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Nurse call buttons are a core communication channel through which hospitalised patients request assistance, reassurance, symptom support, and routine care. When call demand is high, nurses may experience overload, interruptions, and competing priorities that delay responses to urgent needs. Current approaches to call demand management are largely reactive and shift based. They often overlook rapid fluctuations driven by patient acuity, prior call behaviour, room geography, time-of-day routines, and staffing conditions. This article proposes a smart hospital AI system for short-term prediction of nurse call button demand at the room or unit-zone level. The framework is conceptual and system oriented, with emphasis on how operational data streams could support proactive nursing workflow decisions. The system includes a patient acuity scoring module, call history learner, circadian pattern analyser, room-location spatial modeller, staffing-ratio adjuster, and real-time demand forecasting dashboard. Together, these components would translate fragmented operational signals into interpretable demand forecasts. The proposed system would support proactive allocation of nursing resources by identifying rooms or zones likely to generate elevated call demand. It could also guide anticipatory rounding, help reduce avoidable non-urgent calls, and support more balanced workload distribution. A demand-driven nursing workflow would move smart hospital operations beyond reactive response to patient-initiated requests. Predictive nurse call intelligence offers a pathway toward more responsive, equitable, and resilient inpatient care.

Diagnostic services are central to clinical decision-making because imaging, laboratory testing, and cardiology diagnostics often determine the next step in diagnosis, treatment, or referral. Bottlenecks in these services can delay care pathways and increase wait times when demand rises faster than available capacity. Current forecasting approaches in diagnostic departments are often reactive and based on historical averages, recent appointment counts, or manual manager judgment. Such approaches may miss upstream signals such as referral surges, seasonal disease activity, and physician ordering behavior. This article proposes a predictive model for forecasting diagnostic service demand by integrating ambulatory referral volume, seasonal disease trends, physician ordering patterns, equipment availability, and historical appointment backlogs. The model is intended to support short- and medium-term capacity planning across diagnostic services. The proposed approach uses a supervised time-series forecasting framework, such as gradient boosting with temporal features or a recurrent neural architecture, trained on historical diagnostic order and scheduling data. Inputs would be engineered from referral streams, diagnostic ordering records, seasonal indicators, equipment schedules, and backlog measures. Conceptually, the model would generate daily or weekly demand forecasts for each diagnostic modality and service line. Forecasts would include uncertainty bounds and operational alerts when projected demand is expected to exceed available appointment capacity. The proposed predictive model could enable proactive capacity management in diagnostic departments. By anticipating demand before backlogs become severe, the model could support improved scheduling, better equipment utilization, and reduced patient waiting times.

Accurate prediction of demand for emergency, imaging, pharmacy, laboratory, and inpatient services is critical for hospital planning. However, forecasting models are typically built separately by department or institution, which limits their ability to learn from shared demand patterns. Hospitals generate rich operational demand streams, but patient-level data cannot usually be pooled across organizations. This creates a need for collaborative forecasting methods that preserve institutional control over sensitive operational records. This article proposes a federated multi-task learning framework for predicting service demand across multiple hospital units. The framework trains a shared predictive model across hospitals while each institution contributes only protected model updates. The framework includes local data adapters, a shared temporal learning backbone, task-specific forecasting heads, a federated aggregation layer, differential privacy mechanisms, and site-specific personalization modules. Together, these components support collaborative forecasting without transferring patient-level operational data. The framework could improve demand prediction by learning common temporal patterns across hospitals and service lines. It would also support local adaptation, reduce duplicated model development, and preserve data confidentiality. A privacy-preserving, collaborative approach to hospital demand forecasting could become a core infrastructure for multi-site operational coordination. Federated multi-task learning offers a practical conceptual foundation for such a system.