Hospital Quality Risk Standardization via Approximate Balancing Weights

TL;DR

This paper introduces a method for standardizing hospital outcome comparisons by re-weighting patient populations to account for case mix differences, improving fairness and accuracy in hospital performance assessment.

Contribution

The paper develops a novel weighting approach based on survey sampling and causal inference to directly control for patient mix imbalance in hospital outcome comparisons.

Findings

Effective re-weighting reduces bias in hospital comparisons.

Method improves precision by tuning weights and using outcome modeling.

Applied to real data, the method provides standardized complication rates.

Abstract

Comparing outcomes across hospitals, often to identify underperforming hospitals, is a critical task in health services research. However, naive comparisons of average outcomes, such as surgery complication rates, can be misleading because hospital case mixes differ -- a hospital's overall complication rate may be lower due to more effective treatments or simply because the hospital serves a healthier population overall. In this paper, we develop a method of ``direct standardization'' where we re-weight each hospital patient population to be representative of the overall population and then compare the weighted averages across hospitals. Adapting methods from survey sampling and causal inference, we find weights that directly control for imbalance between the hospital patient mix and the target population, even across many patient attributes. Critically, these balancing weights can also be tuned to preserve sample size for more precise estimates. We also derive principled measures of statistical precision, and use outcome modeling and Bayesian shrinkage to increase precision and account for variation in hospital size. We demonstrate these methods using claims data from Pennsylvania, Florida, and New York, estimating standardized hospital complication rates for general surgery patients. We conclude with a discussion of how to detect low performing hospitals.