangeable) models for provider profiling. We also summarize and discuss important issues in the conduct of provider profiling, such as inclusion of provider characteristics in the model and choice of criteria for determining unsatisfactory performance.