Cordato proposes a wellness-classified, AI-supported health forecast layer that sits above existing programs, predicts physiologic risk before symptoms, and helps reduce avoidable hospitalizations across the country.
Wearables, home devices, and RPM programs generate large volumes of data. Very little of it is converted into early, actionable risk prediction across programs. Signals live in silos instead of a shared early-warning layer.
Across ACA, Medicaid, and Medicare, the largest cost drivers are still inpatient and emergency events. Programs end up paying for deterioration rather than preventing it, even when early physiologic changes are visible days in advance.
ACA premiums, state Medicaid budgets, and Medicare projections all face pressure from preventable events. Policymakers lack a simple way to reduce these events at scale without adding complexity or member burden.
Cordato functions as a preventive signal layer: a cloud-based system that ingests continuous wearable signals and daily physiology, builds individual baselines, and detects trend breaks 48–72 hours before symptoms or deterioration.
Instead of changing how benefits are paid, the layer changes when care is triggered. Interventions move several days earlier, when they are less costly, less invasive, and more effective.
Cordato does not replace ACA, Medicaid, or Medicare. It sits above and alongside existing programs and provides:
Launch an initial pilot with a willing state, such as a Texas Medicaid RPM replacement, to generate real-world evidence on:
Successful results can inform a CMMI innovation model that offers a similar signal layer to other states and programs.
Cordato can be offered as a preventive overlay for ACA enrollees, especially in high-deductible and silver-tier plans, with goals that include:
For Medicare and dual-eligible populations, the same signal layer can be used to:
Cordato’s methodology is rooted in early Moneyball-style analytics: prediction wins and reaction loses. The same logic that gives sports teams a predictive edge can now be applied to population health.
In healthcare, we have the signals to see risk forming. The policy choice is whether to build the layer that uses them or continue paying for crises that were visible in advance.
This page is intended to support early-stage exploration. If the concept is directionally aligned, we welcome the opportunity to: