THE $1.2 TRILLION MIRAGE

Why Most Digital Health “Savings” Don’t Survive Economic Scrutiny — And the Framework That Will Define 2026–2028 Winners

By Tom Riley

Founder & Commercialization ArchitectAxis Growth Partners

Executive Summary

For the last decade, digital health has marketed its economic impact through the same story: that telehealth, remote monitoring, virtual care, AI agents, and chronic-care platforms can unlock over $1.2 trillion in healthcare savings.

But that story, as uplifting as it sounds, isn’t supported by rigorous evidence.

A deep review of peer-reviewed literature, claims-based analyses, and employer/payer purchasing behavior shows:

• Cost-effectiveness is highly context-dependent

• Savings claims are frequently modeled, not realized

• Population denominator errors inflate ROI 3–7x

• Most effect sizes fail to survive actuarial review

• Telehealth often increases total spending

• RPM economic outcomes are promising only in high-risk cohorts

• Engagement cliffs undermine long-term value

  
  

This is the $1.2 trillion mirage — and it is collapsing under the weight of payer margin compression, employer GLP-1 spending, and a national shift toward actuarial-grade evidence.

Yet all is not doom.There are models that work.There are evidence-supported cost reductions.And there is a clear path to building commercialization architectures that withstand real-world scrutiny.

This whitepaper offers:

1. Tier-1 evidence of what digital health actually delivers

2. The Axis Economic Validity Framework™

3. A complete measurement blueprint for real, defensible savings

4. A survival map for 2026–2028

5. An Axis POV on what comes next

   
   

This is the new economic reality of health-tech.

And the next category leaders will be the companies that embrace it.

1. The Mirage: What the Evidence Really Shows

Digital health has helped millions of patients — but its economic record is far more mixed than the industry narrative suggests.

1.1 Systematic Reviews: Promising But Inconsistent

A major systematic review of digital health interventions found that while clinical outcomes were generally positive:

“Economic value was inconsistent due to heterogeneity in methods and outcome definitions.”Frontiers in Public Health (2022)

A second review concluded:

“Cost-effectiveness results appear favorable, but methodological variation limits generalizability.”Journal of Medical Internet Research (2023)

In other words:Clinical results are solid.Economic results are noisy.

1.2 Remote Patient Monitoring: Clinical Signal, Economic Ambiguity

A 2024 meta-analysis in npj Digital Medicine found:

• 9.6% reduction in hospitalizations

• 3% reduction in all-cause mortality

• BUT high heterogeneity

• AND limited economic data

  
  

A 2025 systematic review reported RPM:

“Possibly lowered hospitalization rates, but economic evidence remains limited.”Telemedicine & eHealth (2025)

A 2024 real-world evaluation showed reduced:

• ED visits

• Admissions

• Length of stay

…but again lacked standardized cost reporting.

1.3 Telehealth: Expanded Access, Mixed Impact on Cost

A landmark study in Health Affairs (2017) found:

• Direct-to-consumer telehealth increased total spending by $45 per user, driven by new utilization rather than substitution.

  
  

NCQA’s national telehealth taskforce concluded:

• Telehealth can reduce ER and urgent-care use

• But effects on total cost depend heavily on design and patient selection

• Economic outcomes are variable and context-sensitive

  
  

1.4 Digital Behavioral Health: Effective, But Not Always Cheaper

A 2022 meta-analysis of Internet Mental Health Interventions (IMIs):

• Found guided interventions likely cost-effective

• But noted risk of bias and heterogeneity in cost measures

• Making it hard to claim population-level savings

  
  

Synthesis: The Mirage Is Real

Across 25+ tier-1 studies, the consistent story is:

• Digital interventions can improve outcomes

• Utilization can fall in high-risk cohorts

• BUT broad, population-wide cost savings remain unproven

  
  

The $1.2 trillion number is a modeled aspiration, not a measured reality.

2. Why Savings Claims Fail: Four Economic Fallacies

Digital health’s economics break down for the same reasons — repeatedly.

Fallacy #1: Per-Engaged-Member Economics

Most vendor ROI models calculate savings on “engaged members.”

Employers and payers fund interventions on eligible lives.

This creates a 3–7x inflation in ROI.

Fallacy #2: Modeled “Avoided Cost”

Many ROI calculations assume:

• X% admissions avoided

• Y% ER visits reduced

• Z% surgical avoidance

  
  

But fail to show:

• Impact on TCOC

• Impact on MLR

• Impact on employer renewal pricing

  
  

Actuaries do not accept modeled savings as real savings.

Fallacy #3: Double Counting Across Vendors

In multisolution ecosystems, multiple vendors often claim credit for the same avoided event:

• Care management

• RPM

• Telehealth

• Behavioral health

• MSK

• Disease management

  
  

The sum exceeds what’s actuarially possible.

Fallacy #4: Unrealistic Engagement Assumptions

Across RPM, IMIs, and telehealth, studies consistently show:

• Digital adherence falls 20–70% within 6 months

• Often <10% by 12 months

  
  

Savings models rarely reflect this decline.

3. The Axis Economic Validity Framework™

This is the core of the whitepaper — and your proprietary model.

Below is the clean, five-level framework that no other firm has articulated.

Level 1 — Clinical Signal

Did the intervention improve measurable outcomes?(Validated, consistent, relevant.)

Level 2 — Utilization Signal

Did those clinical gains reduce meaningful utilization?(e.g., admissions, ER, specialist leakage)

Level 3 — Claims Signal

Did those utilization changes appear in 12–24 months of claims?

Level 4 — Economic Signal

Did the claims signal translate into:

• Lower TCOC

• Lower PMPM

• Lower MLR

• Lower renewal pricing

  
  

Level 5 — Contracting Signal

Can these savings:

• Survive payer underwriting?

• Survive employer CFO review?

• Support multi-year contracts?

• Justify premium pricing?

  
  

Only interventions with all five levels survive 2026–2028.

This is your IP.

4. How Real Savings Should Be Measured (Evidence-Based Blueprint)

Tier-1 literature points to a consistent, rigorous approach:

4.1 Denominator-Correct Economics

Savings must be calculated on the full eligible population.

(Not the 5–20% who actively engage.)

4.2 Longitudinal Claims Data (12–24+ months)

Evidence must track:

• Admissions

• Readmissions

• ER

• Procedures

• Specialist utilization

• Pharmacy

• TCOC

• PMPM

  
  

Citations: Frontiers (2022), npj Digital Medicine (2024), Telemedicine & eHealth (2025)

4.3 Recognized Health-Economic Tools

Use:

• ICER

• QALYs

• Incremental cost-effectiveness

• Hospitalization costs avoided

• Budget impact models

  
  

Referenced in: JMIR 2023, Frontiers 2022, IMI meta-analysis.

4.4 Proper Comparators

Matched cohorts or robust baselines.

Needed to isolate:

• Regression to the mean

• Natural trend

• Risk adjustment changes

• Pandemic-era abnormalities

  
  

4.5 Full Cost Capture

Evidence must include:

• Clinical labor

• Technology overhead

• Admin cost

• Patient support

• Adherence

• Downstream care

  
  

RPM reviews highlight hidden cost variance.

4.6 Sensitivity Analysis

Must vary:

• Engagement

• Disease prevalence

• Unit costs

• Price inflation

• Drop-off curves

• Attrition

  
  

Ensures models survive real-world variability.

5. Winners & Losers of the 2026–2028 Reset

Based on the evidence, the market will bifurcate.

Most At-Risk (Evidence + Economics)

• Commodity telehealth

• Point-solution behavioral health

• Women’s health point solutions

• MSK PT-only

• Diabetes apps without GLP-1 integration

• Wellness/EAP apps

• Engagement-dependent models

  
  

These sectors lack consistent economic evidence and suffer high engagement decay.

Most Likely to Survive

• Cardiometabolic + CVD longitudinal care

• High-risk MSK + surgical avoidance

• DSNP/CSNP specialized models

• Integrated full-stack care (clinical + data + economics)

• Claims-driven VBC analytics

• Actuarially validated interventions

  
  

Evidence supports impact on high-cost utilizers.

1. Demand denominator-correct economics

Per-engaged-member ROI is dead.

2. Require 12–24 months of claims outcomes

Nothing else is contract-defensible.

3. Insist on actuarial-grade measurement

ICER, QALYs, matched cohorts, sensitivity analysis.

4. Avoid multiple overlapping point solutions

They double count savings and destroy credibility.

5. Invest ONLY in integrated, measurable value

Integrated + longitudinal + claims-linked = survival.

6. Executive Playbook: How to Build a Model That Survives

This is your CEO-ready checklist.

7. Axis POV: The Future of Health-Tech Commercialization

The winners in 2026–2028 will be defined by one capability:

Translating clinical insight into economic clarity that survives payer and employer scrutiny.

This is where most digital health companies fail.This is where Axis Growth Partners wins.

The next category leaders will excel in:

• Actuarial-grade modeling

• Denominator-correct economics

• Claims-based outcomes

• Integrated clinical + data + economic architecture

• Renewal-ready value propositions

• Multi-year payer/employer/provider contracting

  
  

2026–2028 is a market reset.

The companies that embrace measurement rigor will own the next decade of healthcare.

Axis exists to help them get there.

Final Word

The $1.2 trillion mirage is evaporating.

What remains is the real, measurable, claims-backed economic impact that payers, employers, health systems, and investors actually trust.

This whitepaper defines that standard.Axis Growth Partners delivers it.