Optimal Decision of Dynamic Bed Allocation and Patient Admission with Buffer Wards during an Epidemic

Abstract

To effectively prevent patients from nosocomial cross-infection and secondary infections, buffer wards for screening infectious patients who cannot be detected due to the incubation period are established in public hospitals in addition to isolation wards and general wards. In this paper, we consider two control mechanisms for three types of wards and patients: one is the dynamic bed allocation to balance the resource utilization among isolation, buffer, and general wards; the other is to effectively control the admission of arriving patients according to the evolution process of the epidemic to reduce mortality for COVID-19, emergency, and elective patients. Taking the COVID-19 pandemic as an example, we first develop a mixed-integer programming (MIP) model to study the joint optimization problem for dynamic bed allocation and patient admission control. Then, we propose a biogeography-based optimization for dynamic bed and patient admission (BBO-DBPA) algorithm to obtain the optimal decision scheme. Furthermore, some numerical experiments are presented to discuss the optimal decision scheme and provide some sensitivity analysis. Finally, the performance of the proposed optimal policy is discussed in comparison with the other different benchmark policies. The results show that adopting the dynamic bed allocation and admission control policy could significantly reduce the total operating cost during an epidemic. The findings can give some decision support for hospital managers in avoiding nosocomial cross-infection, improving bed utilization, and overall patient survival during an epidemic.

Keywords

• buffer wards
• mixed-integer programming
• dynamic bed allocation
• patient admission control
• COVID-19 pandemic