synthea-healthcare-data

Overview

Synthea is MITRE's open-source synthetic-patient population simulator that generates realistic but fictional medical records across the full patient lifecycle (birth through death). The output is structurally valid FHIR / C-CDA / CSV that downstream health IT systems consume without exposing any real patient data.

Source: github.com/synthetichealth/synthea.

Use this when:

• Building a FHIR-server test environment that needs realistic patient demographics + conditions + encounters + observations + medications.
• Stress-testing an EHR import / export pipeline.
• Demoing a health-tech product without HIPAA exposure on a real dataset.

For non-health-domain synthetic data use faker-synthetic-data. For the categories of PHI that Synthea avoids exposing see pii-categories-reference (HIPAA Safe Harbor 18 identifiers).

When to use

• Testing FHIR server compliance (R4 / STU3 / DSTU2 conformance).
• Populating an EHR-like development environment.
• Generating claims / EDI data for revenue-cycle testing (Synthea's CPCDS output).
• Property-based testing of risk-adjustment / HCC-coding logic.

How to use

1. Clone Synthea and build it with Gradle (JDK 17+); confirm ./gradlew build check test passes.
2. Set the output formats you need in src/main/resources/synthea.properties (FHIR R4, CSV, C-CDA, CPCDS).
3. Run ./run_synthea with a pinned seed (-s), a population size (-p), and the target US state so the run is reproducible.
4. Inspect the output under ./output/fhir/ or ./output/csv/ and confirm the expected resources / schemas are present.
5. Load the records into your test FHIR server (per-file POST, or the *.ndjson bulk files for a bulk-data endpoint).
6. Wire generation into CI with a pinned seed and pinned Synthea tag (references/ci-integration.md).
7. Run your integration tests against the loaded synthetic population.

Authoring

Install + build

Per github.com/synthetichealth/synthea:

git clone https://github.com/synthetichealth/synthea.git
cd synthea
./gradlew build check test

Requires Java JDK 17 or newer (LTS versions recommended per the README).

Generate a population

Basic invocation:

./run_synthea

With explicit population size and state:

./run_synthea -p 1000 Massachusetts

Per the README: -p sets population size; the trailing argument sets the US state (locale-aware demographics and provider networks).

Common run flags

Output formats

Per the README the system emits:

• HL7 FHIR R4 (default), STU3, DSTU2
• Bulk FHIR (ndjson, suitable for $export endpoints)
• C-CDA (Consolidated Clinical Document Architecture)
• CSV (flat-file format)
• CPCDS (Common Payer Consumer Data Set, for claims testing)

Output destination: ./output/fhir/, ./output/csv/, etc.

Configure formats in src/main/resources/synthea.properties:

exporter.fhir.export = true
exporter.fhir_stu3.export = false
exporter.ccda.export = false
exporter.csv.export = true

Running

Pre-generated population

For quick starts MITRE distributes pre-generated SyntheaMass populations (1M patient Massachusetts simulation, etc.) on the project site - search for "Synthea downloadable populations" if you don't need to regenerate.

Disease modules

Synthea uses a Modular Rule System (per README) where each disease / condition is a JSON-defined state machine in src/main/resources/modules/. Examples include diabetes, hypertension, COPD, opioid addiction, COVID-19, sepsis, and dozens more. The module drives the patient's clinical journey probabilistically.

To add a custom module, drop a JSON spec into the modules directory; the engine picks it up on next run.

Loading into a FHIR server

# After ./run_synthea generates output/fhir/*.json
for f in output/fhir/*.json; do
  curl -X POST -H "Content-Type: application/fhir+json" \
       -d @"$f" http://localhost:8080/fhir/
done

For bulk-FHIR ingestion, use the output/fhir/*.ndjson files with your server's bulk-data endpoint.

Parsing results

CSV outputs have predictable schemas:

output/csv/
  patients.csv      - patient_id, birthdate, deathdate, ssn, drivers, ...
  encounters.csv    - encounter_id, patient, organization, ...
  conditions.csv    - start, stop, patient, encounter, code, description
  medications.csv   - start, stop, patient, code, description, ...
  observations.csv  - date, patient, encounter, code, value, units
  procedures.csv
  immunizations.csv
  allergies.csv
  imaging_studies.csv
  careplans.csv
  claims.csv

The patients.csv ssn column contains fake SSNs in Synthea's reserved test range - they look real-formatted but don't correspond to issued SSAs. This is the intended HIPAA-safe replacement.

For FHIR output, parse with any standard FHIR client (HAPI FHIR Java, fhir.resources for Python, etc.).

CI integration

For health IT projects, regenerate Synthea data on every PR with a pinned seed so the dataset is reproducible. The full GitHub Actions job (build, generate, load into a local HAPI FHIR server, run integration tests) plus the pin-to-a-tag note is in references/ci-integration.md.

Worked example

Generate 100 diabetic patients aged 40-75 in Massachusetts, then verify the output before loading it into a test FHIR server:

./run_synthea -p 100 -s 42 -a 40-75 Massachusetts \
  -m diabetes

-m <module> filters to runs that include the named module; -s 42 pins the seed so the same 100 patients regenerate every time. Output appears in ./output/ (fhir/, csv/, c-cda/ per synthea.properties).

Confirm the run: output/csv/conditions.csv should carry diabetes condition rows linked by patient to output/csv/patients.csv, and every patient's birthdate should place them in the 40-75 age band. Once verified, POST the output/fhir/*.json bundles into the test FHIR server as shown under "Loading into a FHIR server".

Anti-patterns

Limitations

• Slow at large scale. A 1M-patient run on a laptop takes hours. For interactive testing keep -p ≤ 10 000.
• US-centric. Modules and demographics are calibrated for US populations; international healthcare contexts need module adaptation.
• Module library is opinionated. Default modules reflect MITRE / public-health authoring choices; custom modules may be needed for niche specialties.
• No claims-data realism guarantee. CPCDS output is structurally valid; claims edge cases (denials, adjustments, coordination of benefits) are simulated but may not match every payer's business rules.
• No PHI guarantee against re-identification of generators. The synthetic population is fictional, but the modules (which conditions get simulated, prevalence rates) are derived from public health data. The output won't re-identify any individual.

References

• Synthea GitHub - github.com/synthetichealth/synthea.
• MITRE Synthea project site - synthea.mitre.org (the canonical project home; documentation, downloads, community).
• HIPAA Safe Harbor (45 CFR § 164.514(b)(2)) - the de-identification standard Synthea output is engineered against. See pii-categories-reference.
• Sibling generator (generic): faker-synthetic-data.
• Composes with: pii-masking-pipeline-builder.
• Reference file: references/ci-integration.md (CI job + tag pinning).