Context-Driven Patient Knowledge Fusion
Keywords:
Context-Aware Clinical Intelligence, Clinical Decision Support Systems (CDSS), Predictive Care Coordination, Proactive Healthcare Analytics, Patient Knowledge Modeling, Clinical Workflow Optimization, Context-Aware Computing, Healthcare Informatics, Predictive Modeling, Artificial Intelligence (AI).Abstract
Clinical decision support systems assist healthcare professionals at the point of care, addressing complex tasks such as disease diagnosis and prognosis, therapy selection, and medication interaction checking. However, these systems remain largely reactive, generating alerts and recommendations only when specific conditions are met within the clinical workflow—often too late to enable effective preventive action. What is needed instead is a broader, domain-independent intelligence capable of proactively analyzing clinical data to predict potential problems and identify preventive opportunities across all clinical contexts and levels of granularity, while coordinating care among providers, patients, and health information systems.
Care coordination constitutes a second, higher level of abstraction in this framework. Whereas reactive systems respond to clinical events after they occur, proactive systems anticipate these events and initiate preparatory actions—notifications, reminders, and alerts—in advance. Predictive care coordination thus operates not only within the clinical-problem domain but extends across all related domains. Domain-specific intelligence modules—for example, predictive models for diabetes management in acute care—can be integrated into this architecture, forming a domain-independent system that satisfies the core requirements of context-aware clinical intelligence. This paper formally defines such a system as the Context-Aware Clinical Intelligence Framework for Unified Patient Knowledge Modeling and Predictive Care Coordination.
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