Misata: Outcome-Conformant Synthetic Data Generation

Muhammed Rasin

Synthetic Data for Healthcare

Describe the practice, for example a dental clinic in Marseille with 3,000 patients and a 7% claim denial rate, and Misata Studio builds patients, providers, encounters, diagnoses with real ICD-10-CM codes, prescriptions, lab results and insurance claims. The pieces agree with each other: a pregnancy code never lands on a male patient, a prescription follows its diagnosis, a lab result carries the unit its test uses, and your denial rate is exact. No real person is involved.

Updated 2026-09-24.

You would write

A dental clinic in Marseille with 3,000 patients and a 7% insurance claim denial rate.

Studio builds this on one of its tested archetypes: the mechanics below are fixed and verified, and what the business sells, the names and the wording are written for yours.

Start from this

What Studio builds

patientsNames, addresses and phone numbers that fit the country

providersClinicians with specialties

encountersVisits and stays; length of stay follows the encounter type

diagnosesReal ICD-10-CM codes and descriptions, consistent with the patient's sex

prescriptions and lab_resultsDrugs that follow the diagnosis category; lab units fixed by test

claimsSubmitted, paid or denied; the allowed amount follows the payer

What holds, and how you know

These are checked on the finished data, not assumed. Each dataset comes with a certificate listing what you asked for and whether it was met. How we verify.

Diagnoses use real ICD-10-CM codes and never contradict the patient's sex
Prescriptions follow the diagnosis category and lab results use the right unit
A claim denial rate you state is exact
No real patient data is used, so there is no PHI in the output
Every foreign key points at a row that exists, checked on the finished data
Every count, total, rate, share and date window you state is applied exactly, or listed as not applied
A certificate lists each requirement and whether the data meets it

Free sample dataset

A ready-made hospital encounters and claims dataset to download.

See the sample

Have a schema, specialised rules or a large volume and would rather hand it off? Ask us to build it for you.

Frequently asked

Do I need real healthcare data to generate this?

No. Misata Studio builds the dataset from a description, a schema you draw, or a structure you import (SQL DDL, DBML or a CSV header). No real records are uploaded, copied or learned from, so there is nothing to anonymise.

Is the generated healthcare data privacy safe?

Yes. Nothing is learned from real records, so no real person, customer or account can appear in the output. A model writes names, places and wording, and Studio may research public facts on the web (you can turn research off). It never asks for your data.

Can I control the numbers, like rates and totals?

Yes. State a total, a monthly curve, a share or a rate in plain words, or draw the curve, and Studio applies it exactly. The certificate lists each figure and whether it was met. If a figure cannot be met, Studio says so instead of changing it.

Can I use my own schema?

Yes. Paste SQL DDL or DBML, import a CSV structure, or draw the tables on the canvas. A schema you give is followed exactly, and any difference is reported.

Which formats can I export?

CSV, Excel, Parquet, JSON Lines, SQL for Postgres, MySQL, SQL Server, Oracle, BigQuery and Snowflake, SQLite, DuckDB, dbt seeds, Prisma, DBML, TypeScript, JSON Schema, a data dictionary and more.

Can I get documentation for a reviewer?

Yes. The data dictionary and datacard exports list every column, and the certificate shows what was checked.

Choosing a tool? How Misata compares

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