AI-Powered Workflow for Continuous Disproportionality Analysis (e.g., PRR, ROR)

Traditional periodic batch analysis creates a dangerous latency between case intake and signal detection. A real-time workflow automates the continuous calculation of disproportionality measures (PRR, ROR) against a dynamic comparator database like the FDA's FAERS or an internal historical corpus. This architecture ingests ICSRs from your safety database (e.g., Oracle Argus, Veeva Safety), normalizes the data, and executes statistical agents upon each new case. The operational upside is a 70-90% reduction in signal detection lag, allowing safety committees to investigate potential issues before they escalate, directly impacting patient safety and regulatory risk.

Implementation requires orchestrating data pipelines, stateless calculation agents, and configurable alerting logic. The workflow integrates with your existing signal management platform (e.g., ArisGlobal, IQVIA) to populate review queues. Critical controls include confidence scoring for alerts, audit trails for all calculations, and medical reviewer approval gates before any regulatory action. Rollout is typically phased, starting with a single product line to validate threshold accuracy and exception handling before scaling to the full portfolio, ensuring the system reduces noise while capturing true signals.

This workflow automates the continuous calculation of disproportionality measures like the Proportional Reporting Ratio (PRR) and Reporting Odds Ratio (ROR). It eliminates the latency and labor of manual, periodic batch analyses by processing each new ICSR as it is validated in the safety database. The operational upside is earlier signal detection, which translates to faster risk mitigation, reduced compliance exposure, and more proactive safety management. The core technical challenge is orchestrating a high-integrity data pipeline that feeds a statistical agent while maintaining an auditable comparator database.

Implementation integrates with legacy PV systems like Oracle Argus or Veeva Safety via secure APIs. The orchestrator, built on LangGraph, manages state and triggers the statistical agent upon each new case. This agent runs the disproportionality analysis against the continuously updated comparator database. Alerts exceeding configurable thresholds are packaged with relevant case listings and routed to a signal management meeting queue in platforms like SharePoint or Teams. Critical controls include versioning the comparator database, logging all calculations for audit, and maintaining a mandatory medical review gate before any regulatory action.

Phase 1 establishes the core data pipeline, ingesting ICSR streams from Oracle Argus or Veeva Safety into a time-windowed comparator database (e.g., Snowflake). A statistical agent, built with Python and orchestrated via Prefect, calculates PRR/ROR on each new case batch. Alerts exceeding configurable thresholds are logged to a dashboard, but all actions remain manual. This MVP validates data quality, calculation accuracy, and provides a baseline for alert volume without triggering automated downstream processes.

Phase 2 introduces automated routing and integrated review. The orchestrator now routes high-confidence signals directly into the signal management workflow within the PV system (e.g., ARISg), generating draft packets for committee review. A mandatory medical reviewer approval gate is enforced before any external reporting. Phase 3 adds predictive scaling and closed-loop learning, where the system retrains threshold models based on reviewer feedback and integrates with downstream expedited reporting workflows, creating a fully autonomous, audit-ready signal detection layer that reduces manual analysis by over 80%.

This workflow eliminates the latency of quarterly signal detection runs by operationalizing continuous disproportionality analysis. It automates the ingestion of new ICSRs, calculates PRR/ROR against a live comparator dataset, and flags emerging safety signals for immediate review. The operational upside comes from detecting potential issues weeks or months earlier, allowing proactive risk management and reducing the compliance risk of delayed signal identification. Implementation requires tight orchestration between your safety database (e.g., ARISg, Oracle Argus) and a statistical computation layer.

The architecture centers on a statistical agent, implemented in Python/R, that executes upon each new validated case batch. It queries the live database—continuously updated from global sources—to compute measures against product and event comparators. Alert thresholds are configurable by product risk profile. Governance is critical: all flagged signals route through a dedicated queue for the weekly signal management meeting, with full audit trails of calculations and data snapshots. Phased rollout starts with a single product line, running in parallel with legacy processes to validate accuracy before full-scale deployment across the portfolio.

This workflow automates the most computationally intensive and repetitive task in quantitative signal detection: running disproportionality analyses. By triggering calculations on each new ICSR intake or at configurable intervals, it eliminates the lag of monthly or quarterly batch jobs. The operational upside is earlier identification of potential safety signals, which translates to faster regulatory response and risk mitigation. Savings come from reducing manual data extraction, statistical programming labor, and the risk of missed signals due to analysis latency. Implementation requires a robust data pipeline from your safety database (e.g., Oracle Argus, Veeva Safety) to a dedicated analysis engine.

The core architecture involves a statistical agent—often a containerized service using Python/R—that executes the disproportionality algorithms. It must integrate with a continuously updated comparator database, which could be an internal historical dataset or a licensed external source. Critical controls include configurable alert thresholds, automated data pack generation for review meetings, and immutable logging of all calculations for auditability. Exception handling routes statistical anomalies or data quality failures to a data engineering queue. Deployment is typically on a cloud-based Kubernetes cluster for scalability, with results fed into a dashboard (e.g., Tableau, Power BI) and the signal management module of your PV system.