Effective hospital capacity management is pivotal for enhancing patient care quality, operational efficiency, and healthcare system resilience, notably during demand spikes like those seen in the COVID-19 pandemic. However, devising optimal capacity strategies is complicated by fluctuating demand, conflicting objectives, and multifaceted practical constraints. This study presents a data-driven framework to optimize capacity management decisions within hospital systems during surge events. Two key decisions are optimized over a tactical planning horizon: allocating dedicated capacity to surge patients and transferring incoming patients between emergency departments (EDs) of hospitals to better distribute demand. The optimization models are formulated as robust mixed-integer linear programs, enabling efficient computation of optimal decisions that are robust against demand uncertainty. The models incorporate practical constraints and costs, including setup times and costs for adding surge capacity, restrictions on ED patient transfers, and relative costs of different decisions that reflect impacts on care quality and operational efficiency. The methodology is evaluated retrospectively in a hospital system during the height of the COVID-19 pandemic to demonstrate the potential impact of the recommended decisions. The results show that optimally allocating beds and transferring just 32 patients over a 63 day period around the peak, about one transfer every two days, could have reduced the need for surge capacity in the hospital system by nearly 90%. Overall, this work introduces a practical tool to transform capacity management decision-making, enabling proactive planning and the use of data-driven recommendations to improve outcomes.

翻译：有效的医院容量管理对于提升患者护理质量、运营效率以及医疗系统韧性至关重要，尤其是在面对如COVID-19大流行这类需求激增时。然而，由于需求波动、目标冲突及多方面的实际约束，制定最优容量策略颇具复杂性。本研究提出一个数据驱动框架，用于优化医院系统在激增事件期间的容量管理决策。在战术规划周期内，需优化两项关键决策：为激增患者分配专用容量，以及在医院急诊科之间转诊新入患者以更好地平衡需求。优化模型被构建为鲁棒混合整数线性规划，从而能够高效计算出对需求不确定性具有鲁棒性的最优决策。模型纳入了实际约束与成本，包括增设激增容量的准备时间与成本、急诊科患者转诊限制，以及反映对护理质量和运营效率影响的不同决策的相对成本。在COVID-19大流行高峰期，我们对该方法进行了回顾性评估，以展示推荐决策的潜在影响。结果表明，在疫情高峰前后的63天内，通过最优分配床位并仅转诊32名患者（约每两天转诊一次），可使医院系统对激增容量的需求减少近90%。总体而言，本研究引入了一种实用工具来转变容量管理决策方式，从而实现主动规划并利用数据驱动建议改善结局。