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Client Context
Pharmacovigilance AI-Assisted Drug Safety Signal Detection
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Industry:
Pharmaceuticals / Global Drug Safety
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Geography:
US & Europe
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Engagement Type:
Pharmacovigilance automation
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Scope:
Literature, EHR, social data, call-center logs, post-market surveillance
Challenges
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 Apporach
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Built a multi-source ingestion pipeline for EHR, ICSRs, social posts, and published sources.
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Applied NLP & named-entity recognition to extract event, drug, dose, duration & seriousness dimensions.
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Developed safety signal scoring models using disproportionality metrics (EBGM / IC) + severity likelihood.
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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.
30+ years of Industry experience in delivering pioneering data and QA solutions.
