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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.