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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.

Chronic obstructive pulmonary disease (COPD) is a leading cause of death, with exacerbations worsening functional decline, reducing quality of life, and increasing healthcare use. Current management remains reactive, with treatment often initiated only after symptoms worsen. Existing monitoring approaches struggle to distinguish between clinically significant deterioration and normal variability, leading to delayed intervention. This article proposes a digital twin framework combining patient-specific respiratory models with real-time wearable data to predict and manage COPD exacerbations proactively. The framework includes a mechanistic lung model, continuous data ingestion, a data assimilation module, an exacerbation prediction layer, and an alert system, enabling early detection of physiological deviations before severe symptoms arise. By supporting pre-emptive telehealth, medication adjustments, and patient self-management with clinician oversight, this approach could shift COPD care from reactive to personalized, proactive management, pending robust modeling, reliable sensing, and real-world validation.