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Emergency department discharge delays are a critical operational bottleneck shaped by diagnostic completion intervals, inpatient bed scarcity, consultant responsiveness, and patient acuity. Understanding these drivers in real time is essential for improving flow and reducing avoidable crowding. Current ED analytics often describe aggregate delays after they occur. Clinicians and operational managers therefore lack transparent patient-level tools that indicate which factor is most responsible for a specific delayed discharge episode. This article proposes an explainable gradient-boosting framework for identifying key contributors to delayed ED discharge. The framework focuses on diagnostic order completion times, bed availability, consultant response delays, and patient acuity scores. The proposed framework uses a gradient-boosted tree ensemble trained on historical ED visit data and paired with SHAP-based post-hoc explanations. It is designed conceptually for real-time use with live electronic health record, bed-board, order, and consultation data. Conceptually, the framework would generate both a delay-risk score and an interpretable decomposition of that risk. These explanations could support targeted actions such as expediting a pending diagnostic test, escalating bed-management review, or re-contacting a delayed consultant service. The framework would shift ED discharge management from reactive reporting toward proactive operational decision support. Explainability is positioned as the foundation for clinician trust, workflow alignment, and accountable deployment.

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.

Inpatient transport is a critical but often under-recognized component of hospital operations. Delays in moving patients between wards, diagnostic areas, procedural suites, and discharge locations can disrupt clinical schedules, prolong waiting, and expose vulnerable patients to avoidable risk. Many hospital transport workflows still rely on first-in-first-out queues, dispatcher judgment, or simple proximity-based assignment. These approaches may overlook patient acuity, destination urgency, transporter workload, elevator congestion, and anticipated surges in unit-level demand. This article proposes an AI systems framework that continuously assigns a dynamic priority score to each inpatient transport request. The framework integrates patient acuity, destination urgency, transporter availability, elevator congestion, and forecasted unit-level transfer demand into a real-time dispatch logic. The framework includes a patient acuity stratification module, destination urgency classifier, transporter tracking layer, elevator congestion model, unit-level demand forecaster, and real-time prioritization engine. These components would operate as an integrated decision-support system rather than as isolated scheduling tools. The framework could support safer and more responsive transport decisions by aligning dispatch priority with both clinical risk and operational constraints. It would be expected to improve coordination among transporters, nursing units, procedural areas, and hospital command centers. A real-time AI transport prioritization framework offers a pathway toward more intelligent inpatient logistics. Its value should be evaluated through phased implementation, transparent governance, and careful assessment of clinical and operational consequences.

Home health agencies must assign clinicians to patients across geographically dispersed service areas while balancing patient needs, workforce qualifications, and operational efficiency. Scheduling decisions must account for clinical urgency, visit complexity, travel burden, and continuity of care. Manual scheduling cannot reliably integrate real-time acuity changes, dynamic travel conditions, clinician availability, and the risk of patients refusing or missing care. As a result, agencies may experience avoidable inefficiencies, delayed visits, fragmented follow-up, and increased coordinator workload. This article proposes an AI-based home health scheduling system that integrates patient acuity, geographic routing, clinician skill profiles, visit duration estimates, and missed-care risk prediction. The system is designed to generate adaptive daily schedules that can be revised as clinical and operational conditions change. The framework includes a patient acuity classifier, visit duration estimator, geographic routing engine, skill-mix matcher, missed-care risk predictor, and real-time scheduling dashboard. These components operate together to support clinically appropriate, geographically efficient, and operationally feasible visit plans. The proposed system would be expected to improve scheduling responsiveness, reduce unnecessary travel, better align clinician competencies with patient needs, and support proactive follow-up for patients at risk of missed care. Its value depends on integration with electronic health records, mobile workflows, coordinator oversight, and transparent decision support. An AI-based home health scheduling system provides a pathway toward a more responsive and coordinated home health operations model. By combining clinical prioritization with routing, workforce matching, and follow-up risk mitigation, such a framework could support both care quality and workforce efficiency.

Pharmacy verification is a critical safety checkpoint that protects patients from inappropriate medication use before administration. Verification backlogs can emerge when complex orders, high patient demand, and limited pharmacist capacity converge. Backlog management is often reactive because supervisors typically recognize risk only after the queue is already growing. By that point, turnaround times may already be delayed and urgent orders may compete with routine workload. This article proposes a conceptual deep learning model to forecast pharmacy verification backlog depth over short operational horizons. The model integrates medication order characteristics, pharmacist staffing, patient acuity, high-alert medication flags, and historical queue dynamics. The proposed model would use timestamped medication orders, staffing indicators, acuity signals, and queue-state variables as a multivariate temporal input stream. A recurrent or temporal convolutional neural network could transform these inputs into predicted queue length and backlog probability for upcoming time windows. Conceptually, the model would provide early warning of impending verification congestion before the queue becomes operationally disruptive. Such forecasts could support pre-emptive pharmacist reallocation, prioritization of urgent orders, and improved shift-lead situational awareness. A deep learning approach could shift pharmacy operations management from retrospective queue monitoring toward proactive backlog prevention. This model-oriented framework offers a foundation for future prospective evaluation in high-volume hospital pharmacy environments.