Reinforcement Learning for Adaptive Scheduling and Optimization of Healthcare Staff and Resources in Multi-Departmental Hospitals

Abstract

Efficient hospital resource management is critical for ensuring timely patient care and avoiding burnout among healthcare workers. Traditional scheduling systems struggle to accommodate the complex interdependencies and unpredictability inherent in hospital operations. This research investigates the application of reinforcement learning techniques for optimizing the complex task of healthcare staff scheduling and resource allocation in multi-departmental hospital settings. We present a novel approach that combines deep reinforcement learning with constraint satisfaction to address the dynamic and stochastic nature of hospital environments. Our methodology employs a multi-agent framework where each department functions as a semi-autonomous agent while operating under system-wide constraints and objectives. The proposed algorithm demonstrates significant improvements in key performance metrics including patient wait times, staff utilization efficiency, and resource allocation. Through extensive simulation testing using synthetic data that mirrors real-world hospital conditions, we show that our approach reduces average patient wait times by 27.8\% and improves staff utilization rates by 18.3\% compared to traditional scheduling methods. Furthermore, the adaptive nature of our approach allows for real-time adjustments in response to unexpected events such as patient surges or staff absences. The mathematical foundation developed in this work establishes a framework that balances operational efficiency with healthcare quality metrics, providing a robust solution to the persistent challenge of optimal hospital resource management.