AI Agentic Workflow for Subrogation and Coordination of Benefits

This workflow automates the identification of claims where another payer is primary (Coordination of Benefits) or a liable third party exists (Subrogation). It ingests claims data, medical records, and policy details to detect recovery opportunities, replacing manual investigator searches with systematic, rules-based analysis. The operational upside comes from accelerating recovery cycles, increasing recovery rates, and reallocating specialist labor to complex exceptions, directly improving payer margins and reducing administrative waste.

Implementation integrates with core payer systems like claims adjudication engines, policy administration, and document management. The architecture uses an orchestrator (e.g., LangGraph) to manage specialized agents for evidence retrieval, case scoring, and correspondence drafting. Critical controls include configurable confidence thresholds for auto-submission, mandatory human review gates for high-value cases, and a immutable audit trail linking every recovery action to its source data and decision logic for regulatory defense.

This workflow directly targets the operational bottleneck of manually identifying claims where another payer is primary or a third party is liable. By automating evidence retrieval from claims systems, policy documents, and external sources, it reduces investigation time from days to minutes, directly improving recovery rates and reducing administrative cost. The solution ingests claims from core systems like Epic, HealthEdge, or Guidewire, triggering a multi-agent orchestration layer built on LangGraph or CrewAI to analyze liability and coverage rules.

Implementation requires integrating with payer APIs, document management systems, and legal correspondence platforms. Critical controls include human-in-the-loop approval for all outgoing demands, confidence scoring on agent findings, and a comprehensive audit trail linking every decision to source data and business rules. This ensures defensible compliance with state regulations and contractual obligations while maximizing financial recovery through systematic, automated execution.

A phased delivery model de-risks implementation while delivering immediate ROI. Phase 1 automates evidence gathering by deploying agents to scan claims, EHRs, and payer portals for primary coverage indicators and third-party liability signals. This reduces manual data collection by 60-80%, focusing analyst effort on high-potential cases. The architecture integrates with core claims adjudication systems (e.g., Facets, HealthRules) and document repositories via secure APIs, with all agent actions logged to an immutable audit trail for compliance.

Phase 2 introduces LLM agents to draft compliant correspondence and populate state-specific legal forms, cutting drafting time from hours to minutes. Human review gates are embedded for quality control, with agents providing explainable rationales for each recommendation. The final phase integrates with external recovery platforms and treasury systems, closing the financial loop. This staged approach allows for continuous tuning of agent logic against recovery rates, ensuring the system learns from exceptions and maximizes net recoverable dollars per FTE.

The operational upside is direct: automating the identification of primary payer liability and third-party recovery cases converts manual investigation labor into scalable, high-throughput asset recovery. Savings come from reduced claims leakage and lower FTE costs in post-payment review units. However, the architecture must embed controls—such as evidence validation loops, correspondence logging, and human-in-the-loop approval for high-value or complex cases—to prevent erroneous recoveries that could trigger regulatory or legal exposure. Integration points typically include claims adjudication engines (e.g., HealthRules Payer, FACETS), document management systems, and external data sources for liability verification.

Implementation follows a phased rollout, starting with a single high-volume, low-complexity claim type (e.g., motor vehicle accident claims in auto-medical states) to validate the evidence retrieval and scoring logic within a sandboxed environment. Each phase introduces new claim categories and more complex recovery scenarios, with performance monitored via dashboards tracking auto-resolution rates, false-positive rates, and recovery dollars per FTE. The final architecture deploys the orchestrator (e.g., LangGraph) to manage the multi-agent workflow, integrating with the core claims system via APIs and ensuring all correspondence and decisions are written back to the system of record for a complete, defensible audit trail.