Empirical Bayes methods for estimating hospital‐specific mortality rates

TLDR

The study proposes alternative empirical Bayes methods for estimating hospital‑level Medicare mortality rates, differing from the current Health Care Finance Administration approach. An empirical Bayes framework with logistic regression adjustment for patient severity is applied to four disease conditions using national Medicare data lacking severity descriptors and supplemental severity‑rich samples. The model delivers more accurate estimates, mitigates multiple‑comparison issues, shows substantial between‑hospital variation in unadjusted rates, but adjusted rates are unreliable due to limited sample sizes.

Abstract

Abstract We present alternative methods for estimating hospital‐level mortality rates to those used by the Health Care Finance Administration for Medicare patients. We use an empirical Bayes model to represent the different sources of variation in observed hospital‐specific mortality rates and we use a logistic regression model to adjust for severity differences (in patient mix) across hospitals. In addition to providing a principled derivation of a standard error for the commonly used estimator, our fully model‐based formulation produces much more accurate estimates and resolves the severe problem of multiple comparisons that arises when extreme estimates are used to identify exceptional hospitals. We estimate models for each of four disease conditions using the national Medicare mortality data base which does not contain patient severity descriptors, and mortality data from national samples which do include patient severity descriptors. We find substantial between‐hospital variation in the unadjusted death rates from the national data base. Mortality rates differ substantially with patient severity in our models, but the sample sizes are too small to yield reliable estimates of the between‐hospital variation in adjusted mortality rates.