Prometheno  ·  For medical researchers
01 — Premise
For medical researchers

Cohorts that respect consent.

Atlas-style cohort builders fetch by criteria and ignore consent context. Forge is built differently: every query is consent-scoped, every access is HAVEN-audited. The cohort you ship is the cohort you can defend — running today on 1,000+ MIMIC-IV patients.

02 — What hurts

Three pains every clinical researcher knows.

01

Your cohort is a compliance gamble

Atlas, i2b2, and EDW exports fetch by criteria and ignore consent context. Your published cohort may include patients who never consented to that purpose. You won’t know until someone asks.

02

No audit chain to hand IRB

When IRB or a journal asks who consented to what, you compile from spreadsheets, emails, and EHR audit logs. There is no single, signed chain you can hand over. There is no chain at all.

03

Multi-site cohorts die at the paperwork

Cross-institutional data sharing requires BAA, DUA, IRB reciprocity per pair of institutions. Months of legal before a single query runs. Most multi-site studies fail here, not at the science.

03 — What I built

Forge as the cohort builder. HAVEN underneath. PSDL family as bonus for cross-site reproducibility.

Researcher app · built into Prometheno

Forge

Cohort builder with consent baked in
Forge is the OHDSI Atlas-style query interface, but every cohort is consent-scoped at query time and HAVEN-audited at access time. AI Cohort Builder turns natural language into OMOP SQL. Exports to CSV, JSON, FHIR R4, OMOP CDM.
Version: v0.10.0
Stack: Next.js 14 + Tailwind
Data: 1,000+ MIMIC-IV patients
Solves: Cohort consent compliance, audit trail, OMOP querying without ignoring sovereignty
Open protocol · the foundation

HAVEN

Consent + audit + attribution, by spec
Four primitives: content-addressed Health Asset, programmable Consent (immediately revocable), hash-chained Provenance (Ed25519-signed), quality-weighted Contribution. What makes Forge consent-aware in the first place.
Spec: v2.0 draft, CC BY 4.0
Citation: DOI 10.5281/zenodo.18701303
Tests: 27 passing in Prometheno backend
Solves: Patient sovereignty, tamper-evident audit, attribution back to contributors
Reference platform

Prometheno

OMOP backbone · HAVEN reference implementation
PostgreSQL + OMOP CDM 6.0 (270K vocabulary concepts), FHIR↔OMOP pipeline. Runs Forge for researchers, Ember for patients. 27 HAVEN protocol tests passing.
Version: backend v0.8.0
Data: 17K+ records synced
Apps: Forge + Ember
Solves: OMOP backbone, vocabulary normalization, the substrate Forge runs on
Bonus · for cross-site reproducibility

PSDL family

PSDL spec · psdl-inspector · PSDL-workbench
Optional layer for portable clinical scenarios across institutions. PSDL is the spec, psdl-inspector is the visual builder + Certified Bundle export, workbench maps your local EDW to PSDL signals (works even without OMOP). Use only if you need multi-site reproducibility.
PSDL: v0.5, 539 tests
Inspector: v0.2.0
Workbench: in development
Solves: Cross-institutional cohort definitions, Certified Bundle export, EDW semantic mapping
04 — Demo

Same scenario. Two simulated sites. Byte-identical hashes.

An AKI Detection scenario, authored once in psdl-inspector, compiled to a Certified Bundle, executed against MIMIC-IV (Site A) and a simulated dataset (Site B). The output hashes match. That is what reproducibility looks like at the protocol layer.

Forge AI Cohort Builder showing a natural-language diabetes cohort query ready to compile to OMOP SQL
Forge
Explore · NL cohort on Prometheno OMOP
psdl-inspector showing the AKI Detection scenario in PSDL YAML, with KDIGO guideline reference in the audit block
psdl-inspector
Formalize · AKI scenario · KDIGO audit
PSDL-workbench dashboard for institutional EDW semantic mapping (in development)
In development
PSDL-workbench
Deploy · your EDW → PSDL signals (even without OMOP)
Step 01

Author

AKI scenario in psdl-inspector with KDIGO criteria.

scenario: AKI_Detection audit: intent: ... rationale: KDIGO 2012 signals: { Cr: ... } trends: { delta_6h: ... } logic: { aki_risk: ... }
Step 02

Certify

Compile to a Certified Bundle. SHA-256 binds everything.

{ "bundle_version": "1.1", "checksum": "sha256:abc123...", "scenario": { ... }, "terminology_anchors": { "creatinine": { "concept_id": 3016723, "vocabulary_id": "LOINC" } } }
Step 03

Run · Site A

Execute on MIMIC-IV via Prometheno. Hash recorded in HAVEN audit chain.

$ psdl run bundle.json --site mimic-iv  Triggered patients: 47 Result IR hash: 0x9799d065d19f...e583 HAVEN audit entry: #1247
Step 04

Run · Site B

Same bundle, different dataset. Hash identical. Reproducibility, proven.

$ psdl run bundle.json --site sim-b  Triggered patients: 31 Result IR hash: 0x9799d065d19f...e583 HAVEN audit entry: #889  ✓ Hashes match
— Reproduce this yourself

The MIMIC-IV Colab notebook runs end-to-end in your browser. No install.

Hashes shown are illustrative. Actual hashes match across runs of identical inputs.

05 — Where this scales

When this works at scale.

Cross-institutional cohorts in hours, not months. Multi-site reproducibility as default. IRB documentation auto-generated from the same source as execution. Researchers spend their time on science instead of data acquisition.

We sell access tiers — academic discount, industry rates — to consented cohorts and to the platform that runs them. The protocols underneath stay open.