H E A L T H I N S I G H T S
The potential impact is impressive . Application of LLM-RAG technology to transform AE case intake has been shown to deliver upwards of 65 % efficiency gains , with 90 %+ data extraction accuracy and quality in early pilots . In safety case narrative generation , the same technology is already demonstrating 80 – 85 % consistency in the summaries it creates . And that ’ s from a standing start , without prior exposure . Data retrieval – in context
The ability to retrieve data in context , rather than via a ‘ Control F ’ ( find all ) command ( e . g . everything among a content set that mentions headaches ), could transform a range of processes linked to safety / adverse event discovery and reporting .
RaviKanth Valigari , VP of Product Development at ArisGlobal
Going forward , equivalent solutions will help streamline the drafting of hefty regulatory safety reports , with advanced automation generating the preliminary narrative ; and perform narrative theme analysis in safety signal detection . The technology could have a significant impact in distilling trends not captured in the structured data ( e . g . a history of drug abuse , or of people living with obesity , across 500 patient narratives that are potentially of interest ). It is this broader potential that is now being discussed at meetings of life sciences ’ new global GenAI Council . Previ objections to smarter automation linked to concerns about reliability or compliance , which are now being addressed directly , are subsiding in the face of a growing urgency to embrace next-generation forms of technology which directly address those concerns and visibly boost process efficiency . As life science technology companies continue to explore and implement these innovations , the industry stands to benefit significantly from these advancements . �
Ramesh Ramani , VP of Technology at ArisGlobal
www . intelligenthealth . tech 19