RAG-Based AI Agent System For Population Health & Care Coordintion
Challenge
The fundamental challenge was scale. The care coordination team could not manually review the clinical and claims data required to identify every member who would benefit from proactive outreach within a clinically meaningful timeframe. Risk stratification was largely retrospective, intervention triggers were inconsistent, and the effort required to coordinate care across fragmented data sources left coordinators with insufficient time for the high-complexity cases that most needed their attention. The client needed a system that could continuously monitor population data, apply predictive analytics to surface risk, automate routine coordination workflows, and integrate with existing clinical data infrastructure — all within a fully HIPAA-compliant environment.
The fundamental challenge was scale. The care coordination team could not manually review the clinical and claims data required to identify every member who would benefit from proactive outreach within a clinically meaningful timeframe. Risk stratification was largely retrospective, intervention triggers were inconsistent, and the effort required to coordinate care across fragmented data sources left coordinators with insufficient time for the high-complexity cases that most needed their attention. The client needed a system that could continuously monitor population data, apply predictive analytics to surface risk, automate routine coordination workflows, and integrate with existing clinical data infrastructure — all within a fully HIPAA-compliant environment.
Solution
We delivered a multi-agent AI system that continuously processes member data from connected clinical and claims sources, applying predictive analytics to identify individuals at elevated risk of deterioration, hospitalization, or care gap. RAG pipelines surface relevant clinical guidelines and member history at the moment of decision, enabling the system to generate personalized, evidence-grounded care recommendations. Automated care workflow agents initiate outreach sequences, schedule follow-ups, and track intervention status, routing complex cases to human coordinators with full context already assembled. The system integrates with the client's FHIR and HL7 data infrastructure and operates within a HIPAA-compliant cloud environment built on inVerita's AWS partnership — an infrastructure layer the team had standing capability to deliver without a learning curve.
We delivered a multi-agent AI system that continuously processes member data from connected clinical and claims sources, applying predictive analytics to identify individuals at elevated risk of deterioration, hospitalization, or care gap. RAG pipelines surface relevant clinical guidelines and member history at the moment of decision, enabling the system to generate personalized, evidence-grounded care recommendations. Automated care workflow agents initiate outreach sequences, schedule follow-ups, and track intervention status, routing complex cases to human coordinators with full context already assembled. The system integrates with the client's FHIR and HL7 data infrastructure and operates within a HIPAA-compliant cloud environment built on inVerita's AWS partnership — an infrastructure layer the team had standing capability to deliver without a learning curve.
Results
The engagement moved from signed contract to a working system in production faster than the client had anticipated, a direct result of inVerita's existing healthcare domain knowledge reducing the time typically spent aligning a development team on clinical workflows and compliance requirements. In production, the proportion of at-risk members identified and contacted within a clinically meaningful window increased significantly, and care coordinators reported spending materially more of their working time on complex interventions rather than data gathering and routine follow-up.
The engagement moved from signed contract to a working system in production faster than the client had anticipated, a direct result of inVerita's existing healthcare domain knowledge reducing the time typically spent aligning a development team on clinical workflows and compliance requirements. In production, the proportion of at-risk members identified and contacted within a clinically meaningful window increased significantly, and care coordinators reported spending materially more of their working time on complex interventions rather than data gathering and routine follow-up.