An Evidence-Based Incentive System for Medicare’s End-Stage Renal Disease Program

Management Science 58:6:1092-1105 (2012)

33 Pages Posted: 27 Nov 2020

See all articles by Donald Lee

Donald Lee

Emory University - Goizueta Business School; Emory University - Dept of Biostatistics & Bioinformatics

Stefanos A. Zenios

Stanford Graduate School of Business

Date Written: 2012

Abstract

Recent legislations directed Medicare to revamp its decades-old system for reimbursing dialysis treatments, with focus on the risk adjustment of payments and on the transition toward a pay-for-compliance system. To design an optimal payment system that incorporates these features, we develop an empirical method to estimate the structural parameters of the principal-agent model underlying Medicare's dialysis payment system. We use the model and parameter estimates to answer the following questions: Can a pay-for-compliance system based only on the intermediate performance measures currently identified by Medicare achieve first-best? How should patient outcomes be risk adjusted, and what welfare gains can be achieved by doing so? Our main findings are as follows: 1) the current set of intermediate measures identified by Medicare are not comprehensive enough for use alone in a pay-for-compliance system; 2) paying for risk-adjusted downstream outcomes instead of raw downstream outcomes can lengthen the hospital-free life of admitted patients by two weeks per patient per year without increasing Medicare expenditures.

Keywords: Healthcare pay-for-performance, dialysis, evidence-based mechanism design, structural estimation

JEL Classification: C01, I13, I18

Suggested Citation

Lee, Donald and Lee, Donald and Zenios, Stefanos A., An Evidence-Based Incentive System for Medicare’s End-Stage Renal Disease Program (2012). Management Science 58:6:1092-1105 (2012), Available at SSRN: https://ssrn.com/abstract=3708480

Donald Lee (Contact Author)

Emory University - Goizueta Business School ( email )

1300 Clifton Road
Atlanta, GA 30322-2722
United States

Emory University - Dept of Biostatistics & Bioinformatics ( email )

Atlanta, GA 30322
United States

Stefanos A. Zenios

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

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