AI Agentic Workflow for Conflict of Interest Disclosure Monitoring

Manual COI monitoring requires staff to cross-reference provider disclosures against CMS Open Payments, internal purchasing systems, and grant databases—a process taking 15-20 minutes per provider, prone to oversight. An agentic workflow automates this search across structured and unstructured sources, performing the same task in seconds and freeing 70-80% of compliance analyst time for higher-value investigation and policy work.

Uncaught conflicts can lead to Stark Law violations, False Claims Act exposure, and reputational damage. This workflow provides continuous, auditable monitoring, creating a defensible record of due diligence. It shifts compliance from a periodic audit scramble to a continuous control, materially reducing the risk of multi-million dollar penalties and corrective action plans from OIG or accrediting bodies.

Delays in COI clearance stall IRB approvals, grant submissions, and vendor contracting. Automated pre-screening flags potential issues early, allowing for proactive resolution. The workflow integrates with systems like Epic, SAP Ariba, and Click IRB to route cleared individuals automatically, shrinking approval wait times from weeks to days and accelerating time-to-research and time-to-contract.

Manual processes lead to inconsistent data entry and reporting errors. The workflow normalizes data from disparate sources (disclosure forms, ERP, CRM), applies validation rules, and generates reconciliation reports with clear data lineage. This creates a single source of truth for COI status, improving the accuracy of mandatory reporting to NIH, FDA, and other funding/regulatory agencies.

As institutions grow or merger activity increases, manual monitoring becomes unsustainable. This agentic architecture scales elastically to handle thousands of providers and transactions without proportional headcount increases. It allows the compliance office to support enterprise growth and M&A integration without becoming a bottleneck or a spiraling cost center.

During an audit, you must justify every flagged and cleared conflict. This workflow doesn't just output a binary flag; it generates an explainability layer—a report citing the source data, the applied rule (e.g., "payment > $5,000 from vendor X in system Y"), and the confidence score. This pre-packaged audit trail turns a compliance burden into a strategic asset, demonstrating rigorous oversight to regulators.

This agent ingests provider disclosure submissions from forms, emails, or portals, extracting entities (company names, payment amounts, relationships) and normalizing them against a master reference list. It handles unstructured text, PDFs, and spreadsheet data, creating a structured, queryable record for downstream comparison. Poor data quality at this stage is the primary bottleneck; the agent must flag incomplete or ambiguous submissions for human follow-up before processing.

A specialized agent queries external databases like the CMS Open Payments API and commercial data providers using the normalized entity list. It performs fuzzy matching on provider and company names, retrieves reported payments or transfers of value, and calculates variance against internal disclosures. This agent operates on a scheduled batch basis but can be triggered by new disclosure submissions, requiring robust API error handling and data freshness controls.

This agent connects to internal ERP (e.g., SAP, Oracle), procurement, and grant management systems to identify financial transactions, purchase orders, or research awards involving the disclosed entities. It correlates internal spending data with disclosure statements to uncover conflicts that may exist only within the organization's own records, a critical layer often missed by manual processes.

The core orchestrator (built with frameworks like LangGraph or Temporal) receives findings from the cross-reference agents. It applies configurable business rules—based on payment thresholds, relationship types (e.g., research vs. consulting), and institutional policy—to assign a risk score and severity flag. High-risk, clear discrepancies are routed automatically for compliance officer review; medium-risk items may trigger automated clarification requests; low-risk variances are logged for audit.

Flagged discrepancies are packaged into structured cases within a compliance workflow system (or integrated with ServiceNow, Salesforce). Each case includes the original disclosure, external/internal evidence, the calculated variance, and the applied rule. Compliance officers review, adjudicate, and add notes. The system automatically generates reconciliation reports and a complete, immutable audit trail of all agent actions, evidence reviewed, and human decisions for regulatory defense.

A production deployment requires phased rollout, starting with a pilot cohort. Key controls include: a human-in-the-loop approval gate for all high-severity flags; regular model validation to ensure matching logic hasn't drifted; explainability reports showing why a conflict was flagged; and integration with the institution's existing compliance training and policy update cycles to close the loop. The architecture must support SOC 2 and HIPAA compliance where applicable.

Owns the business case, defines risk thresholds, and approves the escalation logic. They require a system that produces audit-ready reconciliation reports with clear lineage from external data (Open Payments) to internal flags. The workflow must embed explainability layers to justify each potential conflict flag, supporting defensible decisions during regulatory reviews or internal investigations.

Responsible for integrating the workflow with EHR (e.g., Epic), purchasing systems, and HR databases. Must design a secure data pipeline that normalizes provider identifiers across systems and handles PHI appropriately. The architecture typically involves a LangGraph or custom orchestration layer to sequence agents for data retrieval, matching, and report generation, ensuring scalability and observability.

Key stakeholders who must trust the system's outputs to avoid false positives that damage provider relationships. They provide domain context for refining matching logic (e.g., distinguishing relevant vs. irrelevant financial relationships). The workflow design must include configurable approval gates where department chairs can review and annotate flags before official escalation to compliance.

Manages the phased rollout, starting with a pilot cohort (e.g., high-risk specialties) to validate matching accuracy and user acceptance. This role designs the exception-handling workflows and trains compliance officers on using the agent-generated rationales. Critical success depends on sequencing the deployment to demonstrate quick wins in manual effort reduction before expanding to enterprise-wide monitoring.

Establishes controls for the AI components, including regular bias checks on flagging rates across departments and model audit logs for all agent decisions. This role ensures the workflow's matching algorithms remain fair and its explainability outputs are sufficient for the institution's risk tolerance. They define the retraining triggers based on new disclosure forms or regulatory updates.

Quantifies the value from automating a process that typically requires FTEs to manually cross-reference spreadsheets and PDFs. The upside comes from risk mitigation (avoiding Stark Law penalties), labor leverage (reallocating staff to higher-value investigations), and throughput gains (monitoring 100% of disclosures vs. a sample). The build must be justified against the cost of manual audit failures and staff attrition.