Combining Expert Judgment by Hierarchical Modeling: an Application to Physician Staffing

Expert panels are playing an increasingly important role in U.S. health policy decision making. A fundamental issue in these applications is how to synthesize the judgments of individual experts into a group judgment. In this paper we propose an approach to synthesis based on Bayesian hierarchical models, and apply it to the problem of determining physician sta ng at medical centers operated by the U.S. Department of Veteran A airs (VA). Our starting point is the so-called supra-Bayesian approach to synthesis, whose principal motivation in the present context is to generate an estimate of the uncertainty associated with a panel's evaluation of the number of physicians required under speci ed conditions. Hierarchical models are particularly natural in this context since variability in the experts' judgments results in part from heterogeneity in their baseline experiences at di erent VA medical centers. We derive alternative hierarchical Bayes synthesis distributions for the number of physicians required to handle the (service-mix speci c) daily workload in internal medicine at a given VA medical center (VAMC). The analysis below appears to be the rst to provide a statistical treatment of expert judgment processes for evaluating the appropriate use of resources in health care. Also, while hierarchical models are well established, their application to the synthesis of expert judgment is novel. (OPINIONPOOLING; HIERARCHICALMODELS; BAYESIAN INFERENCE; HEALTH WORKFORCE REQUIREMENTS.) 1