Context-Aware Patient Knowledge for Predictive Care
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 (CDSS) assist healthcare professionals at the point of care, addressing tasks such as disease diagnosis and prognosis, therapy selection, and medication-interaction checking. Despite technological advances, most CDSS remain fundamentally reactive: alerts and recommendations fire only when specific conditions are met within the clinical workflow, often too late to support effective prevention.
We propose a broader, domain-independent form of clinical intelligence that proactively analyzes patient data to predict potential problems and preventive opportunities across all clinical contexts and levels of granularity—and that initiates coordinated action among providers, patients, and health information systems accordingly.
Care coordination forms a second, higher level of abstraction in this model. Where reactive systems respond to clinical events after they occur, proactive systems anticipate events and act in advance, issuing notifications, reminders, and alerts before problems arise. This predictive coordination operates not only within a single clinical domain but across domains, allowing specialized intelligence modules (e.g., a predictive model for diabetes management in acute care) to be integrated into a unified, domain-independent architecture.
We formalize this approach as the Context-Aware Clinical Intelligence Framework, a model for unified patient knowledge representation and predictive, cross-domain care coordination.
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Copyright (c) 2024 Carlos Fernnadez (Author)
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