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Challenges impacting safety case processing
Pharmaceutical safety professionals using GenAI-powered systems to analyze drug safety data can shift from week-long manual processing to streamlined, single-day case management.
Pharmacovigilance case processing relies heavily on manual work and coded systems. While reliable, these methods are time-intensive and labor-heavy. Quality control adds an extra layer to the process, slowing down completion even further. As it stands, these systems can still achieve an accuracy of around 90%. However, there is room for improvement in reducing errors and ensuring compliance, despite the current solid outcome.
Integrating GenAI into pharmacovigilance
The role of GenAI in pharmacovigilance extends beyond simple automation. By embedding GenAI into established workflows, safety case processing can shift from a manual-intensive, week-long process to a streamlined, high-precision system. Adverse event (AE) detection is a primary focus area. GenAI tools, capable of parsing vast volumes of unstructured data, can efficiently analyze text and voice data from call centers and literature to pinpoint potential AEs. This technology significantly reduces the need for multiple tedious data reviews, allowing safety professionals to concentrate on high-value tasks and minimize the risk of missed events.
With the right approach, pharmacovigilance researchers reviewing safety case data using integrated GenAI tools can expect faster adverse event detection and more accurate case processing than traditional methods while maintaining human verification as a final quality check.
GenAI is also essential for managing unstructured data from diverse sources. Safety cases often involve multilingual documents requiring translation and redaction to meet privacy regulations. GenAI accelerates this process, translating, anonymizing and structuring data for easier analysis. This efficiency reduces reliance on traditional coding, enabling faster and more precise case processing. Current data extraction rates sit around 70%, but GenAI is projected to raise this to 90-95%, improving both speed and accuracy in structuring case data.
Driving key benefits and efficiency gains
Quantifying the potential impact of GenAI in drug safety
- Case processing time reduction: Reduces processing timeline from approximately 7 days to 1 day, representing an 85% decrease in processing duration.
- Cost efficiency: One company has set out to reduce case processing costs by 50% by 2027.
- Data extraction accuracy: Improves extraction rates from current 70% to projected 90-95%, representing a 20-25% improvement in data structuring efficiency.
- Case completion accuracy: Current systems achieve 90% accuracy, with GenAI implementations targeting potential accuracy rates above 99% through comprehensive validation processes and final human verification.
The impact of GenAI on pharmacovigilance encompasses more than just cost savings, although these are substantial. By reducing case processing timelines from a week to a single day, GenAI could reduce case management costs by as much as 50% in the coming years. Beyond cost reduction, GenAI drives quality improvements, especially in terms of accuracy for extracted case attributes. Automated features like missing data detection and follow-up field suggestions contribute to more complete, accurate cases, supporting an accuracy goal above 99%.
GenAI addresses key regulatory needs by generating what are known as i-Narratives—case summaries that meet medical standards and read naturally, providing concise and informative summaries of cases. With advanced natural language generation capabilities, GenAI produces clear, coherent summaries, moving beyond the rigid, coded language of older systems. This capability enhances readability and aligns with today’s regulatory standards for clarity and accessibility in drug safety records.
Transforming drug safety monitoring: a real-world example
A global pharmaceutical company recently set out to transform its approach to drug safety monitoring, aiming to streamline processes while cutting costs and maintaining high quality. Traditional methods, though effective, were slow, labor-intensive and costly. To meet its ambitious goals of reducing case processing costs by 50% by 2027 and achieving a quality standard above 99%, the company adopted a phased GenAI-powered solution.
In Phase 1, the focus is on automating core elements like data extraction, AE detection and multilingual data translation. GenAI’s capabilities in these areas significantly reduced manual labor and accelerated workflows, while also adding targeted follow-up suggestions to ensure comprehensive data collection. Phase 2 will expand GenAI’s role, enabling it to generate regulatory-compliant case summaries (i-Narratives) with high clarity, automated outbound query and a holistic view of the case for medical review, reducing the administrative burden on healthcare professionals.
The goal is to drop the company’s case processing time from one week to a single day, cutting costs by 50%, improving data extraction accuracy to 95% and pushing case completion to 99%.
Advanced GenAI systems can process unstructured pharmacovigilance data, improving extraction rates significantly while maintaining strict data security and regulatory compliance in a closed-system environment.
Ensuring robust governance for GenAI integration
GenAI offers transformative potential but integrating it into pharmacovigilance requires a strong governance framework to ensure reliability, data security and its compliance with evolving regulations. Many organizations are taking a closed-system approach, creating secure environments that combine GenAI’s capabilities with proprietary data mappings to protect patient information, maintain data integrity and meet rigorous privacy laws like the General Data Protection Regulation (GDPR) and other international standards.
To safeguard sensitive data, GenAI operations are isolated within controlled ecosystems under strict governance, preventing unauthorized access and minimizing personal data exposure. This approach not only fulfills legal requirements but also strengthens trust by demonstrating a firm commitment to privacy and patient confidentiality alongside the efficiency gains of GenAI.
These secure environments support effective “prompt engineering,” a technique essential for minimizing risks like “data hallucination,” addressing language limitations and refining GenAI processing to reduce errors. Strict privacy protocols, combined with continuous testing and feedback loops, allow GenAI models to become more accurate and stable over time. Human verification remains a final check for critical tasks, ensuring each case is thoroughly reviewed before submission.
Innovating for enhanced patient protection
Uwe Trinks, Ph.D.
The integration of GenAI into safety case processing marks a significant step forward in pharmacovigilance, with the potential to accelerate drug safety monitoring, reduce costs and improve quality. GenAI is the innovation the industry needs by reducing timelines, lowering expenses and enhancing case accuracy. GenAI-driven initiatives are setting a new standard by showing how advanced AI technology can improve patient safety and streamline regulatory compliance in the pharmaceutical sector as they expand.
Uwe Trinks is global practice lead, pharmacovigilance technologies at IQVIA
Filed Under: Data science
