Veradigm Enhances GLP-1 RWE with AI-Powered EHR Curation

Veradigm, a leading healthcare data and technology provider, has announced a major advancement in using artificial intelligence (AI) to scale real-world evidence (RWE) generation for GLP-1 receptor agonists (GLP-1 RAs), including semaglutide and tirzepatide. By leveraging AI to analyze deidentified electronic health record (EHR) data within the Veradigm Network, researchers can now extract critical insights—such as reasons for discontinuation, side effects, and social determinants of health (SDoH)—previously accessible only through labor-intensive manual review.

GLP-1 therapies are reshaping the treatment of type 2 diabetes and obesity, yet real-world usage data remains limited, especially in understanding discontinuation patterns and identifying adverse events buried in physician notes. Veradigm’s AI-driven approach enables scalable analysis of unstructured clinical data, giving life sciences organizations real-time visibility into patient experiences and treatment outcomes.

“AI-powered curation allows us to uncover clinically valuable insights hidden in EHR systems, especially for GLP-1 therapies, where understanding patient challenges is key to improving outcomes,” said Stuart Green, SVP & GM, Veradigm Life Sciences.

Key AI-Driven Capabilities Include:

• Discontinuation Analysis – Identifies reasons for stopping therapy, such as side effects, cost, or lack of efficacy

• Side Effect Detection – Highlights and classifies side effects like GI issues, gallbladder problems, or psychiatric symptoms

• Off-Label Use Monitoring – Flags mentions of compounded or unapproved GLP-1 formulations

• Outcome Tracking – Captures comorbidities and treatment effects not found in structured fields

• SDoH Insights – Extracts social and behavioral factors that impact adherence and health outcomes

This solution taps into the nationwide Veradigm Network, combining AI with clinical validation to ensure data accuracy for life sciences research, regulatory submissions, and value-based care strategies.