CAREER: Optimizing Healthcare Operations through Incentive Mechanism Design

这项学院早期职业发展计划(CALEAR)赠款将通过支持对激励机制的设计和分析的研究，促进最佳健康行为和有效利用稀缺资源，在这种情况下，器官捐赠用于移植，从而促进国家繁荣和经济福利的发展。这项研究旨在通过减少未移植的捐赠器官的数量、有效的患者-器官匹配、通过激励器官捐赠来刺激器官可获得性，以及通过优化设计和时机的激励来促进健康来提高器官利用率。通过将行为方面纳入医疗保健运营，该项目开发了战略激励措施，以帮助将个人行动与社会福祉保持一致，而不强制强制遵守。这项工作的教育部分将用行为概念丰富运筹学课程，通过教育医学研究人员了解高级建模技术来促进跨学科理解，并通过与HBCU的合作吸引代表性不足的学生，有助于建立一支多样化、包容性和有效的科学工作队伍。该项目将在运筹学和行为经济学领域架起桥梁，使用各种随机动态建模技术来构建和评估医疗保健环境中的激励机制，包括排队博弈模型、委托-代理模型和离散时间有限范围模型，同时将个人的战略决策与总体社会目标联系起来。将使用真实生活数据和临床详细的模拟模型来经验性地验证这些机制，确保研究结果能够有效地为政策制定提供信息。开发的分析方法也有可能为个人和社会利益交叉的其他应用领域的激励设计提供信息。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。

This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national prosperity and economic welfare by supporting research on the design and analysis of incentive mechanisms that promote optimal health behaviors and efficient utilization of scarce resources, in this case organs donated for transplantation. The research addresses the improvement of organ utilization by reducing the number of donated organs that are discarded without being transplanted, efficient patient-organ matching, stimulation of organ availability by incentivizing organ donation, and the promotion of wellness through optimally designed and timed incentives. By incorporating behavioral aspects into healthcare operations, the project develops strategic incentives to help align individual actions with societal well-being without imposing mandatory compliance. The educational component of this work will enrich the operations research curriculum with behavioral concepts, foster interdisciplinary understanding by educating medical researchers about advanced modeling techniques, and engage underrepresented students through collaboration with an HBCU, contributing to a diverse, inclusive, and effective scientific workforce.This project will bridge the fields of operations research and behavioral economics to construct and evaluate incentive mechanisms within healthcare settings using a variety of stochastic dynamic modeling techniques, including queueing game models, principal-agent models, and discrete-time finite-horizon models while linking the strategic decisions of individuals to overarching societal goals. Real-life data and clinically detailed simulation models will be employed to empirically validate the mechanisms, ensuring that the research outcomes can fruitfully inform policymaking. The analysis methods developed also have the potential to inform incentive design in other application domains where individual and societal interests intersect.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.