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: 1826144
• 负责人: Ahmed Gaber
• 金额: $ 4.98万
• 依托单位: The Methodist Hospital Research Institute
• 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=1826144&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.

这一奖项将通过确保更有效地使用捐赠者的移植器官来影响国家的医疗体系。器官移植是治疗终末期器官衰竭的最有效、也是唯一可行的治疗方法。不幸的是，器官供不应求。为了确保移植中心有效地利用这些稀缺的器官，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其移植后的结果并最大限度地增加移植数量。这一奖项将通过审查社会目标和提供者激励之间的相互作用，使人们更深入地理解当前移植系统和其他医疗保健环境中的按绩效支付倡议。该奖项的目标与NSF促进国民健康进步的使命目标是一致的。这一奖项将资助一名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。该奖项将包括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。这项研究将建立一个双层优化框架，以建模社会视角和个别移植中心目标之间的互动，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)建立了社会视角(领导者)，它量化了效用调整后的国家利益，并确定了社会最优激励参数；(2)将每个移植中心的视角(追随者)建模为一个连续的随机决策问题，以便在社会激励的约束下最大化其移植体量。双层模型被认为是最难的优化模型之一。这项研究将导致两层优化的进展，在这种情况下，追随者的问题是顺序的和随机的。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、分支切割方法和高性能计算构建高效算法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。