Drug Safety Signal Detection

Industry

Pharmaceuticals / Global Drug Safety

Engagement Type

Pharmacovigilance automation

Scope

Literature, EHR, social data, call-center logs, post-market surveillance

Geography

US & Europe

Project overview

Pharmacovigilance AI-Assisted Drug Safety Signal Detection

Challenges

  • Manual review of large volumes of case narratives delayed signal identification.

  • Structured and unstructured adverse event data existed in silos across multiple systems.

  • Signal prioritization was reactionary rather than proactive.

Our Approach

Built a multi-source ingestion pipeline for EHR, ICSRs, social posts, and published sources.

Applied NLP & named-entity recognition to extract event, drug, dose, duration & seriousness dimensions.

Developed safety signal scoring models using disproportionality metrics (EBGM / IC) + severity likelihood.

Delivered safety dashboards for medical reviewers with drill-down root cause views.

Impact Delivered

  • Reduced case narrative review time by approximately 60% through auto-extracted adverse event summaries.

  • Improved safety-signal detection cadence from quarterly to bi-weekly.

  • Enhanced regulatory submission accuracy and consistency across safety teams.