Healthcare systems worldwide are grappling with increasing complexity in operations, demanding agile and data-driven decision-making. Reinforcement learning (RL), a subfield of machine learning, has emerged as a potent methodology for optimising sequential decisions in uncertain environments. Its relevance in healthcare operations management (HOM) has grown significantly, especially following the disruptions caused by the COVID-19 pandemic. RL enables decision-makers to improve resource allocation, patient flow and epidemic response by modelling healthcare systems as dynamic processes. A recent review published in Health Care Management Science explores the foundational methodologies of RL in HOM, its key applications across different system levels and the challenges and future research directions that define this rapidly evolving field.
Reinforcement Learning Methodologies for Healthcare
The foundational framework of reinforcement learning in HOM is built upon the Markov Decision Process (MDP), which enables the modelling of sequential decisions under uncertainty. MDPs use defined states, actions, transition probabilities, rewards and discount factors to simulate healthcare operations such as patient admission, discharge or resource allocation. Classical dynamic programming approaches can solve smaller MDPs, but RL becomes essential as system complexity increases.
Approximate Dynamic Programming (ADP) techniques enable scalable decision-making by estimating value functions through sampling, such as Monte Carlo simulations. Temporal Difference (TD) learning, a model-free method, updates value estimates using real-time feedback, and Q-learning (QL) further enhances this by optimising policy decisions through action-value functions. To address dimensionality, Deep Q-Networks (DQNs) replace tabular approximations with neural networks, improving scalability. Policy-based methods like Policy Gradient and Actor-Critic algorithms directly optimise decision policies and are especially useful in continuous action spaces.
Reinforcement learning also addresses trade-offs between exploration and exploitation. Bandit strategies and Bayesian RL incorporate uncertainty to guide decisions more effectively. In complex systems, Multi-Agent RL (MARL), Hierarchical RL and Imitation Learning break down large-scale healthcare challenges into manageable subproblems, such as multi-site coordination or human–machine collaboration. These advanced RL variants improve the practicality of RL in real-world HOM applications.
Applications Across System Levels
RL has been applied across macro-, meso- and microlevels of healthcare operations. At the macro level, RL has been widely used to inform public health strategies, particularly during pandemics. During COVID-19, RL supported dynamic policy-making through models like SEIR and SIR, guiding lockdowns, testing and vaccination strategies. Deep RL methods such as DQN and Actor-Critic algorithms enabled adaptive, cost-efficient responses. These approaches balanced infection rates, healthcare capacity and economic impacts more effectively than static policies. RL frameworks also incorporated spatial and demographic data, aiding geographically targeted interventions and mobility restrictions.
The meso level focuses on resource distribution, supply chain logistics and cross-institutional coordination. RL supports humanitarian logistics by optimising the delivery of medical supplies, patient transport and disaster response. Applications involve path planning for rescue missions, multi-vehicle coordination and UAV-based deliveries under uncertainty. Studies applied Q-learning, DQN and MARL to these contexts, showing improved performance in computational efficiency and operational robustness. These methods help address challenges such as limited visibility, dynamic demands and constrained transportation capacities.
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At the micro level, RL enhances operations within individual healthcare facilities. This includes patient flow scheduling, staff rostering, appointment booking and inventory management. For instance, RL models help hospitals optimise bed utilisation and reduce emergency department boarding times. Queueing systems are often used in combination with RL to allocate patients to wards based on real-time data. Model-free methods like QL and DQN effectively handle discrete action problems, while deep learning-based RL handles larger state-action spaces. Additionally, RL has been integrated with electronic health records (EHR) to support real-time clinical decisions, optimise resource use and improve patient care continuity.
Challenges and Future Directions
Despite the growing success of RL in HOM, several challenges persist. A major barrier is the lack of interpretability in deep RL models, which hinders their adoption in high-stakes environments where transparency is crucial. Healthcare stakeholders demand decisions that are not only accurate but also explainable. Research is beginning to explore interpretable RL by using dimensionality reduction or simpler policy models.
Another challenge lies in data limitations. Model-free RL requires vast amounts of interaction data, which may not always be available in healthcare contexts due to privacy concerns and ethical constraints. Model-based approaches and the use of simulations partially address this issue, but balancing realism and generalisability remains complex. Furthermore, reward design is critical: misaligned incentives can lead to suboptimal or unethical decisions. Therefore, ensuring that reward functions accurately reflect healthcare priorities, such as patient safety, equity and cost-effectiveness, is essential.
Finally, the deployment of RL in real-world healthcare systems is still limited. Although simulation-based studies are abundant, few RL applications have been operationalised due to concerns around robustness, scalability and validation. Future research must focus on bridging the gap between simulation and deployment by establishing frameworks for real-time learning, regulatory compliance and stakeholder trust. Combining RL with domain expertise, hybrid methods and robust validation processes will be key to realising its full potential in HOM.
Reinforcement learning represents a powerful tool for advancing healthcare operations management across strategic, tactical and operational domains. It provides decision-makers with the ability to navigate uncertainty, optimise complex systems and adapt to dynamic environments. From pandemic policy design to supply chain logistics and patient flow optimisation, RL has proven its versatility and potential. However, real-world implementation demands further progress in interpretability, data efficiency and model validation. As RL continues to evolve, its integration with healthcare systems promises to significantly enhance decision-making, efficiency and patient outcomes in an increasingly complex healthcare landscape.
Source: Health Care Management Science
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