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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.

Medication administration delays are a persistent patient safety and workflow problem in general medical wards. They arise from interacting pressures across nursing workload, pharmacy processes, medication availability, and patient acuity. Current approaches often rely on retrospective incident review, audit reports, or rule-based thresholds after a delay has already occurred. These methods do not provide timely support for proactive workload redistribution or pharmacy escalation. This manuscript proposes a supervised machine learning model to predict the probability that an upcoming scheduled medication dose will be delayed. The model is designed for operational use in general medical ward settings. The proposed model integrates electronic medication administration records, pharmacy dispensing timestamps, nurse-to-patient ratios, and shift-level workload indicators. A gradient boosting framework is conceptually used to capture non-linear relationships among workflow, staffing, and medication availability factors. The model would be expected to identify scheduled doses at elevated risk of delay before the administration window closes. Its outputs could support risk stratification, targeted charge nurse review, and earlier pharmacy coordination. A supervised prediction model for medication administration delay could function as an early warning component within a ward operations dashboard. Such a tool could support proactive clinical operations without replacing nurse judgment.

Emergency admissions frequently wait for inpatient bed placement when hospital capacity, infection control needs, staffing limitations, and specialty-bed requirements collide. These delays can prolong emergency department boarding and disrupt hospital-wide patient flow. Current bed management is often reactive, relying on bed coordinators, charge nurses, manual communication, and local escalation routines. Without a prospective warning system, teams may recognize an impending placement delay only after the admission queue has already stalled. The objective is to develop a machine learning model that predicts, at the time of admission decision, whether a patient is likely to experience delayed placement beyond a defined operational threshold. The model would use bed assignment logs, isolation requirements, unit census, nurse staffing levels, and specialty service availability as core predictors. A supervised classification model based on gradient-boosted trees would be trained on historical emergency admissions and linked operational data. The model would generate a placement delay risk score that can be refreshed as bed status, staffing, and unit conditions change. Conceptually, the model would identify admissions at elevated risk for delayed placement and attribute risk to operational constraints such as limited isolation rooms, high census, low staffing, or unavailable specialty beds. These explanations would give bed managers lead time to intervene before the delay becomes entrenched. This predictive model could shift hospital bed management from a reactive queue-based process to proactive, data-driven placement coordination. It would support earlier escalation, more targeted resource allocation, and improved alignment between emergency admissions and inpatient capacity.