Use Case — Healthcare
Synthetic healthcare data — certified AI training without patient records
Healthcare AI requires large, labeled datasets but access to real patient data is restricted by HIPAA, HITECH, and institutional review requirements. Certified synthetic EHR and clinical data gives you the training volume you need — with cryptographic proof that no real PHI was used.
What this means for your data strategy
Electronic Health Record (EHR) data, diagnostic imaging metadata, clinical trial records, and patient outcome data are among the most sensitive datasets in existence. Healthcare AI researchers face a paradox: models need large training sets to be clinically useful, but real patient data is difficult and slow to access. Certified synthetic healthcare data breaks this bottleneck — providing statistically realistic training data with documented proof of synthetic origin that satisfies IRB, HIPAA, and institutional security review requirements.
How CertifiedData helps
- →Generate synthetic EHR datasets with realistic patient demographics, diagnoses, medications, and lab results
- →Produce labeled synthetic clinical trial data for protocol testing without patient enrollment
- →Create diagnostic classification training sets (rare conditions, edge cases) at scale without real patient records
- →Certify that model training data contains no real PHI — documented with an Ed25519-signed certificate
- →Accelerate IRB approvals and data sharing agreements by removing real patient data from the process entirely
Regulatory context
Healthcare AI operates under HIPAA (45 CFR Parts 160/164), HITECH, EU MDR (Medical Device Regulation), and FDA guidance on AI/ML-based software as a medical device (SaMD). HIPAA requires de-identification of PHI for research use — but de-identification still starts from real data. Certified synthetic data takes a different approach: the data is never derived from real records in a way that could re-identify individuals, and the certificate proves it.
Why cryptographic certification matters
A certified synthetic healthcare dataset provides documentation that an auditor, IRB, or FDA reviewer can verify independently: the dataset was synthetically generated, has a specific cryptographic fingerprint, and has not been altered since generation. This is particularly valuable for FDA submissions where training data provenance is a required element of the Software as a Medical Device (SaMD) technical file.
Each certificate records: dataset SHA-256 fingerprint, generation algorithm, timestamp, and an Ed25519 signature from CertifiedData's signing infrastructure.
Verification is public: any third party can verify the certificate without a CertifiedData account.
Frequently asked questions
Does synthetic healthcare data satisfy HIPAA de-identification requirements?
HIPAA defines two de-identification methods: Safe Harbor (removing 18 identifiers) and Expert Determination (statistical analysis of re-identification risk). Certified synthetic data generated from population statistics — not from real patient records — can satisfy Expert Determination requirements. Consult your privacy officer and legal counsel for your specific use case.
Is synthetic EHR data realistic enough for clinical AI training?
CertifiedData uses CTGAN which preserves the statistical distributions of real EHR data without retaining any individual records. For most classification and regression tasks in clinical AI, the resulting synthetic data is sufficient for model training, especially when supplemented with domain knowledge from clinical experts.
How does this help with FDA AI/ML SaMD submissions?
FDA guidance on AI/ML-based SaMD requires documentation of training and test data. A certified synthetic dataset provides a timestamped, signed provenance record that can be included in the technical documentation, demonstrating data governance and de-identification practices.
Related resources
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