June 2023 Hospital quality risk standardization via approximate balancing weights
Luke J. Keele, Eli Ben-Michael, Avi Feller, Rachel Kelz, Luke Miratrix
Author Affiliations +
Ann. Appl. Stat. 17(2): 901-928 (June 2023). DOI: 10.1214/22-AOAS1629

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 simply because the hospital serves a healthier population overall. In this paper we develop a method of “direct standardization” where we reweight 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 uncertainty 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.

Funding Statement

Eli Ben-Michael and Avi Feller gratefully are funded by National Science Foundation Grant #1745640.
Rachel Kelz is funded by a grant from the National Institute on Aging, R01AG049757- 01A1.

Acknowledgements

We thank Peng Ding, Skip Hirshberg, and Sam Pimentel for helpful feedback as well as seminar participants in the Penn Causal Group. The dataset used for this study was purchased with a grant from the Society of American Gastrointestinal and Endoscopic Surgeons. Although the AMA Physician Masterfile data is the source of the raw physician data, the tables and tabulations were prepared by the authors and do not reflect the work of the AMA. The Pennsylvania Health Cost Containment Council (PHC4) is an independent state agency responsible for addressing the problems of escalating health costs, ensuring the quality of health care, and increasing access to health care for all citizens. While PHC4 has provided data for this study, PHC4 specifically disclaims responsibility for any analyses, interpretations, or conclusions. Some of the data used to produce this publication was purchased from or provided by the New York State Department of Health (NYSDOH) Statewide Planning and Research Cooperative System (SPARCS). However, the conclusions derived, and views expressed herein are those of the authors and do not reflect the conclusions or views of NYSDOH. NYSDOH, its employees, officers, and agents make no representation, warranty, or guarantee as to the accuracy, completeness, currency, or suitability of the information provided here. This publication was derived, in part, from a limited data set supplied by the Florida Agency for Health Care Administration (AHCA) which specifically disclaims responsibility for any analysis, interpretations, or conclusions that may be created as a result of the limited data set. The authors declare no conflicts.

Citation

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Luke J. Keele. Eli Ben-Michael. Avi Feller. Rachel Kelz. Luke Miratrix. "Hospital quality risk standardization via approximate balancing weights." Ann. Appl. Stat. 17 (2) 901 - 928, June 2023. https://doi.org/10.1214/22-AOAS1629

Information

Received: 1 February 2021; Revised: 1 March 2022; Published: June 2023
First available in Project Euclid: 1 May 2023

MathSciNet: MR4582697
zbMATH: 07692367
Digital Object Identifier: 10.1214/22-AOAS1629

Keywords: direct standardization , risk adjustment , weighting

Rights: Copyright © 2023 Institute of Mathematical Statistics

Vol.17 • No. 2 • June 2023
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