Approximate Dynamic Programming Methods for Dynamic Resource Allocation Problems in Health Care

医疗保健中动态资源分配问题的近似动态规划方法

• 批准号: RGPIN-2018-05225
• 负责人: Sauré, Antoine
• 金额: $ 2.26万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713552
• 关键词: Approximate; Dynamic; Programming; Methods; Resource

Waitlists and wait times are among the most significant problems in health care. These are a consequence not only of an imbalance between capacity and demand but also a result of inefficient resource allocation practices. Demand for health care services has increased dramatically in the past decade. Capacity, on the other hand, has failed to keep up with demand and limited attention has been paid to identifying efficient resource allocation strategies. The latter mainly due to the complexity introduced by a wide variety of services, different types of patients, resource heterogeneity, and the presence of patient-resource interactions. Many resource allocation problems in health care, such as patient scheduling problems, can be modelled as sequential decision-making problems. The determination of optimal decision strategies for such problems typically requires consideration of an extremely large number of scenarios and alternative courses of action. Dynamic programming is a method that offers a mathematical formalization of the trade-off between the immediate and the future impact of alternative actions for such problems. It is well known that for most real-world dynamic resource allocation problems in health care the computational requirements of dynamic programming are overwhelming. Sophisticated methods for dealing with this issue, called approximate dynamic programming (ADP), have been developed in the last two decades. A common approach in these methods consists of using an approximation architecture in the dynamic programming model to represent the future performance of the system for different scenarios. Most approximation architectures found in the literature are either affine in the variables that represent the state of the system or defined as an affine combination of a low-dimensional set of basis functions in such variables. However, the future performance of the system for problems with complex dynamics can be better represented by non-linear functional forms in these variables. The proposed research seeks to develop and use alternative approximation architectures, together with novel linear programming and simulation-based approaches, to obtain improved strategies for dynamic resource allocation problems, particularly for patient scheduling. It will also focus on extending existing models and solution approaches to consider multiple resources, patient-resource interactions, and factors such as stochastic service times and follow-ups. The long-term objectives of this research will thus be to advance ADP techniques and theory and to develop new, implementable methods for this type of problems. The proposed research is targeted primarily at the Operations Research (OR) community, but it is intended to impact practice as well. I expect its outcome to generate new research areas for the OR community and hopefully impact health care policy and patient care.

等待名单和等待时间是医疗保健中最重要的问题之一。这不仅是能力和需求不平衡的结果，也是资源分配做法低效的结果。在过去的十年中，对卫生保健服务的需求急剧增加。另一方面，能力跟不上需求，对确定有效的资源分配战略的关注有限。后者主要是由于各种各样的服务、不同类型的患者、资源异构性以及存在人与资源的交互而引入的复杂性。 卫生保健中的许多资源分配问题，如病人调度问题，可以建模为序贯决策问题。为这类问题确定最佳决策战略通常需要考虑大量的情景和备选行动方案。动态编程是一种对此类问题的替代行动的直接影响和未来影响之间的权衡提供数学形式的方法。 众所周知，对于医疗保健中的大多数现实世界的动态资源分配问题，动态规划的计算要求是巨大的。处理这一问题的复杂方法，称为近似动态规划(ADP)，是在过去二十年中发展起来的。这些方法中的一种常见方法包括在动态规划模型中使用近似体系结构来表示不同场景下系统的未来性能。 在文献中发现的大多数近似结构要么是表示系统状态的变量中的仿射，要么被定义为这些变量中的低维基函数集的仿射组合。然而，对于复杂动力学问题，系统的未来性能可以用这些变量中的非线性函数形式更好地表示。 建议的研究试图开发和使用替代的近似架构，以及新的线性规划和基于模拟的方法，以获得针对动态资源分配问题的改进策略，特别是对于患者调度。它还将专注于扩展现有的模型和解决方案方法，以考虑多种资源、患者与资源的交互以及随机服务时间和后续行动等因素。因此，这项研究的长期目标将是推进ADP技术和理论，并为这类问题开发新的、可实施的方法。 拟议的研究主要针对运筹学(OR)社区，但它也旨在影响实践。我希望它的结果将为手术室社区带来新的研究领域，并有望影响医疗保健政策和患者护理。