HIPAA-Compliant Test Data
Real patient records are radioactive in a test environment — too risky to copy into dev, staging, or a demo, and a chore to de-identify well enough to trust. Synthetic patient data sidesteps the whole problem: SimpleIDGen generates records from public reference distributions (NHANES, ACS, CDC), so there is no protected health information in them to begin with. Nothing is learned from, or traceable to, a real patient.
No signup. See the synthetic patient data →
HIPAA's protections attach to protected health information — health data tied to a real, identifiable individual. De-identification (Safe Harbor or Expert Determination) starts from real records and tries to strip that link, but the rows still descend from real patients and re-identification risk never fully disappears.
Synthetic patient data has no such lineage. SimpleIDGen never ingests a real clinical dataset; it samples each attribute from a published distribution and assembles fresh patients. There is no patient behind any row, so there is no PHI to protect or de-identify in the first place. (That's a property of the data — confirm your specific use with your own compliance team.)
Calibrated demographics plus the clinical fields most testing actually needs — vitals (A1c, BMI, blood pressure, height, weight, waist) and common condition flags (diabetes, hypertension, CKD, and more), calibrated to NHANES 2017–2020 by age and sex and drawn so they cohere across the record. Delivered as a flat CSV or JSONL — no FHIR, no EHR bundle to parse.
| Property | What it means for HIPAA-bound work |
|---|---|
| No PHI by construction | No row maps to a real patient; there is nothing to re-identify or de-identify. |
| No real source data | Built from public references (NHANES, ACS, CDC), not learned from clinical records. |
| Clinically plausible | Vitals and conditions track age, sex and BMI, so the data is realistic for testing. |
| Deterministic | Same seed yields the same patients — reproducible across CI runs and environments. |
| Yours to keep | CSV / JSONL you can store, share with vendors, and pipe anywhere — no PHI rules to inherit. |
Seeding non-production databases for a health app without copying production PHI; QA and integration tests; demos and sales sandboxes; sharing realistic datasets with vendors, contractors, or offshore teams; and training or evaluating models where regulated patient data can't be used. For the EU/India personal-data angle, see GDPR-safe test data →.
No. Every patient is generated from public reference distributions. No real clinical dataset is ingested and no row maps to a real person, so there is no protected health information.
HIPAA's obligations attach to PHI — health data about a real, identifiable person. Synthetic patients contain none, which is why teams use them where production patient data can't go. That's a property of synthetic data, not legal advice; confirm your scenario with your compliance officer.
De-identification transforms real records and carries residual re-identification risk. Synthetic data is generated from distributions — there is no original patient to recover, because none existed.
Flat CSV or JSONL — one patient per row. No FHIR, no Java, no EHR bundle. Free 1,000-row sample with no signup; a free account generates up to 5,000 rows/day.