[论文解读] Optimal Resource and Demand Redistribution for Healthcare Systems Under Stress from COVID-19

Felix Parker, Hamilton Sawczuk+3 more|arXiv (Cornell University)|Nov 6, 2020

Healthcare Operations and Scheduling OptimizationHealth Professions参考文献 29被引用 19

一句话总结

本文提出了一种混合整数线性规划框架，用于在大流行病高峰期对医院之间的患者和资源进行最优再分配，以最小化所需的应急容量和溢出量。结合鲁棒优化与新泽西州、德克萨斯州和迈阿密的真实世界数据，该模型相较于实际观测结果，将所需应急容量至少降低了85%，展示了在不确定性条件下的显著运营效率与可行性。

ABSTRACT

When facing an extreme stressor, such as the COVID-19 pandemic, healthcare systems typically respond reactively by creating surge capacity at facilities that are at or approaching their baseline capacity. However, creating individual capacity at each facility is not necessarily the optimal approach, and redistributing demand and critical resources between facilities can reduce the total required capacity. Data shows that this additional load was unevenly distributed between hospitals during the COVID-19 pandemic, requiring some to create surge capacity while nearby hospitals had unused capacity. Not only is this inefficient, but it also could lead to a decreased quality of care at over-capacity hospitals. In this work, we study the problem of finding optimal demand and resource transfers to minimize the required surge capacity and resource shortage during a period of heightened demand. We develop and analyze a series of linear and mixed-integer programming models that solve variants of the demand and resource redistribution problem. We additionally consider demand uncertainty and use robust optimization to ensure solution feasibility. We also incorporate a range of operational constraints and costs that decision-makers may need to consider when implementing such a scheme. Our models are validated retrospectively using COVID-19 hospitalization data from New Jersey, Texas, and Miami, yielding at least an 85% reduction in required surge capacity relative to the observed outcome of each case. Results show that such solutions are operationally feasible and sufficiently robust against demand uncertainty. In summary, this work provides decision-makers in healthcare systems with a practical and flexible tool to reduce the surge capacity necessary to properly care for patients in cases when some facilities are over capacity.

研究动机与目标

• 通过实现全系统范围的负荷均衡，解决大流行病高峰期反应式、仅限单家医院的应急容量建设效率低下的问题。
• 通过患者与关键资源的最优再分配，最小化全系统范围内所需的总应急容量与患者溢出量。
• 将护士可用性、转运成本和护理路径差异等运营约束整合进实用的决策支持框架中。
• 利用鲁棒优化技术确保解决方案对需求不确定性的鲁棒性。
• 为医疗系统提供一种灵活、可扩展且公开可用的工具，以主动管理极端需求事件下的容量。

提出的方法

• 构建一系列线性与混合整数规划模型，以优化医院网络中患者与资源的再分配。
• 引入鲁棒优化以处理需求不确定性，确保在不同预测情景下均能获得可行解。
• 模型包含护士容量限制、转运成本以及不同的患者护理路径等运营约束。
• 使用新泽西州、德克萨斯州和迈阿密的公开医院住院数据，对模型进行回溯校准与验证。
• 采用Gurobi 9.0.3高效求解大规模优化问题。
• 开发了一个交互式网络平台（https://covid-hospital-operations.com/），实现实时可视化与模型结果探索。

实验结果

研究问题

• RQ1 与仅限单家医院的应对方式相比，患者与资源的最优再分配是否能显著减少大流行病高峰期所需的总应急容量？
• RQ2 在医疗系统中，鲁棒优化方法在需求不确定性下保持解决方案可行性的有效性如何？
• RQ3 在不牺牲性能的前提下，多大程度上可将护士可用性与转运成本等运营约束整合进可扩展的再分配模型中？
• RQ4 与真实世界结果相比，所提模型在减少溢出与提升容量利用率方面表现如何？
• RQ5 该模型是否能够灵活适配不同医疗系统，并应用于未来大流行波次的实时规划？

主要发现

• 与大流行第一波期间新泽西州、德克萨斯州和迈阿密的实际观测结果相比，所提模型将所需应急容量至少降低了85%。
• 该框架成功实现了医院间患者负荷的均衡，最大限度减少了溢出，提升了全系统范围的运营效率。
• 鲁棒优化确保了在需求不确定性下的解决方案可行性，增强了实际部署中的可靠性。
• 纳入护士可用性与转运成本等运营约束并未影响性能，证明了其实际可行性。
• 即使考虑到复杂的真实世界限制与数据不确定性，该模型也远超无再分配策略的表现。
• 公开提供的代码与交互式网络平台使医疗系统能够实时探索与采用该模型，用于战略性容量规划。