A reinforcement learning-based optimal control approach for managing an elective surgery backlog after pandemic disruption

Huyang Xu, Yuanchen Fang (), Chun-An Chou, Nasser Fard and Li Luo
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Huyang Xu: Chengdu University of Technology
Yuanchen Fang: Sichuan University
Chun-An Chou: Northeastern University
Nasser Fard: Northeastern University
Li Luo: Sichuan University

Health Care Management Science, 2023, vol. 26, issue 3, No 3, 430-446

Abstract: Abstract Contagious disease pandemics, such as COVID-19, can cause hospitals around the world to delay nonemergent elective surgeries, which results in a large surgery backlog. To develop an operational solution for providing patients timely surgical care with limited health care resources, this study proposes a stochastic control process-based method that helps hospitals make operational recovery plans to clear their surgery backlog and restore surgical activity safely. The elective surgery backlog recovery process is modeled by a general discrete-time queueing network system, which is formulated by a Markov decision process. A scheduling optimization algorithm based on the piecewise decaying $$\epsilon$$ ϵ -greedy reinforcement learning algorithm is proposed to make dynamic daily surgery scheduling plans considering newly arrived patients, waiting time and clinical urgency. The proposed method is tested through a set of simulated dataset, and implemented on an elective surgery backlog that built up in one large general hospital in China after the outbreak of COVID-19. The results show that, compared with the current policy, the proposed method can effectively and rapidly clear the surgery backlog caused by a pandemic while ensuring that all patients receive timely surgical care. These results encourage the wider adoption of the proposed method to manage surgery scheduling during all phases of a public health crisis.

Keywords: Pandemic disruption; Elective surgery backlog; Stochastic scheduling optimization; Queueing network system; Markov decision process; Reinforcement learning; Operations research; Operations management (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10729-023-09636-5

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