How to Build a GLP-1 ROI Model Your CFO Won't Tear Apart

Why ROI models can look very different

As employers continue evaluating GLP-1 benefits, ROI projections have become a common part of the conversation. Vendors, consultants, and health plans often present financial models to help estimate the potential impact of a program, yet organizations evaluating similar employee populations can arrive at very different conclusions.

The reason is usually not the math; it's the assumptions behind the model. Variables such as medication persistence, program costs, healthcare utilization, productivity assumptions, and the timeframe used for analysis can all influence projected outcomes. Even small changes to those assumptions can produce meaningfully different financial forecasts.

For employers, the goal isn't simply to identify the model with the highest projected ROI. It's to understand how that projection was developed, whether the assumptions are supported by available evidence, and whether they reasonably reflect the organization's workforce and benefit strategy. As rising pharmacy costs continue to influence employer decision-making, many organizations are placing greater emphasis on transparent methodologies and measurable outcomes rather than projected ROI alone.

Understanding those assumptions is the first step toward building an ROI model that can withstand careful review by finance, benefits, and executive leadership teams.

Start with assumptions you can explain

Every ROI model relies on a relatively small number of inputs. Small adjustments to those assumptions can produce very different financial projections.

Some of the most important include:

●      Medication persistence

●      Net program and medication costs

●      Healthcare utilization

●      Weight regain following treatment discontinuation

●      Productivity and absenteeism assumptions

●      The time horizon used for analysis

Whenever possible, these assumptions should be supported by published evidence, organization-specific data, or clearly documented methodologies. Transparent assumptions make it easier for finance, benefits, and executive leadership teams to understand how projected outcomes were calculated.

Why persistence deserves close attention

Persistence is one of the most influential variables in a GLP-1 ROI model because many potential health and financial outcomes depend on participants remaining engaged in treatment over time.

Real-world evidence suggests that discontinuation during the first year is relatively common. A large observational analysis published in JAMA Network Open of U.S. adults who initiated semaglutide or liraglutide found that an estimated 53.6% discontinued treatment within one year. Discontinuation was higher among individuals without type 2 diabetes. These findings do not predict what will happen within every employer population. Workforce demographics, benefit design, clinical eligibility, and program engagement can all influence persistence. However, they illustrate why assumptions based solely on clinical trial participation may not fully reflect real-world experience.

For employers evaluating ROI projections, understanding the persistence assumption may be just as important as understanding the projected return itself.

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Separate investment from potential return

One of the most common challenges in ROI modeling is treating program costs and projected savings as though they apply to the same population throughout the analysis.

In practice, they often do not. Program costs generally begin when a participant starts treatment. Potential savings, however, may depend on participants remaining engaged long enough to achieve and maintain clinically meaningful outcomes.

Consider a hypothetical employer with 1,000 covered employees where 60 individuals begin GLP-1 therapy. Medication and program costs begin when treatment starts for all 60 participants. If a portion of those participants discontinue treatment during the first year, the financial outcomes associated with those participants may differ from participants who continue treatment and maintain clinical improvement.

Modeling these populations separately may provide a more balanced estimate of potential financial impact than applying identical assumptions across every participant.

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A practical framework for evaluating ROI

Rather than focusing on a single ROI figure, employers may find it helpful to evaluate the two sides of the equation independently.

Program investment

●      Medication costs

●      Clinical services

●      Laboratory testing

●      Program administration

●      Participant support

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Potential financial impact

●      Changes in healthcare utilization

●      Changes in pharmacy spending over time

●      Potential reductions in absenteeism

●      Potential improvements in workplace productivity

The relationship between these two categories depends on several factors, including persistence, benefit design, participant engagement, and the characteristics of the covered population.

Use realistic cost assumptions

Medication pricing can vary significantly depending on negotiated contracts, pharmacy benefit arrangements, rebates, and purchasing models. For that reason, employers may benefit from modeling expected net costs whenever possible rather than relying exclusively on publicly available list prices.Using organization-specific cost assumptions can improve the accuracy of financial planning and make comparisons between competing programs more meaningful.

Consider long-term outcomes

Long-term financial planning should also consider what may happen after treatment is discontinued. The STEP 1 Trial Extension, published in Diabetes, Obesity and Metabolism, observed that participants regained approximately two-thirds of the weight they had lost during treatment within one year after discontinuing semaglutide, along with changes in several cardiometabolic risk factors.

Individual outcomes vary, and not every patient will experience the same results. Nevertheless, these findings suggest that employers may wish to evaluate multiple scenarios rather than assuming clinical or financial benefits continue indefinitely after treatment ends.Scenario-based planning often produces a more balanced financial model than relying on a single set of assumptions.

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Evaluate vendor ROI projections carefully

Many organizations receive ROI estimates as part of the vendor evaluation process. These projections can provide a useful starting point, but they should also be accompanied by a clear explanation of the methodology used to produce them.

When reviewing an ROI model, employers may consider asking:

●      What population was included?

●      What timeframe was evaluated?

●      Were outcomes measured across everyone who enrolled or only participants who remained active?

●      Which healthcare costs were included?

●      Which assumptions were based on published evidence?

●      Can projected outcomes be measured after implementation?

Understanding the methodology behind an ROI projection often provides more insight than comparing projected ROI multiples alone.

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Treat ROI as a planning tool

No financial model can predict exactly how a specific workforce will respond to a GLP-1 benefit. Employee demographics, benefit design, eligibility criteria, participation rates, and clinical engagement all influence outcomes.

For that reason, ROI models are generally most valuable as planning tools rather than guarantees of future financial performance. As employers collect additional program data, assumptions can be refined to better reflect observed experience.

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Build models using measurable information

Financial projections become more useful when the underlying assumptions can be evaluated over time. Programs that incorporate longitudinal outcomes measurement may help employers compare projected assumptions with observed program performance. Depending on the program structure, measurable information may include persistence, laboratory testing, medication utilization, and other clinical or operational metrics.

The eMed Population Health GLP-1 Program for Employers combines access to independently licensed healthcare providers, at-home diagnostic testing, ongoing participant support, and a connected digital experience designed to help employers monitor program performance over time. Individual health outcomes and financial results vary and depend on multiple factors, including workforce characteristics, program participation, and benefit design.

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Questions employers may want to ask

Before relying on any GLP-1 ROI projection, employers may wish to ask:

●      Are the assumptions clearly documented?

●      Are persistence estimates supported by published evidence?

●      Does the model use estimated net costs rather than list prices?

●      Are projected savings tied to sustained participation rather than enrollment alone?

●      Can outcomes be measured after implementation?

●      Does the model evaluate multiple scenarios instead of presenting a single forecast?

These questions can help employers better understand both the opportunities and the limitations of any financial projection.

The bottom line

A useful GLP-1 ROI model is not necessarily the one that projects the largest financial return. It's the one built on transparent assumptions, supported by appropriate evidence, and designed to evolve as additional information becomes available. For employers evaluating GLP-1 benefits, understanding how an ROI model was constructed may be just as important as the projected ROI itself.

_{Disclaimer: This blog is maintained by eMed for informational purposes only. Content published here does not constitute medical, legal, financial, or benefits advice and should not be relied upon as such. Third-party statistics, studies, and research cited are sourced from publicly available data and provided for general informational context only; eMed makes no representation as to their accuracy, completeness, or applicability to any specific employer population, and results may vary. eMed's Population Health GLP-1 Program for Employers pairs FDA-approved, on-label medications with clinical oversight; individual health outcomes depend on a variety of clinical and personal factors and cannot be guaranteed.}

_{Any content authored or posted by eMed employees reflects their personal opinions and perspectives only and does not represent the views, positions, or official statements of eMed or its affiliates.}