Mortality Rate Estimation and Standardization for Public Reporting: Medicare's Hospital Compare

TL;DR

This paper critiques and improves Medicare's hospital mortality rate models for heart attack patients by incorporating additional hospital factors and comparing standardization methods to enhance public reporting accuracy.

Contribution

It introduces revised models that include hospital volume, staffing, and procedure capabilities, and evaluates standardization techniques for better risk adjustment and interpretability.

Findings

Revised models better calibrate mortality predictions.

Direct standardization outperforms indirect standardization.

Inclusion of hospital characteristics improves model accuracy.

Abstract

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.