Characterizing Rational Transplant Program Response to Outcome-Based Regulation

David Mildebrath (), Taewoo Lee (), Saumya Sinha (), Andrew J. Schaefer () and A. Osama Gaber ()
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David Mildebrath: Department of Computational Applied Mathematics and Operations Research, Rice University, Houston, Texas 77005
Taewoo Lee: Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
Saumya Sinha: Department of Industrial & Systems Engineering, University of Minnesota, Minneapolis, Minnesota 55455
Andrew J. Schaefer: Department of Computational Applied Mathematics and Operations Research, Rice University, Houston, Texas 77005
A. Osama Gaber: Department of Surgery, Houston Methodist Hospital, Houston, Texas 77030

Operations Research, 2024, vol. 72, issue 4, 1421-1437

Abstract: Organ transplantation is an increasingly common therapy for many types of end-stage organ failure, including lungs, hearts, kidneys, and livers. The last 20 years have seen increased scrutiny of posttransplant outcomes in the United States to ensure the efficient utilization of the scarce organ supply. Under regulations by the Organ Procurement Transplantation Network (OPTN) and Centers for Medicare and Medicaid Services (CMS), the United States has seen a rise in risk-averse patient selection among transplant programs, resulting in decreased transplantation volume for some programs. Despite this observed decrease, the response of transplant programs to OPTN/CMS regulations remains poorly understood. In this work, we consider the perspective of a transplant program that seeks to simultaneously maximize transplant volume and control the risk of OPTN/CMS penalization. Using a chance-constrained mixed-integer programming model, we demonstrate that under certain conditions, it may be rational for a transplant program to curtail its transplant volume to avoid penalization under OPTN/CMS regulations. This finding, which confirms empirical results observed in the clinical literature, suggests that such regulations may be inherently unsuitable for use in incentivizing improved program performance. We also highlight other structural shortcomings of OPTN/CMS regulations that have not been observed previously in the literature.

Keywords: Policy Modeling and Public Sector OR; lung transplantation; pay for performance; chance constraints; healthcare policy (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:72:y:2024:i:4:p:1421-1437

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