Resource planning strategies for healthcare systems during a pandemic

We study resource planning strategies, including the integrated healthcare resources' allocation and shar-ing as well as patients' transfer, to improve the response of health systems to massive increases in de-mand during epidemics and pandemics. Our study considers various types of patients and resources to provide access to patient care with minimum capacity extension. Adding new resources takes time that most patients don't have during pandemics. The number of patients requiring scarce healthcare resources is uncertain and dependent on the speed of the pandemic's transmission through a region. We develop a multi-stage stochastic program to optimize various strategies for planning limited and necessary health-care resources. We simulate uncertain parameters by deploying an agent-based continuous-time stochas-tic model, and then capture the uncertainty by a forward scenario tree construction approach. Finally, we propose a data-driven rolling horizon procedure to facilitate decision-making in real-time, which miti-gates some critical limitations of stochastic programming approaches and makes the resulting strategies implementable in practice. We use two different case studies related to COVID-19 to examine our opti-mization and simulation tools by extensive computational results. The results highlight these strategies can significantly improve patient access to care during pandemics; their significance will vary under dif-ferent situations. Our methodology is not limited to the presented setting and can be employed in other service industries where urgent access matters. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

This study focuses on resource planning strategies, including the allocation and sharing of integrated healthcare resources and patients' transfer, to enhance the responsiveness of health systems during epidemics and pandemics. The research utilizes a multi-stage stochastic program to optimize various planning strategies for limited healthcare resources, and employs simulation and data-driven rolling horizon procedure to facilitate real-time decision-making. The results demonstrate that these strategies can significantly improve patient access to care during pandemics, and their significance varies in different situations.