NurseSchedRL: Attention-Guided Reinforcement Learning for Nurse-Patient Assignment
This addresses nurse scheduling challenges in healthcare systems, but it is incremental as it applies existing RL methods to a specific domain.
The paper tackled the problem of nurse-patient assignment in healthcare by proposing NurseSchedRL, a reinforcement learning framework that improved scheduling efficiency, skill alignment, and reduced fatigue in simulations with realistic data.
Healthcare systems face increasing pressure to allocate limited nursing resources efficiently while accounting for skill heterogeneity, patient acuity, staff fatigue, and continuity of care. Traditional optimization and heuristic scheduling methods struggle to capture these dynamic, multi-constraint environments. I propose NurseSchedRL, a reinforcement learning framework for nurse-patient assignment that integrates structured state encoding, constrained action masking, and attention-based representations of skills, fatigue, and geographical context. NurseSchedRL uses Proximal Policy Optimization (PPO) with feasibility masks to ensure assignments respect real-world constraints, while dynamically adapting to patient arrivals and varying nurse availability. In simulation with realistic nurse and patient data, NurseSchedRL achieves improved scheduling efficiency, better alignment of skills to patient needs, and reduced fatigue compared to baseline heuristic and unconstrained RL approaches. These results highlight the potential of reinforcement learning for decision support in complex, high-stakes healthcare workforce management.