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- D’Aeth, Josh C., et al.
(author)
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Optimal hospital care scheduling during the SARS-CoV-2 pandemic
- 2023
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In: Management science. - : Institute for Operations Research and the Management Sciences (INFORMS). - 0025-1909 .- 1526-5501. ; 69:10, s. 5695-6415
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Journal article (peer-reviewed)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.
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| 2. |
- D’Aeth, Josh C., et al.
(author)
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Optimal national prioritization policies for hospital care during the SARS-CoV-2 pandemic
- 2021
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In: Nature Computational Science. - : Springer Nature. - 2662-8457. ; 1:8, s. 521-531
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Journal article (peer-reviewed)abstract
- In response to unprecedented surges in the demand for hospital care during the SARS-CoV-2 pandemic, health systems have prioritized patients with COVID-19 to life-saving hospital care to the detriment of other patients. In contrast to these ad hoc policies, we develop a linear programming framework to optimally schedule elective procedures and allocate hospital beds among all planned and emergency patients to minimize years of life lost. Leveraging a large dataset of administrative patient medical records, we apply our framework to the National Health Service in England and show that an extra 50,750–5,891,608 years of life can be gained compared with prioritization policies that reflect those implemented during the pandemic. Notable health gains are observed for neoplasms, diseases of the digestive system, and injuries and poisoning. Our open-source framework provides a computationally efficient approximation of a large-scale discrete optimization problem that can be applied globally to support national-level care prioritization policies.
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