The GLP-1 Insights Series, Part 2: Maximizing impact and savings through analytics

GLP-1s present a major opportunity for organizations to improve clinical outcomes and reduce long-term healthcare costs. However, payers and providers must balance this potential with challenges such as high upfront spend, adherence issues, and determining which patients will benefit most. Read a more in-depth overview of these considerations in our previous blog post.

Managing these factors requires a deliberate, data-driven strategy. Proactive payers and providers must treat GLP-1 management as a strategic imperative, not just a cost issue. By leveraging comprehensive data, organizations can identify the most effective ways to apply GLP-1 therapies, including how to support patient selection and monitor adherence to achieve optimal results.

In this second blog post in our three-part series, I’ll discuss how analytics can help ensure GLP-1s deliver clinical value and meaningful long-term financial impact.

Operational strategies for optimizing GLP-1 use

To maximize the clinical and financial impact of GLP-1s, organizations should incorporate the following elements into a thoughtful, data-driven strategy:

  • Risk-stratify populations: Identify patients with multiple chronic conditions, including individuals with type 2 diabetes, obesity, and cardiovascular risk factors who could benefit the most from GLP-1s, and prioritize them for therapy to achieve the greatest impact
  • Optimize benefit design: Use formulary management, evidence-based prior authorization and step therapy, and usage monitoring to balance access, clinical appropriateness, and cost control
  • Track outcomes and persistence: Monitor adherence and compare outcomes, including weight loss, biometric improvements, and downstream events such as hospitalizations or complications, with alternative weight management options
  • Integrate behavioral support: Pair GLP-1s and bariatric surgery with behavioral and lifestyle programs to reinforce healthy habits and sustain therapeutic gains
  • Align provider incentives: Include incentives in value-based models related to long-term weight management outcomes
  • Evaluate cost versus savings: Quantify the return on investment of potential reductions in complications, comorbidities, and hospitalizations over time, relative to higher drug spend
  • Manage budget impact: Implement phased adoption and targeted population criteria to manage budget pressures associated with high prices and large eligible populations

From reactive GLP-1 cost management to proactive, strategic decisions

Utilization and cost analytics

An effective GLP-1 strategy starts with understanding basic use patterns, such as profiling who is receiving the medications, why the medications are being prescribed, and how medication use varies across populations.

Once a data-driven strategy is established, organizations, especially payers, can assess how rising GLP-1 use is affecting their total spend. They can then leverage predictive models to estimate how changing prescribing trends may influence financial outcomes. For example, machine learning models can predict the costs and outcomes using GLP-1s versus bariatric surgery.

Integrating pharmacy benefit manager data enhances the analysis by quantifying rebates, net costs, and per-member-per-month contributions to pharmacy and medical expenses.

Clinical outcomes and value assessment

Although GLP-1s require high upfront costs, their potential to drive long-term savings is significant. For example, a study published in JAMA showed reductions in cardiovascular risk that vary by body mass index (BMI), along with consistent kidney protection in patients with type 2 diabetes. Analytics can confirm whether these outcomes are reflected in a specific population.

By combining claims, lab results, and clinical data, organizations can compare factors such as A1C levels, BMI changes, hospitalization rates, and complication trends between GLP-1 users and non-users. Further segmentation, such as age, comorbidities, or adherence, can pinpoint where therapies deliver the highest value. These insights can estimate downstream savings from reduced emergency room visits, surgeries, readmissions, or long-term cardiovascular and renal events.

Provider and patient engagement analytics

GLP-1s are most effective when used consistently and supported by appropriate care. Analytics can reveal behavior patterns that influence outcomes, such as:

  • Prescriber profiling: Identifies high-volume or outlier providers and highlights prescribing patterns that deviate from guidelines
  • Adherence dashboards: Shows refill gaps and early discontinuation trends, prompting timely intervention
  • Predictive tools: Flags patients at risk of discontinuation or inappropriate use, enabling proactive outreach by care managers
  • Digital engagement analytics: Tracks responses to education campaigns and incentives designed to improve persistence

Advanced predictive algorithms can also estimate which patients are likely to initiate GLP-1 therapy, helping payers prepare for utilization surges and develop more accurate multi-year budget forecasts.

 

Why the analysis of GLP-1 use matters

GLP-1 analysis isn’t just about tracking a popular drug. It’s about managing one of the fastest-growing cost drivers in the healthcare industry while maximizing clinical value. With comprehensive analytics, payers and providers gain a clear understanding of how GLP-1 therapies influence cost, outcomes, and behavior, enabling smarter and more proactive decisions. Together, these insights directly influence care quality, financial performance, and population health outcomes, supporting long-term sustainability.

In the final blog in this series, I’ll transition from strategy to application, showing how payers and providers use analytics to proactively manage GLP-1 use. I’ll explore how data-driven insight supports better outcomes, reduces unnecessary spend, strengthens value-based care, and helps organizations manage the financial risk of rapidly growing GLP-1 adoption.

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Rob Corrigan

Rob Corrigan, Director of Data Science Solution Engineering at MedeAnalytics, has over 40 years of experience partnering with senior executives across commercial and government payers, provider organizations, and national employers. He serves as a strategic advisor, working closely with clients to transform their data into actionable insights through predictive analytics and rigorous evaluation of quality and financial outcomes. Rob also designs advanced analytical solutions and prescriptive models that support cost trend management, population health strategies, payer–provider collaboration, and benefit plan optimization.

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