Veradigm taps AI to expand real-world insights on GLP-1 drug use

AI to examine GLP-1 usage data: ©Lalaka - stock.adobe.com

Veradigm announced a new artificial intelligence initiative designed to dramatically scale real-world evidence generation for GLP-1 receptor agonists, including blockbuster drugs semaglutide and tirzepatide.

By applying AI to deidentified electronic health record data in its nationwide network, Veradigm aims to uncover clinically significant patterns in patient care—such as side effects, reasons for drug discontinuation, and social factors that affect adherence—that have typically been buried in unstructured physician notes.

“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 Life Sciences, in a statement.

GLP-1 therapies have reshaped treatment for type 2 diabetes and obesity, but understanding how patients use these drugs outside of clinical trials remains a challenge. Veradigm’s new approach uses AI to extract signals from physician notes and other free-text fields, which can reveal drivers of discontinuation, track side effects such as gastrointestinal or psychiatric symptoms, and identify off-label or compounded use.

Other capabilities include monitoring cardiovascular outcomes and surfacing social determinants of health—such as transportation access or food insecurity—that may impact treatment success.

The system combines machine learning with clinical validation to ensure data reliability, the company said, and spans a diverse patient population through the Veradigm Network.

Green noted that insights gleaned from this AI-powered platform are 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.”

Veradigm said the technology is available now to life sciences organizations, regulatory bodies, and others seeking to enhance decision-making in the evolving GLP-1 landscape.

GLP-1 drugs reshape diabetes and obesity care amid ongoing access challenges

GLP-1 receptor agonists, a class of medications originally developed to manage type 2 diabetes, have rapidly gained attention for their effectiveness in promoting weight loss and improving metabolic health. These drugs, including semaglutide (marketed as Ozempic and Wegovy) and tirzepatide (marketed as Mounjaro and Zepbound), mimic a naturally occurring hormone called glucagon-like peptide-1. This hormone stimulates insulin secretion, reduces appetite, slows gastric emptying, and helps regulate blood sugar levels.

In clinical trials and real-world use, GLP-1 drugs have shown striking effectiveness. For type 2 diabetes, they significantly lower blood glucose levels and reduce the risk of cardiovascular events. For weight loss, high-dose formulations like Wegovy and Zepbound have demonstrated average reductions of 15% or more in body weight, often outperforming older anti-obesity medications. As a result, these therapies have quickly become a cornerstone of modern treatment for patients with obesity, metabolic syndrome, or related complications.

However, access and tolerability remain challenges. GLP-1 drugs are expensive, often costing over $1,000 per month without insurance, and supply shortages have limited availability. Side effects—especially gastrointestinal issues like nausea, vomiting, and constipation—lead some patients to discontinue treatment. Long-term safety, particularly in off-label uses and among non-diabetic populations, is still being studied.

Despite these concerns, the demand for GLP-1 therapies continues to surge. Physicians increasingly prescribe them for patients with obesity even in the absence of diabetes, and some health systems are evaluating their use as part of broader chronic disease management strategies. As more real-world evidence emerges—especially regarding long-term outcomes and reasons for discontinuation—healthcare providers, researchers, and payers are seeking better data to guide use and policy decisions. AI tools that unlock insights from electronic health records could play a critical role in filling that knowledge gap.