Automation Workflow for AI-Driven MedDRA Coding of Adverse Events

Manual MedDRA coding is a major bottleneck, consuming 15-30 minutes per ICSR. An AI-driven workflow automates the initial coding suggestion for verbatim terms, slashing this to under 5 minutes. This directly compresses the overall case processing timeline, enabling faster regulatory submissions and reducing backlog pressure, especially during high-volume periods like product launches or safety updates.

Human coders introduce variability. An AI agent applies the MedDRA hierarchy and historical coding patterns consistently across all cases and geographies. This creates a standardized, defensible audit trail. The workflow logs the LLM's rationale, suggested codes, and any overrides, providing clear evidence for inspections and reducing remediation effort during audits.

Instead of spending cycles on routine terms, your most experienced pharmacovigilance associates shift to validating AI suggestions and handling ambiguous, complex, or novel terms. This elevates their role, improves job satisfaction, and focuses human expertise where it delivers the most value—ensuring high-risk or unusual cases are coded with precision.

The same AI coding engine can be deployed in batch mode to remediate legacy case backlogs, standardizing historical data for reliable aggregate analysis and signal detection. This unlocks trapped data value, improves the quality of PSURs and risk-benefit assessments, and supports more robust historical comparisons without a massive manual rework project.

Automation creates a scalable, variable-cost model for coding. As case volumes fluctuate—due to new products, campaigns, or external events—the AI layer absorbs the increase without a linear rise in FTEs or contractor costs. This converts a fixed operational expense into a flexible capability, protecting margins and enabling the PV function to scale with the business.

The workflow is built as a microservice that plugs into your existing safety database (e.g., Oracle Argus, Veeva Safety, ARISg) via API. It acts as an intelligent layer within the case processing UI or batch job, requiring no 'rip-and-replace' of core systems. This minimizes disruption, accelerates ROI, and ensures the automation works within your established governance and QC gates.

This core LLM agent ingests verbatim adverse event terms and patient context. It performs a retrieval-augmented search against the full MedDRA hierarchy (SOC, HLGT, HLT, PT, LLT) and historical coding patterns to suggest the most precise PT/LLT code. It outputs a coded term with a confidence score and a reasoning trace linking the verbatim to the dictionary logic, which is critical for auditability.

A secondary agent acts as a quality gate, reviewing the primary agent's output. It checks for consistency with product-specific expected event profiles and flags discrepancies (e.g., a 'headache' verbatim coded to a 'migraine' PT without supporting context). It routes ambiguous or low-confidence codes to a human-in-the-loop queue, ensuring only clear-cut cases proceed automatically.

For processing legacy backlogs or periodic case batches, this orchestrator manages high-volume workflows. It distributes verbatim terms across parallel coding agents, aggregates results, and generates reconciliation reports. It integrates with job scheduling systems (e.g., Apache Airflow) and provides dashboards for tracking progress against SLAs for cleanup projects.

The critical connective tissue between AI agents and the production safety system (e.g., Oracle Argus, Veeva Safety). This layer handles bidirectional API calls: fetching uncoded verbatims and writing back approved codes with full audit metadata. It manages transaction integrity, handles system-specific field mappings, and ensures zero data loss during the handoff.

The human-in-the-loop interface for escalated cases. It presents the verbatim term, the AI's suggested code with reasoning, and relevant historical coding decisions side-by-side. The reviewer can accept, override, or flag for committee review. All actions are logged, training the underlying models and creating a defensible audit trail for regulatory inspection.

A continuous oversight system that tracks key metrics: coding accuracy vs. human gold standard, escalation rates by term type, and agent latency. It triggers alerts for performance drift and manages the controlled rollout of updated coding models. This component ensures the workflow remains reliable, compliant, and improves over time without introducing unvetted changes.

The primary end-users who transition from manual lookups to validating AI-suggested codes. Their buy-in is critical. The workflow must integrate seamlessly into their existing case processing platforms (e.g., Oracle Argus, Veeva Safety) and provide clear explanations for code suggestions to build trust. Adoption reduces their repetitive search burden by ~60%, allowing focus on complex, ambiguous terms.

Own the final medical accuracy and governance of coded data. They define the escalation logic and approval gates for ambiguous terms flagged by the AI agent. Their involvement is required to establish confidence thresholds, review edge cases in the validation queue, and sign off on the workflow's operational procedures before go-live.

Responsible for the integration architecture and runtime orchestration. They deploy the LangGraph or similar orchestration layer, manage APIs to the MedDRA dictionary and the safety database, and ensure the workflow meets enterprise security, scalability, and audit trail requirements. They partner with the AI team to operationalize the coding agent.

Builds and maintains the core LLM agent and validation models. Their work includes fine-tuning models on historical coding patterns, implementing retrieval-augmented generation (RAG) over the MedDRA hierarchy, and designing the confidence scoring logic that determines auto-accept vs. human review. They own model performance monitoring and retraining cycles.

Ensures the automated workflow is validated and audit-ready. They develop the test scripts for the coding agent, oversee the 100% automated QC checks on its output, and define the anomaly detection rules for the audit trail. Their sign-off is mandatory before production deployment and for any subsequent model changes.

Drive the business case and change management. They quantify the ROI from reduced coding cycle time and improved consistency, manage the phased rollout (e.g., pilot with one product line), and train end-users. They own the operational metrics dashboard and are accountable for the workflow's impact on overall case processing throughput.