Privacy-Preserving, Communication-Efficient, and Target-Flexible Hospital Quality Measurement (2203.00768v3)

Abstract: Integrating information from multiple data sources can enable more precise, timely, and generalizable decisions. However, it is challenging to make valid causal inferences using observational data from multiple data sources. For example, in healthcare, learning from electronic health records contained in different hospitals is desirable but difficult due to heterogeneity in patient case mix, differences in treatment guidelines, and data privacy regulations that preclude individual patient data from being pooled. Motivated to overcome these issues, 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 implement our causal estimation and inference procedure to 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 59% to 91% 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 63% (32 of 51) of hospitals. We find that hospitals rarely excel in both PCI and MM, which highlights the importance of assessing performance on specific treatment regimens.