# How should employers evaluate the true ROI of digital health benefits?

Lily Armstrong · September 14, 2026

> The Reality of Digital Health ROI for Employers Employers face a challenging environment when assessing digital health solutions. For years, vendors...

## The Reality of Digital Health ROI for Employers

Employers face a challenging environment when assessing digital health solutions. For years, vendors promised massive savings based on engagement metrics like app downloads or monthly active users. However, recent evaluations, such as those published by the Peterson Health Technology Institute (PHTI) regarding digital diabetes tools and continuous glucose monitors (CGMs), show that many of these tools do not deliver the economic returns they claim. While clinical benefits exist for specific high-risk cohorts, the broad application of these tools across an entire employee population rarely justifies the per-member-per-month (PMPM) fees. Employers must look past vendor-supplied case studies and demand independent, peer-reviewed evidence before committing capital.

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The market has matured to a point where simple self-reported health improvements are no longer sufficient. In 2026, benefit leaders require hard proof of medical cost reduction, particularly in high-spend areas like musculoskeletal (MSK) care, cardiometabolic diseases, and digestive health. For instance, studies published in the American Journal of Managed Care (AJMC) regarding digital digestive care management demonstrate that structured digital interventions can reduce emergency department visits and imaging costs, but only when tightly integrated with traditional clinical pathways. Without this integration, digital health tools become expensive administrative add-ons that increase, rather than decrease, total cost of care.

To make informed decisions, benefit managers must adopt a skeptical, data-driven approach. They must recognize that a vendor's reported return on investment (ROI) often relies on flawed methodologies, such as comparing active users to non-users, which introduces severe selection bias. True evaluation requires analyzing claims data over a multi-year period, adjusting for baseline risk, and accounting for the natural regression to the mean that occurs in chronic disease populations. Only by establishing rigorous internal benchmarks can employers determine whether a digital health intervention genuinely lowers medical spend or simply shifts costs to other parts of the benefit plan.

## The Shift from Soft Savings to Hard Claims Data

For a decade, vendors successfully sold digital health platforms using the concept of Value on Investment (VOI). This framework allowed companies to claim financial benefits based on subjective metrics like employee satisfaction, reduced absenteeism, and improved productivity. While these factors are important to human resource departments, they are notoriously difficult to quantify on a balance sheet. In the current economic environment of 2026, chief financial officers are rejecting these soft metrics in favor of hard claims data. They demand to see direct reductions in medical claims, pharmacy spend, and inpatient admissions before renewing contracts.

This shift requires a fundamental change in how employers collect and analyze benefit data. Instead of relying on vendor-generated dashboards, employers must work with third-party data warehouses and actuarial firms to track actual medical spend. This process involves comparing the claims of employees enrolled in a digital health program against a matched control group of non-participants with similar risk profiles. By focusing on hard endpoints, such as a reduction in spinal surgeries for employees using digital MSK platforms, employers can isolate the true financial impact of their digital health investments.

Additionally, the rise of high-cost therapies, particularly GLP-1 receptor agonists for weight loss and diabetes, has forced employers to scrutinize digital health programs even more closely. Many digital weight management vendors claim their programs reduce overall spend, yet pharmacy claims for these medications continue to skyrocket. Employers must evaluate whether a digital program actually helps titrate patients off these expensive drugs or if it merely acts as a funnel to increase prescription volume. True ROI in this category must be measured by the program's ability to achieve sustained clinical outcomes that allow for the safe discontinuation of high-cost pharmaceuticals.

## Methodologies for Measuring Economic Impact

Measuring the economic impact of digital health requires a solid understanding of health economics and actuarial science. Historically, state disease management programs struggled to prove ROI due to poor evaluation methodologies, a challenge documented extensively by health economists like Thomas W. Wilson. One of the primary methodological errors is failing to account for regression to the mean. Patients typically enroll in disease management programs when their symptoms and costs are at an all-time high; even without intervention, their costs would likely decrease over the following year. Failing to adjust for this natural trend leads vendors to claim credit for savings they did not generate.

To avoid this pitfall, employers should utilize a quasi-experimental design with a matched control group. This methodology matches program participants with non-participants based on age, gender, geographic location, comorbidities, and baseline healthcare spending. By comparing the cost trajectories of these two groups over a twelve-to-twenty-four-month period, employers can determine the net savings directly attributable to the digital health intervention. This approach filters out external factors, such as changes in plan design or general healthcare inflation, providing a much more accurate picture of the program's financial performance.

Another essential metric is the medical cost ratio (MCR) or the medical expense ratio (MER) associated with the targeted condition. Employers should calculate the total spend on a specific disease state—such as musculoskeletal disorders—before and after implementing a digital solution. If the total spend on physical therapy, imaging, injections, and surgeries does not decrease on a per-capita basis across the entire eligible population, the digital program is not delivering a positive ROI. This population-level analysis prevents vendors from hiding poor overall performance behind a small group of highly successful program completers.

## Comparing Digital Health Categories: Where the Savings Actually Lie

Not all digital health categories are created equal when it comes to generating a positive financial return. Some clinical areas, such as musculoskeletal care and digestive health, offer direct pathways to savings by steering patients away from high-cost surgeries and unnecessary diagnostic imaging. Other areas, such as general mental wellness apps, struggle to demonstrate any direct claims reduction, even if they improve employee morale. Understanding these differences allows employers to allocate their benefit budgets to the categories most likely to yield a tangible return.

| Clinical Category | Typical Pricing Model | Primary Source of Savings | Realistic ROI Timeline | Proven Claims Impact |
| --- | --- | --- | --- | --- |
| Musculoskeletal (MSK) | PMPM or Per-Participant | Avoided surgeries, reduced physical therapy claims, fewer MRIs | 12 to 18 Months | High (1.5:1 to 2.5:1) |
| Digestive Health | Per-Participant | Reduced emergency room visits, fewer specialist consultations | 12 to 24 Months | Moderate (1.2:1 to 1.8:1) |
| Diabetes & GLP-1 | Per-Participant + Drug Cost | Medication adherence, potential drug titration, reduced complications | 24 to 36 Months | Low to Moderate (Highly variable) |
| Mental Health (General) | PEPM (Per Employee Per Month) | Reduced absenteeism, improved retention (soft savings) | 24+ Months | Low (Hard to measure via claims) |
| Care Navigation | PEPM or PMPM | Steerage to high-value providers, reduced out-of-network care | 12 to 24 Months | Moderate (1.1:1 to 1.5:1) |

As the table indicates, musculoskeletal interventions present some of the most reliable opportunities for hard cost savings. By providing digital physical therapy and autonomous clinical guidance directly to the employee's home, these programs can successfully divert patients from expensive orthopedic consultations and subsequent surgeries. In contrast, diabetes management programs, particularly those bundled with continuous glucose monitors and GLP-1 prescribing capabilities, require a much longer timeline to show positive returns. The high cost of the devices and medications often offsets any immediate savings from improved glycemic control, making strict clinical criteria essential for these programs.

## Step-by-Step Framework for Evaluating Vendor Claims

When evaluating a new digital health vendor, employers must follow a structured vetting process to separate marketing claims from clinical reality. The first step is to demand a detailed breakdown of the vendor's actuarial methodology. If the vendor refuses to share their underlying data assumptions or relies solely on internal white papers rather than peer-reviewed studies, this should be treated as a major red flag. Employers should insist that any ROI claims be validated by an independent, credentialed actuarial firm using standard industry guidelines.

The second step is to establish a clear baseline of the employer's own historical claims data for the specific condition. Before signing a contract, the employer must know exactly how much they spend annually on the target population, including medical, pharmacy, and disability costs. This baseline data serves as the foundation for all future ROI calculations. It also helps identify whether the employer's population actually has a high enough disease burden to justify the intervention; introducing a digital diabetes program to a young, exceptionally healthy workforce is highly unlikely to yield a positive financial return.

The third step involves structuring the contract to align the vendor's financial incentives with the employer's savings goals. Employers should push for performance-based pricing models, where a portion of the vendor's fees is at risk based on achieving specific, measurable outcomes. These outcomes should not be limited to engagement metrics like app logins; instead, they should include clinical markers, such as a specific reduction in HbA1c levels, or financial markers, such as a reduction in total orthopedic claims. If the vendor is confident in their ability to generate ROI, they should be willing to put their own revenue on the line.

## Common Pitfalls in Employer ROI Calculations

One of the most common mistakes employers make when calculating digital health ROI is ignoring the cost of the program itself. Vendors often present "gross savings" figures, which represent the total reduction in medical claims among participants. However, to calculate "net ROI," employers must subtract the total fees paid to the vendor, including implementation costs, communication campaigns, and internal administrative time. A program that saves $100,000 in medical claims but costs $120,000 in fees and administration actually results in a negative return of $20,000.

Another frequent pitfall is selection bias, which occurs when the employees who choose to participate in a digital health program are inherently more motivated and healthier than those who do not. These highly motivated individuals are already proactive about managing their health and would likely have lower healthcare costs regardless of the digital tool. If a vendor compares this self-selected group to the rest of the employee population, the resulting "savings" are artificial. Employers must insist on risk-adjusted comparisons that account for these behavioral differences to get an accurate assessment of the program's efficacy.

Finally, employers often suffer from "point solution fatigue," where they implement multiple overlapping digital health programs without considering how they interact. For example, an employer might offer one digital program for diabetes, another for weight loss, and a third for general wellness. If a patient enrolls in all three, each vendor may claim credit for the same reduction in healthcare utilization, leading to double or triple counting of savings. To prevent this, employers must utilize a centralized data platform that can deduplicate savings claims and attribute financial impact accurately across the entire benefit ecosystem.

## The Role of AI and Automation in Modern Benefit Design

The integration of artificial intelligence into digital health platforms is transforming the economic equation for employers. Historically, digital health programs relied heavily on human coaches, physical therapists, or educators to guide patients. While effective, this human-in-the-loop model is expensive to scale and limits the vendor's ability to lower prices. In contrast, autonomous AI platforms, such as Flok Health in the musculoskeletal space, use advanced algorithms to deliver personalized clinical therapy without requiring constant human intervention. This shift dramatically lowers the delivery cost of the care, allowing vendors to offer more competitive pricing to employers.

These AI-driven models can also adapt in real-time to an employee's progress, providing a level of customization that was previously impossible at scale. For instance, an AI system can analyze an employee's movement via a smartphone camera during a physical therapy session, correcting their form instantly and adjusting the difficulty of the exercises. By automating these routine clinical tasks, the platform reduces the need for expensive specialist visits while maintaining high levels of patient safety and engagement. This reduction in professional labor costs directly translates into a lower break-even point for the employer's investment.

However, employers must approach AI-driven solutions with a critical eye, ensuring that the technology is backed by robust clinical validation. It is not enough for a vendor to claim their AI is highly advanced; they must demonstrate that the autonomous system achieves clinical outcomes equivalent to or better than traditional, human-led care. Employers should look for platforms that have received regulatory clearance, such as FDA clearance for specific medical algorithms, and those that publish their clinical trial data in reputable medical journals. Investing in unproven AI tools risks not only financial loss but also potential harm to employees.

## Financial Thresholds and Contractual Guarantees

To protect their financial interests, employers must establish clear financial thresholds and demand robust contractual guarantees from digital health vendors. A standard performance guarantee should require the vendor to put fifteen to twenty-five percent of their total fees at risk. These fees should only be paid out if the vendor meets specific, mutually agreed-upon key performance indicators (KPIs) over the course of the contract year. These KPIs should be divided between operational metrics, clinical outcomes, and financial savings to ensure a balanced evaluation of the program's performance.

When establishing financial thresholds, employers should target a minimum net ROI of 1.5:1. This means that for every dollar spent on the digital health program, the employer should realize at least one dollar and fifty cents in direct medical or pharmacy claims savings. Any program that struggles to project a net ROI above 1.2:1 should be viewed with skepticism, as the administrative burden of managing the vendor often erodes these slim margins. Employers should also establish a clear timeline for these savings, typically requiring the program to reach a break-even point within the first eighteen months of implementation.

The decision to implement or renew a digital health program should be driven by a structured annual review process. If a vendor fails to meet their performance guarantees or cannot provide clean, third-party validated claims data at the end of a two-year cycle, the employer should be prepared to terminate the contract. In the rapidly evolving digital health market of 2026, there is no shortage of alternative solutions. By maintaining a disciplined approach to financial accountability, employers can ensure that their healthcare benefits budget is spent only on programs that deliver genuine value to both the organization and its employees.

## Quick answers

### What is the difference between gross ROI and net ROI in digital health?

Gross ROI measures the total reduction in medical claims generated by a program, whereas net ROI subtracts the program's total cost, including implementation and administrative fees, from those savings. An employer must always calculate net ROI to determine if a vendor actually saved them money or simply increased administrative overhead.

### How does regression to the mean affect digital health evaluations?

Regression to the mean is a statistical phenomenon where patients with exceptionally high healthcare costs naturally experience lower costs over time, even without intervention. If a vendor does not use a matched control group to adjust for this trend, they will falsely attribute these natural cost decreases to their digital health program.

### What percentage of fees should be put at risk in performance guarantees?

Employers should negotiate to put fifteen to twenty-five percent of a digital health vendor's total fees at risk. These fees should only be paid out if the vendor meets specific, pre-determined clinical and financial outcomes verified by independent claims analysis.

### Are digital diabetes programs with CGMs cost-effective for all employees?

Independent evaluations, such as those by the Peterson Health Technology Institute, show that broad deployment of continuous glucose monitors (CGMs) and digital diabetes tools across entire populations is rarely cost-effective. These tools should be strictly targeted to high-risk, insulin-dependent cohorts where clinical and financial returns are most viable.

### How can employers avoid double-counting savings from multiple point solutions?

Employers should utilize an independent third-party data warehouse to centralize all claims data and deduplicate savings. This ensures that if a patient is enrolled in multiple programs, the financial impact is accurately attributed rather than claimed multiple times by different vendors.

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