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

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: Approximate dynamic programming; Healthcare operations; Healthcare services delivery; Markov decision process; Neural networks; Reinforcement learning.

Fig. 1

Number of publications related to RL for HOM by year

Fig. 2

Mapping from applications to learning methods

Fig. 3

Mapping from applications to approximators

Fig. 4

Evolution of RL methodologies used in HOM