AIDEVELOPER TOOLS API MCP READY

Synthea - Create Synthetic FHIR Compliant

Create realistic synthetic patient healthcare data without privacy concerns. Generates FHIR R4 bundles, CSV files, and comprehensive patient records with demographics, conditions, medications, and procedures. Ideal for EHR testing, healthcare development, medical research, and machine learning.

MEDIAN LAGon demand
PARAMETERS9
TOTAL USERS16
MONTHLY ACTIVE7
TOTAL RUNS283
RUNS SUCCEEDED100.0%
RATING5.0 (3)

Input parameters

PARAMETERTYPEREQDEFAULTDESCRIPTION
population_size integer yes 1 Number of synthetic patients to generate.
seed integer no — Random seed for reproducible patient generation.
reference_date string no — Reference date for patient generation in YYYYMMDD format (e.g., '20260101').
clinician_seed integer no — Seed for clinician assignment to patients.
gender enum no — Filter patients by gender.
age_range string no — Filter patients by age range in format 'minAge-maxAge' (e.g., '30-40').
state string no — US state name for patient location (e.g., 'Massachusetts', 'California').
city string no — City name for patient location.
exporter_fhir_use_us_core_ig boolean no False If enabled, generates FHIR bundles using US Core R4 Implementation Guide profiles.

Worked examples

Basic — the required input, with the schema's own example values
{
  "population_size": 1
}

Tasks

Saved runs with the inputs already filled in — each one a standalone page and a working configuration example.

Request a task →

Code

curl

curl -X POST "https://api.apify.com/v2/acts/johnvc~Synthea-Medical-Record-Generator-API/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"population_size": 1}'

Python

from apify_client import ApifyClient

client = ApifyClient("APIFY_TOKEN")
run = client.actor("johnvc/Synthea-Medical-Record-Generator-API").call(
    run_input={'population_size': 1}
)
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)