Is any of this data real?

No. Every name, diagnosis, and date is randomly generated in your browser from fixed sample pools. Nothing is fetched from a real medical record, so there is no PHI and nothing leaves your device.

Can I use this for HIPAA testing?

Yes. Because the data is fully synthetic and contains no real identifiers, it is safe to use in non-production environments where real PHI is not allowed. Always confirm against your own compliance policy.

What fields are included?

Each record has a patient ID, name, age, sex, a primary diagnosis with ICD-10 code, blood pressure and heart-rate vitals, and a last-visit date. The diagnosis codes are drawn from common real ICD-10 entries for realism.

How are the diagnoses chosen?

Each record is randomly assigned one entry from a fixed list of common conditions paired with its real ICD-10 code, such as Type 2 diabetes (E11.9) or essential hypertension (I10). This keeps the data plausible for demos.

Will the same data regenerate every time?

No. Each click produces a fresh random set. If you need a reproducible dataset, generate once and save the output rather than regenerating.

Sample Healthcare Dataset Generator

This generator produces realistic-looking but entirely fake patient records so you can build and demo health-tech dashboards, EHR prototypes, and analytics tools without touching real protected health information. Everything is created locally in your browser.

How it works

Each record is assembled from fixed sample pools using a simple uniform random pick per field:

A patient ID like P-00042 from a running counter.
A name combined from first-name and surname pools.
An age (1–95) and sex, then plausible vitals: systolic/diastolic blood pressure and heart rate drawn from normal physiological ranges.
A primary diagnosis chosen from a list of common conditions, each paired with its real ICD-10 code so the data passes a glance test.
A last-visit date randomly placed within the past two years.

The output is serialized to either CSV (with a header row) or pretty-printed JSON.

Tips and notes

Use a few hundred rows for dashboard layout work and a few thousand to stress pagination and charting. Because the diagnosis codes are real ICD-10 entries, the dataset is good for demoing code-lookup or grouping features. Save the output if you need a stable dataset — each generation is independently random.