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

Author

Listed:
  • Huyang Xu

    (Chengdu University of Technology)

  • Yuanchen Fang

    (Sichuan University)

  • Chun-An Chou

    (Northeastern University)

  • Nasser Fard

    (Northeastern University)

  • Li Luo

    (Sichuan University)

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.

Suggested Citation

  • Huyang Xu & Yuanchen Fang & Chun-An Chou & Nasser Fard & Li Luo, 2023. "A reinforcement learning-based optimal control approach for managing an elective surgery backlog after pandemic disruption," Health Care Management Science, Springer, vol. 26(3), pages 430-446, September.
    • Handle: RePEc:kap:hcarem:v:26:y:2023:i:3:d:10.1007_s10729-023-09636-5
    DOI: 10.1007/s10729-023-09636-5
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    References listed on IDEAS

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    1. Yufan Zhao & Donglin Zeng & Mark A. Socinski & Michael R. Kosorok, 2011. "Reinforcement Learning Strategies for Clinical Trials in Nonsmall Cell Lung Cancer," Biometrics, The International Biometric Society, vol. 67(4), pages 1422-1433, December.
    2. Schütz, Hans-Jörg & Kolisch, Rainer, 2012. "Approximate dynamic programming for capacity allocation in the service industry," European Journal of Operational Research, Elsevier, vol. 218(1), pages 239-250.
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