Need Synthetic Data Generation for Regulated-Domain Research Access
Iris waits months to years for ethical approvals and data extractions before she can touch real patient data. She needs a synthetic-data pipeline that's data-driven, schema-conformant, and privacy-preserving — so research can move while the legal track runs in parallel.
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Persona Story:
Iris’s team has a question: can we predict heart failure six months before formal diagnosis? The data exists. Halland alone has 15 years of records on half a million people. But before Iris can train a model, the project has to run an ethical application, a research plan, a data extraction request — months at best, two years at the long tail. Real-world impact dies in that gap. She wants a synthetic-data pipeline that’s data-driven (not rule-based), produced by looking at the original distribution shape and generating similar-but-not-identical records, conforming to the same open-standard schema clinicians use. Researchers move while the legal track runs in parallel; nobody touches PHI until clearance lands.
Problem Context
- Regulated-domain research-data access pipelines (ethical applications, research plans, extraction tickets) take months to years
- Rule-based synthetic data is fast to generate but doesn’t reflect real distributions, so models trained on it don’t transfer
- Data-driven synthetic generation needs access to the original data first — circular dependency unless the synthetic pipeline lives inside the data boundary
- Researchers who can’t wait often pivot to less-impactful problems they have data for, instead of the high-impact problems they don’t
Problem Impact
- Research-to-impact lag in healthcare AI is measured in years
- Ethical-approval queues are clogged with projects that could have started on synthetic data and only needed real data for final validation
- Cross-institution collaboration stalls because partner institutions can’t share even synthetic data unless its provenance and privacy posture is auditable
- Industry partners (vendors, consultancies) can’t prototype dashboards or AI features against realistic data without the same multi-year wait
Naftiko Today
- Capability YAML can declare a synthetic-data generator as a consume that produces records conforming to a typed output schema (FHIR resource, openEHR archetype, OMOP table)
- JSON Schema validation enforces that synthetic records are structurally indistinguishable from real ones at the contract level
- OutputParameters normalization ensures generated records pass through the same shaping pipeline real data does
- Naftiko Engine runs the generator inside whatever enclave the data boundary requires — same container abstraction works inside or outside the firewall
Naftiko Tomorrow
- Reference capabilities for FHIR and openEHR-conformant synthetic generation (Second Alpha) — clinicians and researchers fork instead of starting from scratch
- Starter templates for common conditions (Second Alpha) — synthetic chronic-wound, heart-failure, diabetic-foot-ulcer cohorts ready to run
- Webhook adapter (Second Alpha) — synthetic-data pipelines that emit cohort updates to dashboards and research notebooks
- JSON Schema Store publication (GA) — synthetic-generation contracts published as discoverable, reusable schema-bound artifacts