GLP-1 Spending Is Spiraling — But There's a Smarter Way Forward

The recent Modern Healthcare piece spotlighted what many in the industry already know: GLP-1 medications are breaking budgets and forcing tough decisions. In just a few short years, weight-loss prescriptions for drugs like tirzepatide and semaglutide (marketed under the names Zepbound and Wegovy) have skyrocketed — and so have payer costs.

Blue Cross Blue Shield of Massachusetts reported spending $300 million on GLP-1s in 2023. They now forecast nearly $1 billion in spend by 2026 if current trends continue. Other plans — including BCBS Michigan and Independence Blue Cross — are also scaling back coverage, citing overwhelming utilization, unclear outcomes, and unsustainable financial pressure.

But does it have to be all or nothing?

The Problem Isn’t the Medication — It’s the Model

GLP-1s can be transformative for the right patients. But without a precision strategy, payers, providers and patients are flying blind — covering expensive medications that have substantial side effects without knowing who is likely to benefit.

In fact, many members stop therapy before achieving significant weight loss, meaning payers absorb the cost without realizing improvements in health outcomes or decreasing cost of care. That’s not sustainable.

Introducing Predictive Response Modeling for GLP-1s

At Helix, we believe there’s a better path forward — one that protects both outcomes and budgets, and patients and payers.

Our GLP-1 Precision Effectiveness Model integrates a polygenic risk score (PRS) for BMI alongside clinical factors (like diabetes, hypertension, and BMI) to characterize the expected weight loss patterns over the course of 12 months of treatment.

Based on this model, patients with a high predicted likelihood of treatment response were approximately twice as likely to achieve ≥10% weight loss compared to those with a low predicted likelihood (67% vs. 36%, respectively).

With predictive models like these, health plans and providers may be able to:

• Identify members diagnosed with obesity who are most likely to respond to treatment
• Improve shared decision making around the best individual treatment for obesity
• Support informed resource allocation by providing additional information about potential treatment outcomes
• Provide information that providers may consider when discussing dosage considerations to help balance goals with possible side effects, and expected outcomes with members.

This is not a theoretical approach. It’s built on Helix’s robust genomic dataset and is being evaluated through ongoing research in real-world settings.

A Precision Approach to a Blockbuster Challenge

GLP-1s aren’t going away. In fact, demand will likely continue to climb. The real question is: how will payers respond?

Dropping coverage may feel like the only lever left — but it’s not the only answer. A precision-based model enables smarter, more sustainable, and personalized management of GLP-1s. If you’re ready to take a targeted approach that delivers better ROI, we’d love to talk.

Note: Our approach complies with all applicable laws regarding the use of genetic information and is designed to supplement, not replace, clinical decision-making. The predictive value of these models continues to be studied. Healthcare decisions should always be made in consultation with qualified healthcare providers.