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Hospital discharge delays are costly, disrupt inpatient capacity, and expose patients to avoidable iatrogenic harm. Early identification of patients likely to be ready for discharge could improve patient flow and reduce operational bottlenecks. Current discharge decisions often rely on subjective judgment, fragmented documentation, and sequential review by multiple clinical teams. No single tool routinely integrates the morning snapshot of clinical readiness. This article proposes a predictive model that estimates the probability of same-day discharge readiness by 9 am. The model uses morning laboratory results, active medication orders, vital sign stability, mobility documentation, and pending consultation status. The proposed approach is a supervised classification model using gradient-boosted trees trained on historical inpatient encounters. Features would be assembled from electronic health record data available before morning rounds. Conceptually, the model would generate a calibrated discharge readiness list for clinical review. This list could help care teams focus on borderline patients and support bed-management forecasting. The model could accelerate discharge throughput while maintaining safety by surfacing hidden readiness signals. It is intended to complement, not replace, clinical judgment.

Hospital discharge delays are rooted in fragmented coordination across clinical, administrative, and logistical work. Discharge readiness depends on many interdependent tasks that must be completed in the correct sequence. No current system fully orchestrates discharge sub-tasks by reasoning across care plans, medication reconciliation, transport requests, follow-up scheduling, and pending orders in real time. Existing digital tools often support isolated functions rather than end-to-end coordination. This article proposes an agentic AI system that perceives discharge requirements, plans task sequences, executes coordination actions, and monitors completion under human supervision. The system is conceptual and should be understood as an emerging design pattern rather than an evaluated intervention. The proposed system includes a task decomposition engine, goal-oriented reasoning core, EHR data adapters, follow-up scheduling rules engine, transport request interfaces, and human override dashboard. These components would allow the agent to coordinate discharge logistics while preserving clinician authority. The system would function as a virtual discharge coordinator that continuously tracks task status and identifies stalled work. It could reduce idle coordination time, prevent overlooked tasks, and allow nurses and case managers to focus on higher-value clinical and relational work. Agentic AI could transform discharge management from a manual, interruption-prone process into an automated, accountable, and scalable workflow. Safe deployment would require interoperability, auditability, constrained autonomy, and rigorous evaluation.