Collaborative Research: Performance Incentives for Organ Transplantation Centers

该奖项将通过确保更有效地使用供体移植器官来影响国家的医疗保健系统。器官移植是治疗终末期器官衰竭最有效的方法，也是唯一可行的方法。不幸的是，对器官的需求远远超过了供应。为了确保移植中心有效地利用这些稀缺器官，该奖项将制定新的移植中心评估标准，以激励移植中心改善移植后的结果，最大限度地提高移植量。该奖项将通过检查社会目标和提供者激励之间的相互作用，加深对移植系统和其他医疗保健环境中当前按绩效付费举措的理解。该奖项的目标与美国国家科学基金会促进国民健康进步的使命目标是一致的。该奖项将资助一名从事跨学科研究的博士生，并为德克萨斯医学中心的手术研究人员和移植专业人员创建教育材料。该奖项将涉及来自代表性不足群体的学生，并通过莱斯大学和休斯顿大学的各种项目参与外展工作。本研究将建立一个双层优化框架来模拟社会视角和个体移植中心目标之间的相互作用，以确定同时最大化社会和中心层面利益的激励措施。该框架(1)建立社会视角（领导者），量化经效用调整后的国家效益，确定社会最优激励参数；(2)将每个移植中心视角（追随者）建模为一个顺序的随机决策问题，以便在社会激励下实现移植量最大化。双层模型被认为是最难的优化模型之一。这一研究将会在二阶优化问题中取得进展，其中追随者的问题是顺序的和随机的。为了解决计算方面的挑战，研究人员将开发利用每个中心问题的流体模型结构和机会约束规划技术的近似技术，并基于分解、分支-切割方法和高性能计算构建高效算法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.