Machine Learning in Hospital Bed Management and Patient Flow: A Comprehensive Review of Evidence Synthesis and Implementation Guidance

Abstract

Efficient hospital bed management and patient flow are fundamental to healthcare quality, operational efficiency, and cost containment. Traditional approaches such as regression modeling, simulation, and queueing theory have offered limited flexibility in addressing the dynamic and nonlinear nature of hospital operations. The rise of machine learning (ML) has transformed this landscape by enabling predictive, adaptive, and automated decision-making across multiple facets of hospital management. Recent advances demonstrate how ML supports real-time hospital intelligence through applications including demand forecasting, length-of-stay prediction, discharge readiness assessment, and dynamic bed allocation. Techniques such as random forest, XGBoost, long short-term memory, transformer models, and reinforcement learning have been successfully applied to anticipate admission surges, predict recovery trajectories, and optimize capacity distribution. These systems enhance decision support for administrators and clinicians alike, enabling faster throughput, reduced congestion, and improved coordination of resources. Emerging innovations like AI-powered hospital command centers, digital twin simulations, and federated learning frameworks are redefining operational adaptability and resilience, particularly in crisis contexts like pandemics and mass casualty events. Yet, significant translational barriers persist, including fragmented data systems, limited interoperability, and the gap between technical model design and clinical relevance. Bridging this divide requires the development of explainable, ethically governed, and clinically meaningful AI that integrates seamlessly with hospital workflows. Overall, ML is steering hospitals toward a new operational paradigm, transforming routine management into a dynamic system capable of learning and adapting in real time.

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