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In an era of escalating healthcare demands, hospitals face persistent challenges in maintaining operational resilience amid fluctuating patient volumes, resource constraints, and unforeseen disruptions. This conceptual manuscript introduces a novel framework for real-time hospital capacity intelligence, designed to enhance decision-making through integrated AI-driven analytics and interoperable data ecosystems. Drawing on theoretical foundations from clinical AI architectures, healthcare analytics infrastructures, and decision support pipelines, the proposed system emphasizes seamless integration with electronic health records (EHRs), governance mechanisms for AI deployment, and dynamic monitoring to mitigate risks such as capacity overloads. The framework outlines a layered architecture that orchestrates data exchange, predictive analytics, and adaptive resource allocation, ensuring interoperability across clinical workflows. Key conceptual formulas are presented to interpret risk propagation in capacity management, decision confidence in real-time intelligence, and governance load in system operations. By synthesizing recent peer-reviewed literature on AI governance and clinical interoperability, this work highlights the potential for such frameworks to foster resilient hospital operations without relying on empirical data or model evaluations. Implications for healthcare systems include improved preparedness for surges, ethical AI integration, and scalable intelligence ecosystems. This theoretical exploration underscores the need for robust, AI-augmented infrastructures to support sustainable healthcare delivery.

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.