Prometheno.
Memory, restored.
Your health data generates value for research every day. You never see it come back. Prometheno changes that — patients earn when their records move research forward, control who studies their data, and audit every access. Built on HAVEN, an open protocol. Running today on 1,000+ MIMIC-IV patients.
Read further from where you stand.
For medical researchers.
Forge- Cohort tools ignore consent
- No audit chain to hand IRB
- Multi-site dies at paperwork
Forge + HAVEN end the first two. PSDL ends the third.
For clinical leadership.
HAVEN- Audit assembled by hand
- Every cohort restarts the cycle
- Consent revocations don't propagate
HAVEN consent + audit, built into Prometheno. Automatic.
For patients.
Ember- Records scattered across providers
- Studies you joined never report back
- Data used without your consent or share
Be in the loop, not the dataset.
Medical research depends on lives rendered legible. We return it — to the patient, by their consent, for their share.
Data
Your records scattered across hospitals, clinics, apps, and labs — pulled together into a single shared language that research can read.
Nothing lost. Nothing flattened.
OMOP CDM 6.0 · FHIR R4 · vocabulary mapping for LOINC, RxNorm, ICD-10, CPT4
Trust
No one reaches for your data without your consent.
Every access is logged in a way nothing can hide — not from you, not from a court.
Content-addressable consent · Hash-chained audit · Merkle inclusion proofs
Value
When your records help research move forward:
- Payment — actual money, tiered by each record’s clinical weight.
- First in line — priority access to treatments and trials from research you helped.
- Personal answers — what the research learns, applied back to your own care.
Three-tier valuation · Quality gates · Attribution registry · Outcome-linked distribution
The work above is early. The market it points at is not.
Built on open specs. SDK and public API coming after the first pilot lands. For now, the foundations are public.
Consented research
Patients in the loop, not the dataset.
Outcome validation
Real-world AI accountability for what shipped models actually do.
Actuarial evidence
A foundation for risk pricing built on truth, not survey.