faker-healthcare-provider

Generate realistic, medically accurate, and correlated healthcare/medical test data in 6 languages: English, Spanish, Portuguese, Chinese, French, and German.

This provider generates correlated clinical data based on the WHO ICD-10 and ICD-10-CM (CDC/NCHS) classifications, ensuring that symptoms, medications, specialties, and diagnostic codes match the generated disease.

Installation

pip install faker-healthcare-provider

Quick Start

from faker import Faker
from faker_healthcare import HealthcareProvider

# English (default)
fake = Faker()
fake.add_provider(HealthcareProvider)

# Generate a complete patient scenario
scenario = fake.patient_scenario()
print(scenario)
# {
#   'disease': 'Type 2 Diabetes',
#   'icd10': 'E11.9',
#   'symptoms': ['Fatigue', 'Blurred Vision', 'Frequent Urination'],
#   'medications': ['Metformin', 'Insulin Glargine'],
#   'specialty': 'Endocrinology'
# }

# Or generate individual data
fake.disease()            # 'Essential Hypertension'
fake.diagnosis()          # 'Type 2 Diabetes (E11.9)'
fake.medical_specialty()  # 'Cardiology'

# Use a different language (Spanish, Portuguese, Chinese, French, German)
fake_es = Faker('es_ES')
fake_es.add_provider(HealthcareProvider)
fake_es.disease()         # 'Diabetes Tipo 2'
fake_es.diagnosis()       # 'Diabetes Tipo 2 (E11.9)'

💡 Tip: Run python showcase.py to see all available features and examples!

Supported Locales

• 🇺🇸 English (en_US) - Default
• 🇪🇸 Spanish (es_ES)
• 🇧🇷 Portuguese (pt_BR - Brazil)
• 🇨🇳 Chinese (zh_CN - Simplified)
• 🇫🇷 French (fr_FR)
• 🇩🇪 German (de_DE)

Usage

Basic Usage (English)

from faker import Faker
from faker_healthcare import HealthcareProvider

fake = Faker()
fake.add_provider(HealthcareProvider)

fake.diagnosis()          # 'Type 2 Diabetes (E11.9)'
fake.disease()            # 'Essential Hypertension'
fake.icd10_code()         # 'I10'
fake.generic_drug()       # 'Metformin'
fake.medical_specialty()  # 'Cardiology'
fake.blood_type()         # 'O+'

Multi-Language Support

from faker import Faker
from faker_healthcare import HealthcareProvider

# Spanish
fake_es = Faker('es_ES')
fake_es.add_provider(HealthcareProvider)
fake_es.disease()  # 'Diabetes Tipo 2'

# Portuguese (Brazil)
fake_pt = Faker('pt_BR')
fake_pt.add_provider(HealthcareProvider)
fake_pt.disease()  # 'Diabetes Tipo 2'

# Chinese (Simplified)
fake_zh = Faker('zh_CN')
fake_zh.add_provider(HealthcareProvider)
fake_zh.disease()  # '2型糖尿病'

# French
fake_fr = Faker('fr_FR')
fake_fr.add_provider(HealthcareProvider)
fake_fr.disease()  # 'Diabète de Type 2'

# German
fake_de = Faker('de_DE')
fake_de.add_provider(HealthcareProvider)
fake_de.disease()  # 'Typ-2-Diabetes'

Available Methods

Locale-Specific Features

Each locale includes:

• Translated medical terminology (diseases, symptoms, procedures)
• Locale-specific insurance systems:
  • 🇺🇸 US: Medicare, Medicaid, PPO, HMO, TRICARE, ACA Marketplace
  • 🇪🇸 Spain: Seguridad Social, Seguro Privado (Cuadro Médico / Reembolso), Mutualidades (MUFACE/ISFAS/MUGEJU)
  • 🇧🇷 Brazil: Plano Individual, Familiar, Empresarial, com/sem Coparticipação
  • 🇨🇳 China: 商业保险, 雇主赞助保险, 牙科保险
  • 🇫🇷 France: Assurance Maladie, Mutuelle, Complémentaire santé
  • 🇩🇪 Germany: GKV (AOK, Barmer, TK, …), PKV, Zusatzversicherungen

Universal data (same across all languages):

• ICD-10 codes (international standard)
• Blood types (universal notation)

Disclaimer

All data generated by this provider is synthetic test data for development and testing only, and must not be used for medical diagnosis, treatment, or any clinical or healthcare decision. The combinations produced are random.

• Diagnoses & ICD-10 codes are drawn from real classifications so records look realistic. Granular codes are ICD-10-CM (produced by CDC/NCHS and distributed free by the U.S. government); base codes are WHO ICD-10, © World Health Organization, used under CC BY-ND 3.0 IGO — reproduced verbatim, with attribution.
• Generic drug names are real International Nonproprietary Names (INN), which WHO formally places in the public domain.
• Brand drug names are entirely fictitious — generated from invented morphemes, deliberately avoiding INN stems. They are not real trademarks, and any resemblance to a real product is coincidental.

License

MIT