AI-powered insights from the Veradigm Network reveal real-world GLP-1 outcomes hidden in EHR data

Veradigm, a leading provider of healthcare data and technology solutions, announced an advancement in the use of artificial intelligence to scale the generation of real-world evidence (RWE) for GLP-1 receptor agonists, including semaglutide and tirzepatide. By applying AI to electronic health record (EHR) deidentified data within the Veradigm Network, researchers can now surface rich, contextual insights—such as side effects, discontinuation reasons, and social determinants of health (SDoH)—that traditionally required intensive manual curation.

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“AI-powered curation allows us to unlock clinically meaningful insights from millions of patient records—insights that have traditionally been hidden in unstructured and semi-structured fields of EHR systems.” -Stuart Green, SVP & GM Veradigm Life Sciences

GLP-1 therapies are transforming the management of type 2 diabetes and obesity, yet significant gaps remain in understanding real-world usage, especially in identifying reasons for discontinuation or capturing side effects hidden in physician notes. Veradigm’s AI-driven approach enables scalable extraction of clinical signals from unstructured data, offering life science organizations deeper, real-time insight into patient experiences and outcomes.

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“AI-powered curation allows us to unlock clinically meaningful insights from millions of patient records—insights that have traditionally been hidden in unstructured and semi-structured fields of EHR systems,” said Stuart Green, SVP & General Manager, Veradigm Life Sciences. “This is especially critical for GLP-1 therapies, where understanding why patients discontinue, or which side effects matter most can significantly improve patient outcomes and therapeutic strategy.”

Key Capabilities and Insights from Veradigm’s AI-Driven Curation Include:

  • Discontinuation Drivers – Automatically extracts reasons for stopping therapy (e.g., side effects, cost, perceived inefficacy) from clinician notes
  • Side Effects Monitoring – Detects and stratifies severity of gastrointestinal and side effects, such as gallbladder issues or psychiatric symptoms, through contextual analysis of progress notes
  • Off-Brand Use Identification – Flags mentions of compounded or unapproved formulations (e.g., “semaglutide from weight-loss clinic”), supporting safety and market surveillance
  • Outcome Tracking – Tracks comorbidities (e.g., cardiovascular events) and treatment responses not typically captured in structured EHR fields
  • Social and Behavioral Context – Surfaces SDoH that influence adherence and health outcomes

This new offering leverages the national footprint of Veradigm Network EHR Data, capturing both structured and unstructured data across diverse patient populations and geographies. By pairing AI with clinical validation, Veradigm ensures data accuracy and applicability for life science research, regulatory engagement, and value-based decision-making.

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Source – businesswire