Hospital Mortality Rate Estimation for Public Reporting

Bayesian models are increasing fit to large administrative data sets and then used to make individualized recommendations. For instance, Medicare's Hospital Compare webpage provides information to patients about specific hospital mortality rates for a heart attack or Acute Myocardial Infarction (AMI). Hospital Compare's current recommendations are based on a random effects logit model with a random hospital indicator and patient risk factors. By checking the out of sample calibration of their individualized predictions against general empirical advice, we are led to substantial revisions of the Hospital Compare model for AMI mortality. As opposed to Hospital Compare, our revised models incorporate information about hospital volume, nursing staff, medical residents, and the hospital's ability to perform cardiovascular procedures, information that is clearly needed if a model is to make appropriately calibrated predictions. Additionally, we contrast several methods for summarizing a model's predictions for use by the public. We find that indirect standardization, as currently used by Hospital Compare, fails to adequately control for differences in patient risk factors, whereas direct standardization provides good control and is easy to interpret.