We turn plan-level guidelines into individual-level follow-through. Built for population health teams who need cleaner, more testable claims about adherence.
HEDIS measures, clinical guidelines, and registries already define the right actions for most members. The gap is closing the distance between the right action and the actual life of the person who has to take it. Members want to follow through, primary care teams want to help them, and the structure between them isn't built for that translation. The PCP shortage stretches new-patient waits, specialty access slips by months, and referral and prior-auth processes add weeks on top.
Morgen runs every candidate plan against the member specifically, including motivation, constraints, history, and what they will and won't realistically do. The plan that comes out the other side has a chance of being completed.
We don't claim to move outcomes directly. We claim to raise completion of the actions that prevent progression for a specific member, which is a cleaner and more testable claim that maps directly to HEDIS.
GLP-1s changed what's possible pharmacologically, but adherence still falls off after about twelve months. Members come off the medication, regain weight, and return to baseline labs, leaving plans paying for the same outcome twice.
Morgen is the behavioral layer that holds the result. We simulate the lifestyle plan most likely to be completed by this specific member and walk them through it the same way we'd walk them through a referral or a follow-up, with the plan adjusting as the data comes in.
For members not on GLP-1s, the same simulation runs the diet, activity, and behavioral interventions that can produce comparable results without the prescription. A user used the prototype for a year and saw a roughly 70% drop in triglycerides and a 20% drop in total cholesterol on their annual primary care labs.
One early user's outcome. We're running cohort pilots to test whether this generalizes.
HEDIS measures, claims, and registries map directly into the twin's progression model. No custom feeds.
Bidirectional FHIR for the cohort under test. Read clinical context. Write back action completion.
SMS, email, and in-app. Tone and timing matched per member based on the agentic score.
Personal advisors with experience at Georgia Tech, Stanford Health Care, Cleveland Clinic, and the Harvard School of Public Health, spanning public health, AI research, clinical care, and academic medicine. Advisors participate in personal capacity and don't represent their institutions.
MVP is live and the first pilot launches Q2 2026.