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Optimal hospital care scheduling during the SARS-CoV-2 pandemic
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- D’Aeth, Josh C. (författare)
- 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, United Kingdom
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- Ghosal, Shubhechyya (författare)
- Department of Analytics, Marketing, & Operations, Imperial College Business School, Imperial College London, London, United Kingdom
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- Grimm, Fiona (författare)
- The Health Foundation, London, United Kingdom
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- 2023-02-21
- 2023
- Engelska.
- Ingår i: Management science. - : Institute for Operations Research and the Management Sciences (INFORMS). - 0025-1909 .- 1526-5501. ; 69:10, s. 5695-6415
- Tidskriftsartikel (refereegranskat)
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Matematik -- Beräkningsmatematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Computational Mathematics (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Annan medicin och hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Other Medical and Health Sciences (hsv//eng)
Nyckelord
- COVID
- care prioritization
- grouped weakly coupled dynamic programs
- fluid approximation
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