Privacy-preserving, communication-efficient, and target-flexible hospital quality measurement

Abstract

Accurate hospital performance measurement is important to both patients and providers but is challenging due to case-mix heterogeneity, differences in treatment guidelines, and data privacy regulations that preclude the sharing of individual patient data. Motivated to overcome these issues in the setting of hospital quality measurement, we develop a federated causal inference framework. We devise a doubly robust estimator of the mean potential outcome in a target population and show that it is consistent even when some models are misspecified. To enable real-world use, our proposed algorithm is privacy-preserving (requiring only summary statistics to be shared between hospitals) and communication-efficient (requiring only one round of communication between hospitals). We show that our estimator has good finite sample properties in simulation studies. We investigate the quality of hospital care provided by a diverse set of 51 candidate Cardiac Centers of Excellence, as measured by 30-day mortality and length of stay for acute myocardial infarction (AMI) patients. We find that our proposed federated global estimator improves the precision of treatment effect estimates by 34% to 86%, compared to using data from the target hospital alone. This precision gain results in qualitatively different conclusions about the estimated effect of percutaneous coronary intervention (PCI), compared to medical management (MM) in 43% (22 of 51) of hospitals.