What You Should Know:
– Veradigm, a provider of healthcare data and technology solutions, today announced a significant advancement in the application of artificial intelligence (AI) to scale the generation of real-world evidence (RWE) for the increasingly prevalent GLP-1 receptor agonists (GLP-1 RAs), including widely used medications like semaglutide and tirzepatide.
By deploying AI algorithms on deidentified electronic health record (EHR) data within the extensive Veradigm Network, researchers can now efficiently surface rich, contextual insights. This includes crucial information such as side effects, reasons for medication discontinuation, and social determinants of health (SDoH), details that traditionally required labor-intensive manual data curation.
Addressing Critical Gaps in GLP-1 Real-World Understanding
GLP-1 therapies are revolutionizing the management of type 2 diabetes and obesity. However, considerable gaps persist in understanding their real-world usage patterns and effects. Identifying specific reasons why patients discontinue these therapies or capturing nuanced side effects often hidden within unstructured physician notes has remained a significant challenge. Veradigm’s AI-driven approach directly addresses these hurdles by enabling the scalable extraction of vital clinical signals from this unstructured data, offering life science organizations deeper, near real-time insights into actual patient experiences and outcomes.
AI Unlocks Key Clinical Insights from EHR Data
Veradigm’s innovative AI-driven curation process offers several key capabilities to enhance the understanding of GLP-1 RA therapies:
- Uncovering Discontinuation Drivers: The AI can automatically extract and categorize reasons why patients stop their GLP-1 therapy. This includes identifying factors such as specific side effects, cost concerns, or a perceived lack of efficacy, directly from clinician notes.
- Enhanced Side Effects Monitoring: The technology detects and helps stratify the severity of known gastrointestinal side effects, as well as other potential issues like gallbladder problems or psychiatric symptoms, through contextual analysis of patient progress notes.
- Identifying Off-Brand Use: The system is designed to flag mentions of compounded or unapproved formulations of GLP-1s (e.g., “semaglutide from weight-loss clinic”), which is crucial for supporting safety monitoring and market surveillance.
- Comprehensive Outcome Tracking: Veradigm’s AI can track associated health conditions (comorbidities), such as cardiovascular events, and detailed treatment responses that are not typically captured in the structured fields of an EHR.
- Revealing Social and Behavioral Context: The AI is capable of surfacing SDoH factors that can significantly influence a patient’s adherence to treatment and their overall health outcomes.
“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.”
