Linking Providers and Payers for Better Care
Keywords:
Health services research, data integration, clinical intersections, funding intersections, clinical care disruptions, anticipated demand, exploratory modelling, deployment readiness, sentinel indicators, learnings and corrective actions.Abstract
The recent COVID-19 pandemic has emphasized the need for resilience in health service delivery. One avenue to support resilience is through an integrated clinical-payer intelligence infrastructure that leverages all data generated from routine clinical practice and operations, not just for service reimbursement or regulatory compliance, but also for real-time service delivery decision support. Current health data usability is limited by barriers in interoperability, privacy, security, clinical validity, and governance. Addressing these barriers through a unified intelligence fabric—encompassing data commons, analytics pipelines, dashboards, and predictive models that span both clinical and payer data—could enhance the resilience of not only individual health systems but also geographically dispersed systems serving the same population.
The continuity of care and quality of clinical outcomes in any health system can deteriorate during unexpected events (e.g., COVID-19, hurricanes, wildfires) that divert resources away from routine functions or when natural disasters severely disrupt the normal operation of health services. A unified clinical-payer intelligence fabric designed to address these challenges has the potential to simulate outcome scenarios for unplanned disruptions and identify clinical conditions or care pathways requiring expedited planning or resource allocation. Transitioning from government-mandated plans (layout of service delivery–insurance claims coordination) to formalized CIA—network-enabled shared responsibility—is also critical for real-time patient, institutional, and payer collaboration to ensure the completion of proposed care orders along the approved pathway, improving service delivery quality at lower cost.
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