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Pandemic surges can rapidly overwhelm hospital capacity, where shortages of beds and nurse fatigue contribute directly to increased excess mortality, making coordinated decision-making across emergency departments, intensive care units, and general wards essential yet difficult to achieve under centralized control systems. Centralized approaches to bed allocation and nurse staffing optimization are limited because each hospital unit holds critical local information—such as real-time patient acuity, staff availability, and infection control status—that cannot be easily shared due to privacy constraints and communication delays during crisis conditions. To address these challenges, we propose a federated multi-agent reinforcement learning framework that enables coordinated decision-making for bed distribution and nurse staffing across hospital units without requiring centralization of sensitive clinical or workforce data. The system consists of local reinforcement learning agents deployed in each unit that participate in federated aggregation, a coordination mechanism that aligns inter-unit policies, and a surge detection module that dynamically switches operational strategies during pandemic escalation periods. This distributed architecture maintains data privacy while supporting adaptive, system-wide coordination under surge conditions, overcoming the limitations of both centralized optimization models and rule-based heuristic approaches.

Healthcare operations are constrained by demand volatility, resource scarcity, staffing pressures, and interdependent patient pathways. Artificial intelligence and predictive analytics offer a way to anticipate operational stress before it becomes visible in queues, bed shortages, overtime, or delayed care. This systematic review examines predictive analytics models applied to hospital staffing, scheduling, bed capacity, patient flow, and service demand forecasting from 2017 to 2022. The objective is to synthesize model types, data sources, operational targets, validation approaches, and implementation maturity across these domains. A PRISMA 2020–compliant review design was used to guide database searching, screening, eligibility assessment, extraction, and synthesis. Searches covered PubMed, Scopus, IEEE Xplore, and Web of Science, with narrative synthesis grouped by operational domain and risk of bias considered using an operationally adapted PROBAST-AI lens. The evidence base was dominated by retrospective, single-centre studies demonstrating the technical feasibility of predictive analytics for bed demand, emergency department arrivals, admission prediction, discharge prediction, and length-of-stay estimation. Staffing and scheduling studies were less frequent, and prospective implementation in real operational workflows remained uncommon. Predictive analytics for healthcare operations management is technically mature but practically under-deployed. The central challenge is translating forecasts into staffing, scheduling, bed-management, and command-centre decisions that measurably improve operational performance.