Veradigm Deploys AI to Extract Real-World Insights on GLP-1 Medications from EHR Data
核心洞察
Veradigm (搜索) has launched an AI initiative to analyze unstructured electronic health record data, uncovering previously hidden insights about GLP-1 (搜索) receptor agonist use patterns, side effects, and discontinuation reasons.
The technology extracts clinically significant information from physician notes across Veradigm (搜索)'s nationwide network, providing critical real-world evidence on blockbuster drugs like semaglutide (Ozempic/Wegovy) and tirzepatide (Mounjaro/Zepbound).
This AI-powered approach aims to address knowledge gaps in GLP-1 (搜索) therapy outcomes, including identifying social determinants of health affecting treatment adherence, monitoring cardiovascular outcomes, and detecting off-label usage patterns.
Veradigm (搜索) has announced a significant advancement in real-world evidence generation through a new artificial intelligence initiative focused on GLP-1 (搜索) receptor agonists, including widely prescribed medications semaglutide and tirzepatide. The technology applies AI to deidentified electronic health record (EHR) data to extract valuable clinical insights typically hidden in unstructured physician notes.
The AI system is designed to uncover patterns in patient care that traditional data analysis methods miss, including specific side effects, reasons for discontinuation, and social factors affecting medication adherence. This approach could transform understanding of how these popular diabetes and weight loss medications perform outside of controlled clinical trials.
"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, Senior Vice President and General Manager of Veradigm (搜索) Life Sciences, in a statement.
Addressing Critical Knowledge Gaps in GLP-1 Therapy
GLP-1 (搜索) receptor agonists have rapidly transformed treatment approaches for type 2 diabetes (搜索) and obesity (搜索). Medications like semaglutide (marketed as Ozempic and Wegovy) and tirzepatide (marketed as Mounjaro and Zepbound) have demonstrated remarkable efficacy in clinical trials, with high-dose formulations achieving average weight reductions of 15% or more.
Despite their clinical success, understanding real-world usage patterns remains challenging. Veradigm (搜索)'s AI technology specifically targets this knowledge gap by extracting signals from free-text fields in electronic health records. The system can identify:
- Specific reasons patients discontinue therapy
- Detailed side effect profiles, including gastrointestinal and psychiatric symptoms
- Instances of off-label or compounded drug use
- Cardiovascular outcomes in real-world populations
- Social determinants of health affecting treatment success
Green emphasized that these insights are "especially critical for GLP-1 (搜索) therapies, where understanding why patients discontinue, or which side effects matter most can significantly improve patient outcomes and therapeutic strategy."
Technical Approach and Validation
The technology combines machine learning algorithms with clinical validation to ensure data reliability. By analyzing both structured and unstructured data across Veradigm (搜索)'s nationwide network, the system captures insights from diverse patient populations and practice settings.
Key capabilities of Veradigm (搜索)'s AI-driven curation include:
- Automatic extraction of discontinuation reasons from clinical notes
- Detection and severity stratification of side effects through contextual analysis
- Identification of off-brand or compounded formulation use
- Tracking of comorbidities and treatment responses
- Surfacing social and behavioral factors influencing adherence
The company states that the technology maintains accuracy through a combination of advanced AI and clinical oversight, making the resulting data suitable for life science research, regulatory engagement, and value-based decision-making.
Addressing Real-World Challenges with GLP-1 Medications
While GLP-1 (搜索) therapies have shown impressive clinical results, they face significant real-world challenges. These medications typically cost over $1,000 per month without insurance coverage, and ongoing supply shortages have limited availability for many patients.
Side effects—particularly gastrointestinal issues like nausea, vomiting, and constipation—lead some patients to discontinue treatment. Long-term safety profiles, especially in off-label uses and non-diabetic populations, continue to be studied.
By extracting real-world data at scale, Veradigm (搜索)'s AI initiative could provide valuable insights into these challenges, potentially informing clinical practice, payer policies, and pharmaceutical development strategies.
Future Applications and Availability
Veradigm (搜索) has announced that the technology is currently available to life sciences organizations, regulatory bodies, and other stakeholders seeking enhanced decision-making data in the evolving GLP-1 (搜索) landscape.
The company plans to present research findings based on this data at ISPOR 2025, focusing on GLP-1 (搜索) persistence and real-world reasons for therapy discontinuation. This presentation will take place during Poster Session 2 on Wednesday, May 14, from 4:00–7:00 PM EDT.
As demand for GLP-1 (搜索) therapies continues to surge and physicians increasingly prescribe them for patients with obesity (搜索) even without diabetes, tools that provide deeper insights into real-world outcomes could play a crucial role in optimizing their use and addressing ongoing challenges in patient care.