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 30 patients over a 63 day period around the peak, less than one transfer every two days, could have reduced the need for surge capacity in the hospital system by approximately 98%. 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：医院容量管理的有效性对于提升患者护理质量、运营效率及医疗系统韧性至关重要，尤其在应对COVID-19疫情等需求激增时更是如此。然而，由于需求波动、目标冲突及多方面的实际约束，制定最优容量策略极具挑战性。本研究提出一个数据驱动框架，用于优化突发事件期间医院系统的容量管理决策。在战术规划周期内，需要优化两个关键决策：将专用容量分配给突发事件患者，以及在各医院急诊科之间转移新收治患者以更好地平衡需求。该优化模型被构建为鲁棒混合整数线性规划形式，能够在考虑需求不确定性的情况下高效计算最优决策。模型纳入了实际约束与成本，包括增设应急容量的准备时间与成本、急诊科患者转移限制，以及反映护理质量和运营效率影响的各项决策相对成本。本研究通过回顾性评估COVID-19疫情高峰期间某医院系统的实际运营数据，验证了推荐决策的潜在影响。结果表明，在疫情峰值前后的63天内，通过最优分配床位并仅转移30名患者（平均每两天少于一次转移），可将该医院系统对应急容量的需求降低约98%。总体而言，本研究为转变容量管理决策提供了一种实用工具，能够支持前瞻性规划并借助数据驱动建议改善医疗成效。