COVID-19压力下医疗系统的最优资源和需求再分配
Optimal Resource and Demand Redistribution for Healthcare Systems Under Stress from COVID-19
摘要：
当面临极端压力时，如COVID-19大流行，医疗保健系统通常会做出反应，在达到或接近其基线容量的设施中建立激增容量。然而，在每个设施处创建单独的能力不一定是最佳方法，并且在设施之间重新分配需求和关键资源可以减少所需的总能力。数据显示，在2019冠状病毒病大流行期间，这些额外的负荷在医院之间分布不均，需要一些医院创建激增的能力，而附近的医院则有未使用的能力。这不仅效率低下，而且还可能导致能力过剩的医院的护理质量下降。在这项工作中，我们研究的问题，找到最佳的需求和资源转移，以尽量减少所需的激增能力和资源短缺期间的需求增加。我们开发和分析了一系列的线性和混合整数规划模型，解决需求和资源再分配问题的变种。我们还考虑了需求的不确定性，并使用稳健的优化来确保解决方案的可行性。我们还纳入了决策者在实施此类计划时可能需要考虑的一系列运营限制和成本。我们的模型使用新泽西州、德克萨斯州和迈阿密的COVID-19住院数据进行了回顾性验证，相对于每个病例的观察结果，所需的浪涌能力至少减少了85%。结果表明，这样的解决方案是可行的操作和足够强大的需求不确定性。总之，这项工作为医疗保健系统的决策者提供了一个实用而灵活的工具，以减少在某些设施能力过剩的情况下适当照顾患者所需的浪涌能力。
英文摘要：
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.
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