A stochastic programming approach for dynamic allocation of bed capacity and assignment of patients to collaborating hospitals during pandemic outbreaks

Abstract: Sustaining regular and infectious care during an infectious outbreak requires adequate management support for capacity allocation for regular and infectious patients. During the COVID-19 pandemic, hospitals faced severe challenges, including uncertainty concerning the number of infectious patients needing hospitalization and too little regional cooperation. This led to inefficient usage of healthcare capacity. To better prepare for future pandemics, we have developed a decision support system for central regional decision-making on opening and closing (regular care) hospital rooms for infectious patients and assigning new infectious patients to regional hospitals. Since the relabeling of rooms takes some lead time, we make decisions with a stochastic lookahead approach using stochastic programming with sample average approximation based on scenarios of the number of occupied infectious beds and infectious patients needing hospitalization. The lookahead approach produces high-quality decisions by measuring the impact of current decisions on future costs, such as costs for bed shortages, unused beds for infectious patients, and opening and closing rooms. Our simulation study applied to a COVID-19 scenario in the Netherlands, demonstrates that the stochastic lookahead approach outperforms a deterministic approach as well as other heuristic decision rules such as hospitals acting individually and implementing a pandemic unit, i.e., one hospital is designated to take all regional infectious patients until full. Our approach is very flexible and capable of tuning the model parameters to take into account the characteristics of future, yet unknown, pandemics, and supports sustaining regular care by minimizing the strain of infectious care on the available number of beds for regular care.