Hospital Capacity Pooling in Pandemics: Simulation‐Based Mortality Impacts and Diversification Mechanisms

Abstract

Elevated healthcare strain during the COVID‐19 pandemic increased patient mortality rates and prompted costly non‐pharmaceutical interventions. This work examines how the size of a hospital's service population, which can be expanded by linking hospitals through patient transfer networks, influences healthcare strain through two diversification mechanisms: (i) volatility dampening, which reduces the volatility of demand for healthcare resources by aggregating uncorrelated individual patient needs, and (ii) epidemic phase averaging, which flattens demand peaks by aggregating patients from asynchronous local epidemics. Using facility‐level intensive care unit (ICU) data across three COVID waves, we analyze how service population size affects the size of peaks in ICU occupancy, evaluate the two proposed mechanisms, and explore the health impacts of pooling through scenario simulations grounded in observed occupancy levels. We find that volatility dampening effects reduce variability in ICU occupancy at a rate proportional to the square root of population size, whereas the effectiveness of epidemic phase averaging is dependent on the transmission dynamics of the pathogen. Increasing service population size also reduces the frequency and magnitude of demand spikes, lowering the likelihood of costly short‐term interventions and increasing demand predictability. In the winter 2020 wave, our simulations find that randomly constructed local pools (2–6 hospitals) reduce COVID ICU mortality by a median of 2%–7%, with reductions exceeding 6%–18% in the top decile of outcomes. These findings suggest that coordinated patient transfer strategies could meaningfully reduce mortality and costs during future pandemics.