Healthcare Automation Platform

Context

A healthcare organization managing multiple practices needed to automate repetitive tasks that consumed 4+ hours of staff time daily. The system integrates with existing EHR systems and handles real patient data with strict compliance requirements.

Problem

Without this system:

• Staff manually entered data across disconnected systems
• Compliance reports took days to generate
• Human errors in data entry caused billing issues
• No audit trail for data changes

The non-negotiables:

• HIPAA compliance — all data encrypted, access logged
• Zero data loss — every record must be traceable
• 24/7 operation — failures must self-recover

Architecture

Task queue architecture with Celery workers processing jobs asynchronously:

• Django — web dashboard and REST API
• Celery + Redis — distributed task queue
• PostgreSQL — primary data store with audit logging
• Playwright — browser automation for legacy EHR systems

Each automation type runs in isolation. A failure in one workflow doesn't cascade to others.

Key Design Decisions

Idempotency First

Every task is designed to be safely retryable:

@celery_app.task(bind=True, max_retries=3)
def sync_patient_data(self, patient_id: str, request_id: str):
    # Check if already processed
    if ProcessedRecord.objects.filter(request_id=request_id).exists():
        return {"status": "already_processed"}
    
    # Process with transaction to ensure atomicity
    with transaction.atomic():
        result = do_sync(patient_id)
        ProcessedRecord.objects.create(request_id=request_id)
    
    return result

Conservative Error Handling

When uncertain, fail loudly rather than proceeding with potentially incorrect data:

def process_record(record):
    if not validate_required_fields(record):
        raise ValidationError("Missing required fields")
    
    # Never assume. Verify at each step.
    if not verify_patient_exists(record.patient_id):
        raise DataIntegrityError("Patient not found")

Screenshot-Based Debugging

Every browser automation action captures a screenshot. When failures occur, engineers can visually trace exactly what the automation saw.

Failure Modes Handled

Failure Mode	Handling
Network timeout	Exponential backoff retry (3 attempts)
EHR system down	Queue task for later, alert on-call
Invalid data	Reject record, log to review queue
Partial completion	Transaction rollback, full retry

Outcome

• Reduced manual data entry by 70%
• Zero compliance violations since deployment
• Processed 50,000+ records with 99.9% uptime
• Staff time reallocated to patient care