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Privacy-Preserving and Communication-Efficient Causal Inference for Hospital Quality Measurement

by   Larry Han, et al.
Harvard University

Data sharing can improve hospital quality measurement, but sharing patient-level data between hospitals is often infeasible due to privacy concerns. Motivated by the problem of evaluating the quality of care provided by candidate Cardiac Centers of Excellence (CCE), we propose a federated causal inference framework to safely leverage information from peer hospitals to improve the precision of quality estimates for a target hospital. We develop a federated doubly robust estimator that is privacy-preserving (requiring only summary statistics be shared between hospitals) and communication-efficient (requiring only one round of communication between hospitals). We contribute to the quality measurement and causal inference literatures by developing a framework for assessing treatment-specific performance in hospitals without sharing patient-level data. We also propose a penalized regression approach on summary statistics of the influence functions for efficient estimation and valid inference. In so doing, the proposed estimator is data-adaptive, downweighting hospitals with different case-mixes from the target hospital for bias reduction and upweighting hospitals with similar case-mixes for efficiency gain. We show the improved performance of the federated global estimator in extensive simulation studies. Studying candidate CCE, we find that the federated global estimator improves precision of treatment effect estimates by 34% to 86% for target hospitals, qualitatively altering the evaluation of the percutaneous coronary intervention (PCI) treatment effect in 22 of 51 hospitals. Focusing on treatment-specific rankings, we find that hospitals rarely excel in both PCI and medical management (MM), stressing the importance of treatment-specific performance assessments.


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