Preparing for the Next Health Emergency: The Effect of Facility Specialization in Concurrent Management of Pandemic and Non-Pandemic Demand

Problem Definition: We investigate how a network of healthcare facilities should manage non-pandemic and pandemic demand, asking whether each facility must operate in a specialized (i.e., treating a single patient type) or a generalized (i.e., treating both patient types) mode. Given the many drivers of the specialization–generalization decision, we focus on the value that specialization can create by reducing mix-variability in lengths of stay and limiting the scope and intensity of infection prevention measures. Methodology/results: We develop two optimization models that minimize the sum of patient waiting costs and facility infection mitigation costs under a static demand allocation policy. We provide an analytical characterization of the optimal allocation when facilities’ bed capacities are equal, and a highly effective heuristic when they are not. These results suggest that the optimal configuration contains at most one generalized facility. We also propose a simple expression that yields a conservative lower bound on the value of specialization, defined as the reduction in total cost when adopting the specialization-based configurations produced by our models instead of a configuration with all facilities being generalized. Managerial Implications: For length-of-stay parameters from the first COVID-19 waves in England and the Netherlands, this lower bound equals 6.5% and 21.2%, respectively. However, the actual performance gap could be substantially larger depending on infection mitigation costs and capacity imbalances, indicating a considerable potential value of facility specialization. For the special case of two equal-capacity facilities, we also show that a virtual pooling policy, as a representative dynamic policy, yields substantial savings over the best static policy only at very high traffic intensities; at lower intensities, static allocation can perform better. Finally, we show that adopting optimal static allocation policies can substantially reduce costs of a network facing an imminent pandemic by proposing a supply-demand management framework that combines capacity expansion with optimal demand allocation.