Synthetic Data for Healthcare
Healthcare data is the hardest to obtain and the riskiest to move, which makes it the strongest case for generation from specification. Misata builds a coherent hospital dataset from scratch: patients with plausible demographics, admissions that follow a real lifecycle, diagnoses and lab tests drawn from clinical vocabularies, and every foreign key resolved. No protected health information is involved because none is used.
The tables Misata generates
patientsDemographics, blood types, coherent age and admission historyadmissionsAdmit and discharge dates ordered, length of stay reconciled, status lifecyclediagnosesDrawn from real diagnosis vocabularies, not filler labelslab_testsTest names, units, and result ranges consistent per paneldoctorsClinical departments and specialties, with admission counts that roll upWhat holds true, every time
- Admit date precedes discharge date on every row, length of stay reconciles
- Diagnoses and lab panels use clinical vocabularies, never lorem ipsum
- A doctor's total admissions equals the count of their admission rows
- No real patient data is sourced, so there is no PHI to protect
Frequently asked
Do I need real healthcare data to generate this?
No. Misata builds the dataset from a specification, not a sample. There is no real healthcare data to source, anonymize, or leak. You describe the tables you need and the engine constructs them with referential integrity and realistic distributions.
Is the generated healthcare data privacy safe?
Yes, by construction. Nothing is learned from real records, so there is no membership to infer and nothing to leak. It runs entirely on your machine with no API key for the core engine.
Can I control the outcomes, like rates and totals?
Yes. Declare a target such as a monthly volume curve or an event rate and Misata produces rows that hit it exactly, while foreign keys stay intact and roll-up columns reconcile after a JOIN.
Choosing a tool? How Misata compares