arbiter
JournalsAccount

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

Qihao Wu et al.

Health Care Management Science2025https://doi.org/10.1007/s10729-025-09699-6review
AJG 2ABDC B
Weight
0.64

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.

19 citations

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1007/s10729-025-09699-6

Or copy a formatted citation

@article{qihao2025,
  title        = {{Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions}},
  author       = {Qihao Wu et al.},
  journal      = {Health Care Management Science},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s10729-025-09699-6},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

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

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.64

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.68 × 0.4 = 0.27
M · momentum0.97 × 0.15 = 0.15
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.