Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions

Qihao Wu (), Jiangxue Han (), Yimo Yan (), Yong-Hong Kuo () and Zuo-Jun Max Shen ()
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Qihao Wu: The University of Hong Kong
Jiangxue Han: The University of Hong Kong
Yimo Yan: The University of Hong Kong
Yong-Hong Kuo: The University of Hong Kong
Zuo-Jun Max Shen: The University of Hong Kong

Health Care Management Science, 2025, vol. 28, issue 2, No 9, 298-333

Abstract: Abstract With the advancement in computing power and data science techniques, reinforcement learning (RL) has emerged as a powerful tool for decision-making problems in complex systems. In recent years, the research on RL for healthcare operations has grown rapidly. Especially during the COVID-19 pandemic, RL has played a critical role in optimizing decisions with greater degrees of uncertainty. RL for healthcare applications has been an exciting topic across multiple disciplines, including operations research, operations management, healthcare systems engineering, and data science. This review paper first provides a tutorial on the overall framework of RL, including its key components, training models, and approximators. Then, we present the recent advances of RL in the domain of healthcare operations management (HOM) and analyze the current trends. Our paper concludes by presenting existing challenges and future directions for RL in HOM.

Keywords: Reinforcement learning; Healthcare operations; Healthcare services delivery; Markov decision process; Approximate dynamic programming; Neural networks (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10729-025-09699-6

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