agdar/DATA_GENERATION_GUIDE.md

Saudi-Influenced Test Data Generation Guide

Overview

This project includes a comprehensive management command to generate realistic Saudi-influenced test data for all applications in the AgdarCentre healthcare platform.

Features

The generate_test_data command creates:

Saudi Cultural Context

• Arabic Names: Both English transliteration and Arabic script

  • Male names: محمد (Mohammed), عبدالله (Abdullah), فهد (Fahad), etc.
  • Female names: نورة (Noura), فاطمة (Fatima), سارة (Sarah), etc.
  • Family names: العتيبي (Al-Otaibi), الغامدي (Al-Ghamdi), etc.

• Saudi Phone Numbers: Proper format with Saudi mobile prefixes

  • Format: +966 5X XXX XXXX
  • Valid prefixes: 50, 53, 54, 55, 56, 57, 58, 59

• National IDs: 10-digit Saudi national ID format

  • Format: 1XXXXXXXXX (Saudi) or 2XXXXXXXXX (Resident)

• Addresses: Saudi cities and districts

  • Cities: Riyadh, Jeddah, Mecca, Medina, Dammam, etc.
  • Riyadh districts: Al-Olaya, Al-Malaz, Al-Naseem, etc.

• Currency: All financial data in SAR (Saudi Riyals)

• Work Schedule: Saudi work week (Sunday-Thursday)

  • Morning shift: 08:00-12:00
  • Afternoon shift: 15:00-19:00 (after prayer break)

• Insurance: Saudi insurance companies

  • Bupa Arabia, Tawuniya, Medgulf, Malath, etc.

Generated Data

The command generates data for all apps:

1. Core App

   • Tenants (healthcare organizations)
   • Users (with various roles: doctors, nurses, therapists, admin, etc.)
   • Patients (with Saudi demographics)
   • Clinics/Departments
   • Files and SubFiles
   • Notification Preferences

2. Appointments App

   • Providers
   • Rooms
   • Schedules (Sunday-Thursday)
   • Appointments (with varied statuses)
   • Appointment Reminders
   • Appointment Confirmations

3. Finance App

   • Services (billable services)
   • Packages (session bundles)
   • Payers (insurance companies)
   • Invoices (with VAT)
   • Payments
   • Package Purchases

4. Clinical Apps

   • Medical Consultations
   • Nursing Encounters (with vital signs)
   • ABA Consultations
   • OT Sessions
   • SLP Interventions

5. Notifications App

   • Message Templates (bilingual)
   • Messages (SMS/WhatsApp)

6. Referrals App

   • Internal and external referrals

Usage

Basic Usage

python manage.py generate_test_data

This will create:

• 1 tenant
• 50 patients per tenant
• 100 appointments per tenant
• Associated clinical, financial, and communication records

Command Options

python manage.py generate_test_data [OPTIONS]

Options:

• --tenants N: Number of tenants to create (default: 1)

  python manage.py generate_test_data --tenants 2
  

• --patients N: Number of patients per tenant (default: 50)

  python manage.py generate_test_data --patients 100
  

• --appointments N: Number of appointments per tenant (default: 100)

  python manage.py generate_test_data --appointments 200
  

• --clear: Clear existing data before generating new data

  python manage.py generate_test_data --clear
  

  ⚠️ Warning: This will delete all existing data except superuser accounts!

Examples

Generate data for a single tenant with default settings:

python manage.py generate_test_data

Generate data for multiple tenants:

python manage.py generate_test_data --tenants 3 --patients 30 --appointments 80

Clear existing data and generate fresh data:

python manage.py generate_test_data --clear --patients 100 --appointments 200

Generate large dataset for testing:

python manage.py generate_test_data --patients 200 --appointments 500

Data Characteristics

Patient Demographics

• Age Distribution: Weighted towards children (therapy center context)
  • 60% children (2-12 years)
  • 25% teenagers (13-18 years)
  • 15% adults (19-60 years)

Appointment Distribution

• Time Range: Past 3 months + next month
• Status Distribution:
  • Past appointments: 75% completed, 15% no-show, 10% cancelled
  • Today's appointments: Mix of confirmed, arrived, in-progress
  • Future appointments: 70% confirmed, 30% booked
• Scheduling: Excludes Saudi weekends (Friday & Saturday)

Financial Data

• Services: 5 service types per clinic
• Pricing: Realistic Saudi healthcare pricing (200-400 SAR)
• VAT: 15% tax applied to all invoices
• Insurance: Mix of self-pay and insured patients

Clinical Records

• Generated for completed appointments only
• Includes realistic vital signs and measurements
• Age-appropriate clinical data

Dependencies

The command requires the following Python packages:

• django - Django framework
• faker - For generating fake data
• phonenumbers / django-phonenumber-field - For phone number handling

These should already be installed in your project environment.

Output

The command provides detailed progress output:

Starting Saudi-influenced test data generation...

Generating data for tenant: Agdar Rehabilitation Center
  Created 18 users
  Created 5 clinics
  Created 50 patients
  Created 15 providers
  Created 14 rooms
  Created 150 schedules
  Created 100 appointments
  Created clinical records
  Created 25 services
  Created 5 packages
  Created financial records
  Created communication records
  Created integration records

============================================================
DATA GENERATION SUMMARY
============================================================
  Appointments: 100
  Clinics: 5
  Patients: 50
  Providers: 15
  Rooms: 14
  Schedules: 150
  Services: 25
  Tenants: 1
  Users: 18
============================================================

✓ Test data generation completed successfully!

Notes

• All generated data is realistic and follows Saudi cultural norms
• Patient names are bilingual (English and Arabic)
• Phone numbers follow Saudi mobile format
• Addresses use real Saudi cities and districts
• Work schedules respect Saudi work week and prayer times
• Financial data uses SAR currency with proper VAT
• Clinical data is age-appropriate and realistic

Troubleshooting

Issue: Command not found

python manage.py generate_test_data
# Error: No module named 'core.management.commands.generate_test_data'

Solution: Ensure the management command directory structure exists:

core/
  management/
    __init__.py
    commands/
      __init__.py
      generate_test_data.py

Issue: Import errors

# Error: No module named 'faker'

Solution: Install required dependencies:

pip install faker

Issue: Database errors

# Error: UNIQUE constraint failed

Solution: Use the --clear flag to clear existing data first:

python manage.py generate_test_data --clear

Best Practices

1. Development: Use smaller datasets for faster generation

   python manage.py generate_test_data --patients 20 --appointments 40
   

2. Testing: Use moderate datasets

   python manage.py generate_test_data --patients 50 --appointments 100
   

3. Demo: Use larger datasets for realistic demos

   python manage.py generate_test_data --patients 200 --appointments 500
   

4. Fresh Start: Always use --clear when you want to reset data

   python manage.py generate_test_data --clear
   

Support

For issues or questions about the data generation command, please refer to the project documentation or contact the development team.