Search

In acute care settings, where patient interactions and healthcare worker movements create complex contact networks, inferring transmission risks for infectious diseases remains a critical challenge for enhancing preventability. This conceptual manuscript introduces a novel contact-integrated risk evaluation system (CIRES), an AI-driven architectural framework designed to model contact-structured data for analytical inference of transmission pathways and preventability opportunities. Grounded in healthcare analytics infrastructures and clinical decision support pipelines, CIRES orchestrates interoperability across electronic health records (EHR) intelligence ecosystems and workflow integration models to enable theoretical risk propagation assessments without empirical data reliance. The framework incorporates layered modules for contact mapping, risk inference, and governance monitoring, facilitating interpretive formulas that capture decision confidence and resource allocation dynamics. By synthesizing recent advancements in AI governance and deployment systems, this work highlights how contact-structured modeling can theoretically optimize acute care protocols, reduce nosocomial transmission, and inform policy through analytical foresight. Emphasizing ethical interoperability and system resilience, CIRES represents a paradigm for AI-orchestrated preventability analysis, offering insights into scalable infrastructures that align with evolving healthcare demands. This conceptual approach underscores the potential for AI to transform transmission risk management in resource-constrained environments, paving the way for future theoretical explorations in clinical AI architectures.