Patient data without the privacy review.
Realistic, privacy-safe synthetic patient records for healthcare AI training, medical simulation, and research. Across emergency, surgical, chronic, oncology, and rare conditions — 100% HIPAA-aligned by design.
Built for the hard parts.
Realistic, not random
Records that pass clinical sniff tests — coherent histories, plausible labs, longitudinal narratives. Built from de-identified distributions, not noise.
100% privacy-safe by design
Synthetic from the ground up. Nothing is re-identifiable because nothing real was used. HIPAA-aligned, no PHI, no consent surface.
All clinical domains
Emergency, surgical, chronic care, oncology, rare conditions, paediatrics. Generate cohorts at the size and balance your model needs.
FHIR R4 native
Output as FHIR R4 resources straight into your data lake. Compatible with EHR Bridge for end-to-end pipelines.
Cohort design
Specify demographics, comorbidities, severity distributions, and rare-event prevalence. Reproducible with seeded generation.
Validation reports
Per-batch reports on statistical fidelity, clinical plausibility, and bias. Auditable artefacts for IRB and regulatory review.
Plays nice with what you run.
Real PHI takes months of approvals and lawyers. MedSynth gets your model to first training run in hours, with no privacy review on the data itself.
Real-world validation. Use MedSynth for development, then validate the trained model on de-identified real data with the right governance in place.
What it does, in numbers.
Production-grade, not a prototype.
- Built and run by Orynx engineers — not handed off to a vendor.
- Privacy-by-design: end-to-end encryption, HIPAA / SOC 2 / ISO 27001 alignment.
- Pairs with our dedicated engineering teams for integration, customisation, and support.
