  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.
