Optimal Hospital Care Scheduling During the SARS-CoV-2 Pandemic

Josh C. D’Aeth (), Shubhechyya Ghosal (), Fiona Grimm (), David Haw (), Esma Koca (), Krystal Lau (), Huikang Liu (), Stefano Moret (), Dheeya Rizmie (), Peter C. Smith (), Giovanni Forchini, Marisa Miraldo and Wolfram Wiesemann ()
Additional contact information
Josh C. D’Aeth: MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London, London SW7 2AZ, United Kingdom
Shubhechyya Ghosal: Department of Analytics, Marketing, & Operations, Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom
Fiona Grimm: The Health Foundation, London EC4Y 8AP, United Kingdom
David Haw: MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London, London SW7 2AZ, United Kingdom
Esma Koca: Department of Analytics, Marketing, & Operations, Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom
Krystal Lau: Department of Economics and Public Policy and Centre for Health Economics and Policy Innovation, Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom
Huikang Liu: Research Institute for Interdisciplinary Sciences, School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China
Stefano Moret: Department of Analytics, Marketing, & Operations, Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom
Dheeya Rizmie: Department of Economics and Public Policy and Centre for Health Economics and Policy Innovation, Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom
Peter C. Smith: Department of Economics and Public Policy and Centre for Health Economics and Policy Innovation, Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom
Wolfram Wiesemann: Department of Analytics, Marketing, & Operations, Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom

Management Science, 2023, vol. 69, issue 10, 5923-5947

Abstract: The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity to reduce the backlog of non-COVID patients while maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nationwide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient’s health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient’s health, and whose rewards encode the contribution to the overall objectives of the health system. The individual patients’ dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors, and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury and poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system, and cancer.

Keywords: COVID; care prioritization; grouped weakly coupled dynamic programs; fluid approximation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:69:y:2023:i:10:p:5923-5947

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