across five years
The latest result may not look dramatic alone. The trend, family history, waist circumference, and access to prevention programmes tell a different story.
For health plans, employers, and life sciences
If earlier intelligence influences just 10% of current U.S. annual health spending.
CDC spending context ↗*Illustrative projection based on 10% of $5.3T in annual U.S. health spending. Actual opportunity depends on population, intervention, adoption, and outcomes.The opportunity
A diagnosis creates a code. An admission creates a claim. A prescription creates a transaction. By then, the disease has already announced itself.
MedArchitex looks for the quieter period: risk rising across time, a missing follow-up, an intervention that will fail because the person cannot afford or reach it. That is where prevention may still have room to work.
Two member pathways
The same transition can appear across laboratory trends, missing tests, changing circumstances, and care that arrives too late.
The latest result may not look dramatic alone. The trend, family history, waist circumference, and access to prevention programmes tell a different story.
A member has diabetes, hypertension, and a declining eGFR. The record has no recent urine albumin-creatinine ratio. The absence itself is actionable.
The prevention maths
Move the assumptions. The upside moves with them. Earlier intelligence can turn preventable deterioration into measurable economic value.
*Projection based on the selected population and assumptions, including $12,000 per avoided high-cost event. Programme cost, selection effects, clinical efficacy, and time to savings are not included.
MedArchitex Prevent
Prioritise preventive outreach, expose care gaps, and distinguish biological risk from the barriers that make a good recommendation impossible to follow.
MedArchitex Evidence
Build pre-diagnosis cohorts, examine progression, and understand where access, adherence, and treatment pathways break down in the real world.
A hard boundary
MedArchitex is not designed to set premiums, exclude coverage, or make treatment harder to obtain. If a prediction is used to withdraw help, it has been used for the wrong purpose.
Figures and context