Optimal Outcome-Based Regulation in Lung Transplantation: Modeling and ComputationOptimal Outcome-Based Regulation in Lung Transplantation: Modeling and Computation

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dc.contributor.advisorSchaefer, Andrew Jen_US
dc.creatorMildebrath, David T. K.en_US
dc.date.accessioned2022-10-11T19:21:13Zen_US
dc.date.available2023-08-01T05:01:12Zen_US
dc.date.created2021-08en_US
dc.date.issued2021-06-15en_US
dc.date.submittedAugust 2021en_US
dc.date.updated2022-10-11T19:21:13Zen_US
dc.description.abstractOrgan transplantation is an increasingly common therapy for many types of end-stage organ failure, including heart disease, chronic obstructive pulmonary disease, and end-stage renal disease. Unfortunately, the supply of available organs has not kept pace with this increasing demand. In order to ensure the efficient utilization of scarce organs, the past 20 years has seen increased scrutiny of post-transplant outcomes in the United States. This scrutiny has come in the form of two sets of closely-related regulations, overseen by the Organ Procurement Transplantation Network (OPTN) and Centers for Medicare and Medicaid (CMS), which penalize transplant programs with worse-than-expected transplant outcomes. In spite of their stated goals of improving outcomes, these regulations have led to adverse unintended consequences. Most notably, there is evidence that these rules may have caused some programs to reject certain medically-suitable patients that are perceived to be ``high-risk,'' in order to avoid penalization. However, there remains debate in the literature over what drives this risk-averse behavior, and whether it is even a rational response by transplant programs seeking to avoid penalization. In this dissertation, we study the problem of regulatory-induced risk aversion in lung transplantation from the perspective of both the transplant program and regulators. To study program behavior, we present the first mathematical models of CMS and OPTN outcome-based regulations from the transplant program perspective. By calibrating our models with real data, we demonstrate that a rational program may reduce its risk of penalization by rejecting certain medically-suitable patients, thereby answering an open question in the clinical literature. We explore the incentives created by the regulations using a game-theoretic model, and find evidence that the large penalties associated with CMS penalization may be the primary driver of observed adverse patient selection. Motivated by this finding, we propose a new regulatory mechanism that is similar to current CMS regulations in other healthcare domains, and demonstrate that our proposed scheme may eliminate the incentive for programs to reject medically-suitable patients.en_US
dc.embargo.terms2023-08-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMildebrath, David T. K.. "Optimal Outcome-Based Regulation in Lung Transplantation: Modeling and ComputationOptimal Outcome-Based Regulation in Lung Transplantation: Modeling and Computation." (2021) Diss., Rice University. <a href="https://hdl.handle.net/1911/113689">https://hdl.handle.net/1911/113689</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/113689en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectOrgan transplantationen_US
dc.subjectoperations researchen_US
dc.subjecthealthcare policyen_US
dc.titleOptimal Outcome-Based Regulation in Lung Transplantation: Modeling and ComputationOptimal Outcome-Based Regulation in Lung Transplantation: Modeling and Computationen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputational and Applied Mathematicsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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