How LLMs can advance safety case intake—points to consider and insights from a proof of concept

Abstract

As per Good Pharmacovigilance Practices, pharmaceutical companies must act on potential adverse reactions to drugs. With significant increases in the number of case reports in recent years, they face pressure to raise the efficiency of their processes while maintaining data integrity and patient safety. The use of Large Language Models (LLMs) in safety case intake provides potential to advance processes without compromising quality. In this perspective review, we highlight the potential benefits of LLMs in case intake workflows, and points to consider relating to the current research landscape, inspired by our proof-of-concept (PoC) study. Benefits include raising the consistency of data extraction, reducing bias, and enhancing efficiency. We reflect on challenges in realizing the potential of this new technology from a practical industry perspective, namely (a) measuring the Return on Investment, (b) early involvement of subject matter experts, (c) handling unclear regulatory expectations, (d) system integration, and (e) organizational readiness. We illustrate the potential and its challenges through the lens of our PoC’s insights as well as through insights from published literature, which allowed us to estimate an efficiency gain from a business process perspective for data extraction and initial case report, demonstrating the technology’s potential and practical applicability in real-world scenarios.