Resource planning strategies for healthcare systems during a pandemic

Fattahi, Mohammad, Keyvanshokooh, Esmaeil and Govindan, Devika Kannanc Kannan (2022) Resource planning strategies for healthcare systems during a pandemic. European Journal of Operational Research. ISSN 0377-2217 (In Press)

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Official URL: https://doi.org/10.1016/j.ejor.2022.01.023

Abstract

We study resource planning strategies, including the integrated healthcare resources’ allocation and sharing as well as patients’ transfer, to improve the response of health systems to massive increases in demand 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 healthcare resources. We simulate uncertain parameters by deploying an agent-based continuous-time stochastic 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 mitigates 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 optimization 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 different situations. Our methodology is not limited to the presented setting and can be employed in other service industries where urgent access matters.

Item Type: Article
Uncontrolled Keywords: OR in health services, COVID-19 pandemic, resource sharing and allocation, patients’ transfers, multi-stage stochastic programming, data-driven rolling horizon
Subjects: B900 Others in Subjects allied to Medicine
Department: Faculties > Business and Law > Newcastle Business School
Depositing User: John Coen
Date Deposited: 19 Jan 2022 14:30
Last Modified: 19 Jan 2022 14:30
URI: http://nrl.northumbria.ac.uk/id/eprint/48207

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