Healthcare data mapping, re-engineered
Turn source schemas into auditable healthcare model outputs.
MEDMAPPER AI STUDIO combines evidence-backed AI, structured human review, and enterprise deployment controls so teams can move from raw schema complexity to approved target-model delivery with less risk.
Source SystemEpic Clarity / AthenaOne / Snowflake
Start from the real healthcare source systems your team already operates.
MEDMAPPER AI STUDIOInference + Review + Validation
Explainable AI suggestions, confidence bands, and human approval in one governed workflow.
Joins0.94
Mappings127
Blocked6
Validated OutputOMOP / PCORnet / FHIR / GitHub
Deliver approved-table SQL and dbt-oriented artifacts once readiness gates are satisfied.
Review signal
Human-in-the-loop by defaultConfidence-aware review queue keeps human effort focused where the risk is real.
Deployment mode
SaaS or customer data planeKeep the same product workflow while adapting runtime boundaries to the environment.
Confidence-aware workflowv1 enterprise preview
Join evidence
0.94Auto-approvedMapping review
127 fields16 need reviewDelivery path
DiscoveryInferenceReviewValidationPublish
Why teams switch
AI assistance where it speeds work. Review controls where it protects trust.
The site should sell a practical system, not a black-box promise. MedMapper’s advantage is the combination of automation, evidence, and enterprise readiness.
Evidence-backed AI mapping
Map healthcare source schemas into OMOP, PCORnet, FHIR, Sentinel, i2b2, CAPRICORN, and custom models with confidence scoring and explainable suggestions.
Human review where it matters
Join evidence, mapping workbench approvals, validation results, and auditability keep the platform trustworthy for regulated data engineering.
Built for enterprise healthcare
Warehouse-native execution, PHI-aware boundaries, multi-tenant controls, and customer-deployed data plane support fit enterprise delivery models.
Operating model
How teams use MedMapper across the full mapping program.
The home page should describe the business workflow around delivery, review, and reuse. The technical pipeline lives on the platform page.
Launch the project
Start from the source system and target model you actually need to support, not a generic transformation template.
Coordinate the right reviewers
Give data engineers, model owners, and informatics reviewers a shared place to inspect evidence and approve decisions.
Control mapping quality
Use confidence states, evidence panels, and review queues to focus human time where the risk is real.
Ship governed outputs
Move approved tables into validated SQL, dbt-oriented artifacts, and downstream delivery without bypassing readiness gates.
Reuse what your teams learn
Turn reviewed mappings, model assets, and marketplace extensions into reusable institutional knowledge over time.
Core product
The platform is built around concrete mapping work, not generic AI claims.
Each surface on the site should tie back to an actual MedMapper workflow component.
Join evidence
Evidence before approvalUse overlap score, type compatibility, and critic checks to make join confidence understandable instead of magical.
Lineage and validation
Trace every target pathFollow how source tables, transforms, and target models connect before the team trusts generated outputs.
Delivery
Approved-table outputs onlyMove ready tables into SQL, dbt-oriented artifacts, and GitHub workflows while blocked tables stay visible and gated.
Mapping Workbench
Spreadsheet-grade review surface for source expressions, transform types, confidence bands, and bulk approvals.
Join Evidence Viewer
Inspect overlap scores, type compatibility, domain plausibility, and critic warnings before trusting join paths.
Lineage and validation
Trace source-to-target paths and move from proposed mappings to export-ready artifacts with explicit validation gates.
SQL, dbt, and GitHub publish
Generate approved-table outputs and push deterministic bundles into a governed delivery workflow.
Security posture
Keep PHI boundaries clear and deployment options credible.
The marketing site should reinforce trust with simple architecture language, not security theater.
Push-down execution
Run computation where the customer data already lives so MedMapper does not need to extract raw patient data.
PHI-aware AI policy
Scrub and normalize schema metadata before LLM use, with evidence-first reasoning and bounded AI behavior.
Deployment flexibility
Support SaaS delivery or customer-deployed data planes with centralized control-plane operations.
Ecosystem
Extend the platform with governed models, mapping packs, and shared intelligence.
Marketplace and knowledge-bank positioning give the site a stronger systems story than a single-product homepage.
Target model catalog
Distribute OMOP, PCORnet, FHIR, Sentinel, i2b2, CAPRICORN, and custom models with version-aware governance.
Reusable mapping intelligence
Surface reviewed mapping patterns and shared knowledge-bank signals without exposing tenant data.
Vendor ecosystem extensions
Let partners publish trusted dictionaries, value sets, and implementation assets into a governed marketplace.
FAQ
Short answers for technical buyers.
Keep this section tight and useful. It should help qualified prospects understand fit quickly.
What does MedMapper AI Studio actually accelerate?
It accelerates the path from healthcare source schemas to governed target-model mappings, review, validation, and downstream delivery.
Is the platform only for OMOP?
No. OMOP is a core workflow, but the platform also targets PCORnet, FHIR, Sentinel, i2b2, CAPRICORN, and custom models.
Can it fit strict enterprise environments?
Yes. The architecture supports tenant controls, auditability, PHI-aware handling, and customer-deployed data planes.
Ready to evaluate fit?