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The rapid evolution of artificial intelligence (AI) in healthcare necessitates innovative frameworks to optimize hospital operations. This conceptual manuscript proposes the Digital Twin-Enabled Operations Resilience Architecture (DTORA), a novel intelligence framework that leverages digital twins to simulate, monitor, and enhance hospital operational dynamics. DTORA integrates real-time data from electronic health records (EHRs), clinical workflows, and interoperable systems to create virtual replicas of hospital processes, enabling predictive analytics and decision support without empirical testing. The framework’s layered structure includes a simulation core, intelligence orchestration layer, and governance feedback loop, addressing challenges in resource allocation, workflow efficiency, and risk mitigation. By synthesizing recent literature on clinical AI architectures and healthcare analytics infrastructures, DTORA emphasizes theoretical interoperability, AI governance, and human-AI integration. Conceptual formulas model risk propagation, decision confidence, and monitoring burden, providing interpretive tools for system design. This work highlights the potential of digital twins to transform hospital intelligence ecosystems, fostering resilient operations amid data complexities and regulatory demands. While theoretical, DTORA offers a blueprint for future deployments, underscoring the need for ethical monitoring and seamless integration in diverse clinical settings.

Medical equipment shortages and surpluses often coexist in hospitals because utilization is observed after demand has already emerged rather than predicted in advance. Expensive mobile assets may sit idle in low-demand areas while clinicians search for pumps, monitors, beds, ventilators, or imaging-related devices in high-demand units. Current equipment management often depends on manual counts, static par levels, delayed inventory review, and reactive dispatching. These practices do not fully integrate forward-looking signals already present in procedure schedules, unit demand projections, maintenance logs, and device availability records. This article proposes an artificial intelligence framework that ingests real-time location system data, procedure schedules, maintenance logs, unit-level demand forecasts, and device availability records. The framework is designed to generate continuous predictions of equipment utilization and impending shortages across hospital units. The framework includes a real-time location ingestion module, a procedure-schedule demand mapper, a maintenance downtime predictor, a unit-level demand forecaster, a multi-source fusion engine, and an operational decision-support dashboard. These components would convert fragmented hospital data streams into coordinated predictions for equipment planning. The framework would shift equipment management from reactive searching toward proactive allocation. It would be expected to reduce avoidable idle time, improve visibility of available equipment pools, and support earlier decisions about staging, redistribution, maintenance rescheduling, or rental planning. An AI-enabled equipment utilization framework offers a pathway toward a data-driven and anticipatory medical equipment supply chain. Such a framework could help hospitals coordinate scarce assets more effectively in complex, high-pressure clinical environments.