Collaborative Research: Performance Incentives for Organ Transplantation Centers

合作研究：器官移植中心的绩效激励

• 批准号: 1826323
• 负责人: Andrew Schaefer
• 金额: $ 24.57万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826323&HistoricalAwards=false
• 关键词: Collaborative; Research; Performance; Incentives; Organ

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.

该奖项将通过确保更有效地使用捐赠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，对器官的需求大大超过了供应。为了确保移植中心有效地使用这些稀缺器官，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结果并最大限度地提高移植量。该奖项将通过研究社会目标和提供者激励之间的相互作用，更深入地了解移植系统和其他医疗环境中当前的按绩效付费计划。这个奖项的目标是符合国家科学基金会的使命目标，促进国家健康的进步。该奖项将资助跨学科研究的博士生，并为德克萨斯州医学中心的手术研究人员和移植专业人员制作教育材料。该奖项将涉及来自代表性不足的群体的学生，并通过莱斯大学和休斯顿大学的各种项目参与外展工作。这项研究将建立一个双层优化框架，以模拟社会观点和单个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。这个框架（1）制定了社会的角度（领导者），量化的效用调整，国家利益，并确定社会最佳的激励参数，和（2）模型每个移植中心的角度（追随者）作为一个顺序的，随机的决策问题，以最大限度地提高其移植量的社会激励。双层模型被认为是最困难的优化模型之一。这一研究将导致两层优化的进展，其中的追随者的问题是顺序和随机的。为了解决计算挑战，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编程技术的近似技术，并基于分解，分支和切割方法，和高-该奖项反映了NSF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进行评估，被认为值得支持审查标准。