Collaborative Research: Performance Incentives for Organ Transplantation Centers

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This award will impact the Nation's healthcare system by ensuring more effective use of donor transplant organs. Organ transplantation is the most effective, and often the only viable, therapy for end-stage organ failure. Unfortunately, the demand for organs greatly exceeds the supply. To ensure that transplant centers are effectively using these scarce organs, this award will develop new transplant center evaluation criteria to incentivize transplant centers to improve their post-transplant outcomes and maximize transplantation volume. This award will lead to a deeper understanding of current pay-for-performance initiatives in the transplantation system and in other healthcare settings by examining the interplay between societal goals and provider incentives. The goals of this award are in line with the NSF mission goal of promoting the advancement of the national health. This award will fund a doctoral student in inter-disciplinary research and create educational materials for both operations researchers and transplantation professionals at the Texas Medical Center. This award will involve students from underrepresented groups and engage in outreach efforts through various programs at Rice University and the University of Houston.This research will build a bilevel optimization framework to model the interaction between the societal perspective and the goals of individual transplant centers to determine incentives that simultaneously maximize societal and center-level benefits. This framework (1) formulates the societal perspective (the leader) that quantifies the utility-adjusted, national benefit and determines societally optimal incentive parameters, and (2) models each transplant center's perspective (the followers) as a sequential, stochastic decision-making problem so as to maximize its transplant volume subject to the societal incentives. Bilevel models are considered among the hardest optimization models. This research will lead to advances in bilevel optimization where the followers' problems are sequential and stochastic. To address the computational challenges, the researchers will develop approximation techniques leveraging the fluid model structure of each center's problem and chance-constrained programming techniques, and build efficient algorithms based on decomposition, branch-and-cut methods, and high-performance computing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.