Adverse Event Intelligence
Real-time monitoring and analysis of safety signals for proactive pharmacovigilance.
The Challenge
Safety teams were manually reviewing thousands of adverse event reports monthly, creating dangerous delays in identifying emerging safety signals.
The sheer volume of reports made it impossible to detect subtle patterns that might indicate a larger safety issue.
Regulatory deadlines were being missed because analysis could not keep pace with incoming reports.
“When patient safety is on the line, every day of delay matters.”
The Solution
We built a real-time monitoring system that automatically analyzes incoming adverse event reports, identifies potential signals, and alerts safety teams to emerging patterns.
The system uses semantic analysis to understand report narratives, correlate related events, and assess signal strength.
Automated prioritization ensures safety teams focus on the most critical issues first.
Key Features
- Real-time adverse event report processing
- Semantic analysis of narrative descriptions
- Pattern detection across report populations
- Signal strength scoring and prioritization
- Automated regulatory report generation
Implementation
Technical Approach
- Claude API for narrative analysis
- Kafka for real-time event streaming
- Elasticsearch for signal detection
- Python analytics pipeline
- Dashboard for safety team monitoring
Change Management
- Safety team training on AI-assisted analysis
- Parallel processing during validation period
- Clear escalation protocols for AI-detected signals
- Regular model performance reviews
Results & Impact
- Safety signals identified weeks earlier than manual process
- Regulatory deadlines consistently met
- Safety team capacity increased without additional staff
- More comprehensive coverage of incoming reports