Challenges in Pharmacovigilance Literature Monitoring
With the expanding volume of scientific literature, automating literature monitoring is essential for both large and small companies. Despite the clear benefits of literature in detecting new safety events, the task remains labor-intensive with low yield.
For instance, the European Medicines Agency (EMA) reported that in 2019, only 1.8% of unique cases were identified from over 355K screened articles. By 2022, this dropped to 0.85% unique cases with doubled screening volumes.
How Automation Can Help Medical Literature Monitoring
To address this, let's explore each stage of the medical literature monitoring workflow:
Database Search and Deduplication of Results
Ensuring quality results requires searches across multiple scientific databases. EMA's GVP Module VI guidelines recommend selecting appropriate databases for each product. A single, consistent query can streamline searches across multiple databases, saving time and improving results.
Duplicate detection is crucial for maintaining data integrity. Advanced algorithms, such as text similarity measures, identify and eliminate redundant information, ensuring accurate adverse event reporting.
Ranking and Filtering Results for Literature Screening
Once results are retrieved, they can be ranked or filtered based on workflow requirements (e.g., ICSR, aggregate reporting). AI automation in Crypta AI replicates pharmacovigilance workflows, using predictions to rank and filter results, reducing the volume of articles to screen and enhancing quality control processes.
Safety Screening Assessments
Automation improves the quality and speed of safety assessments. Natural language processing extracts key components, while machine learning highlights important passages, aiding focused assessments and pre-populating case details or aggregate reports.
Automation and AI in Medical Literature Systems: How to Choose?
Implementing AI systems requires diverse skills and extensive domain knowledge. Partnering with a vendor for medical literature automation offers faster ROI and simpler implementation. Companies should consider:
Regulatory Guidance for AI in Pharmacovigilance: Continuous monitoring of regulatory guidance ensures compliance and audit readiness.
Robust AI Transparency and Governance: Regular performance monitoring and quality checks are essential, as reflected in regulatory guidance.
Compliance and Validation: Platforms must comply with industry standards like CFR-11 and GVP, emphasizing validation of automated features.
Human Oversight: Ensuring visibility and traceability of automated decisions allows for necessary oversight and validation.
Intelligent Literature Monitoring with Crypta AI
Crypta AI is a comprehensive literature monitoring solution for pharmacovigilance teams. Its flexible workflow, extensive scientific database, and unique AI features provide fast, cost-effective, and fully traceable results for all screening needs.
Interested to know more about Crypta AI? Book for a demo!
Comments