Building a scalable GLP-1 care model

The rapid rise of GLP‑1 therapies has revealed a fundamental challenge across healthcare. For many patients, the pathway to their first prescription is not a single, unified entry point. It’s a maze of primary care visits, telehealth platforms, retail providers, and employer programs. The result is a fragmented experience that burdens clinicians, slows down access, and limits consistency in care. 

In a recent Q&A with pharmaphorum, EVERSANA leaders Aaron Uydess and Faruk Capan explored these challenges and shared what the industry must build to support safe, scalable, and patient‑centered GLP‑1 care. 

What barriers still stand in the way of broad GLP‑1 access? 

Cost remains one of the most visible barriers, even as pricing trends shift. But access challenges extend far beyond affordability. Many patients have limited access to clinicians trained in obesity medicine and the holistic management required for long‑term success.  

When patients bounce across multiple care settings without coordination, the pathway becomes confusing and inconsistent. 

How can unified data and AI redesign the front door to GLP‑1 care? 

According to Capan, the most significant missing asset in GLP‑1 care is a connected patient journey. Right now: 

• Diagnoses sit in one system 

• Prescriptions sit in another 

• Affordability checks are captured elsewhere 

• Outcomes data is dispersed across claims and clinical records 

When these signals come together, teams can identify where patients struggle, predict drop‑off points earlier, and personalize support at scale. 

AI plays a pivotal role here. It can streamline benefit checks, capture medical history, identify comorbidities, and recommend the right next step. It removes administrative friction so clinicians can focus on coaching and caring for the whole patient. 

What infrastructure is required for safe, scalable, AI‑enabled GLP‑1 care? 

Uydess and Capan highlight three foundational elements: 

• Trusted data connections — Privacy‑safe, interoperable ways to unify clinical, claims and socioeconomic signals 

• Strong AI governance — Oversight of model behavior, bias, auditing and population‑level performance 

• Workflow‑ready systems — Insights that feed directly into patient support tools, provider workflows and payer processes 

When these elements align, GLP‑1 care can become far more predictable, equitable, and sustainable, especially for underserved populations who face the steepest access challenges. 

Explore how EVERSANA supports patient access, affordability and AI‑enabled care pathways. 

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