AI-Powered Workflow for Batch Coding and Validation of Legacy Case Backlogs

Legacy case backlogs are a compliance liability and a missed analytical opportunity. This workflow processes thousands of uncoded or poorly coded historical ICSRs (Individual Case Safety Reports) in a structured batch pipeline. By applying NLP agents to suggest MedDRA and WHO-DD codes at scale, you convert a static, unusable data mass into a clean, queryable asset for historical trend analysis, supporting PSURs, signal detection, and regulatory inquiries with a complete data picture.

Manual remediation of a 50,000-case backlog can take a team 6-12 months, stalling other PV work. This automated batch pipeline, orchestrated with tools like LangGraph or Apache Airflow, processes cases in parallel, applying coding agents, validation rules, and routing logic. A focused pilot can clear a 10,000-case subset in 3-4 weeks, demonstrating ROI and operational feasibility before full-scale rollout, dramatically accelerating the time-to-value of your historical data.

Uncoded backlogs represent a significant compliance gap during regulatory inspections. This workflow embeds automated quality control (QC) checks and creates a defensible audit trail for every code suggestion, reviewer action, and final decision. By implementing a human-in-the-loop review dashboard with side-by-side comparisons of source verbatims and AI suggestions, you ensure medical oversight while systematically closing a high-risk data integrity exposure, strengthening your overall pharmacovigilance posture.

Beyond a one-time project, this workflow architecture creates a reusable asset. The batch processing pipeline, integrated with your safety database (e.g., Oracle Argus, Veeva Safety), and the configured coding/validation agents can be re-deployed for future data migrations, acquisitions, or periodic data quality sprints. This turns a recurring cost center into a controlled, efficient operational process, improving long-term PV operational resilience and data management maturity.

Manual coding of historical data often leads to inconsistent application of MedDRA terms over time. The AI-powered workflow applies a consistent, rule-based logic to every case in the backlog, reducing variance. Ambiguous terms are flagged for expert review via a centralized exception queue. This systematic approach not only cleans the backlog but also establishes a higher standard of data quality that benefits all downstream safety analyses, reducing the need for future re-coding efforts.

The primary business impact is labor leverage. By automating the repetitive, low-judgment task of backlog coding, your highly trained PV associates and medical reviewers are freed from months of tedious work. They can redirect their time to higher-value activities like complex case assessment, signal evaluation, and medical review of new, real-time cases. This directly improves the efficiency and effectiveness of your core pharmacovigilance operations without increasing headcount.

The core workflow engine (e.g., LangGraph, Apache Airflow) that manages the end-to-state batch job. It ingests uncoded case files from legacy safety databases (e.g., Oracle Argus, ARISg), chunks them into parallelizable batches, routes them through specialized agents, and tracks progress against the total backlog. It handles retries, manages state persistence, and feeds a real-time dashboard showing cases processed, throughput, and estimated completion time.

A specialized LLM agent fine-tuned on historical coding decisions. It processes unstructured verbatim terms and drug names from each case, suggests the most precise MedDRA PT/LLT and WHO-DD codes, and provides a confidence score and rationale. It leverages Retrieval-Augmented Generation (RAG) over the official dictionaries and internal coding guidelines to improve accuracy and consistency, reducing coder rework.

A configurable interface where human PV coders review the AI's suggestions. High-confidence codes can be approved in bulk, while low-confidence or ambiguous suggestions are routed to a prioritized exception queue for expert review. The system captures all reviewer overrides, feeding them back as training data to improve the model. This gate ensures final data quality and provides the necessary audit trail for compliance.

A critical compliance component that logs every action—from initial case ingestion, through each agent's suggestion, to the final human approval or override. It produces a defensible record of the entire remediation project, supports rollback if needed, and automatically reconciles the newly coded data back into the production safety database, ensuring data integrity and traceability for future inspections.

A real-time operational dashboard built for project managers and PV leadership. It visualizes key metrics: total backlog size, cases processed per hour, average coding time, agent accuracy rates, and reviewer throughput. It flags bottlenecks (e.g., a slow review queue) and generates daily summary reports on project velocity and forecasted completion, enabling data-driven resource allocation.

A final validation layer that runs automated QC checks on the batch of newly coded cases before they are committed to the live database. It checks for data completeness, internal consistency (e.g., event date vs. drug start date), and adherence to business rules. Any anomalies are routed back to the review queue, ensuring the cleaned backlog meets the same quality standards as routine case processing.