The Problem
On Monday you tested the 3 prompts in ChatGPT. Awesome! You saw how extraction → validation → questions works. But here's the thing: you can't ask your staff to copy-paste 500 times per day. One nurse spending 2 hours running prompts manually? That's $60/day in labor costs. Multiply that across a busy clinic and you're looking at $18,000/year just on intake admin. Plus the copy-paste errors that lead to incomplete charts.
See It Work
Watch the 3 prompts chain together automatically. This is what you'll build.
Watch It Work
See the AI automation in action
The Code
Three levels: start simple, add reliability, then scale to production. Pick where you are.
When to Level Up
Simple API Calls
- Direct OpenAI/Claude API calls
- Basic retry logic
- Simple JSON validation
- Manual review of all outputs
Add Reliability
- Exponential backoff retries
- Structured logging (Winston/Python logging)
- Rate limiting
- Async processing with queues
- 10% human review sampling
Framework with Orchestration
- Multi-step workflows with LangGraph
- AWS Comprehend Medical for PHI detection
- Conditional routing based on completeness
- EHR/FHIR integration
- HIPAA-compliant logging
- Automated quality checks
Multi-Agent System
- Multiple specialized agents (extraction, validation, triage)
- Load balancing across API providers
- Real-time EHR sync
- Advanced PHI redaction
- Compliance monitoring dashboard
- 24/7 automated processing
Healthcare-Specific Gotchas
Real issues you'll hit. Here's how to handle them.
PHI Redaction Before LLM
Use AWS Comprehend Medical to detect and redact PHI before sending to LLM
import boto3
comprehend = boto3.client('comprehendmedical')
def redact_phi(text: str) -> str:
response = comprehend.detect_phi(Text=text)
entities = response['Entities']
HIPAA Logging Requirements
Log every API call with patient ID, user ID, timestamp, action
import logging
import json
logger = logging.getLogger(__name__)
def log_hipaa_access(patient_id: str, user_id: str, action: str, data: dict):
logger.info('HIPAA_ACCESS', extra={
'patient_id': patient_id,Medical Abbreviation Handling
Pre-process text to expand common abbreviations before extraction
MEDICAL_ABBREVS = {
'SOB': 'shortness of breath',
'CP': 'chest pain',
'HA': 'headache',
'N/V': 'nausea and vomiting',
'HTN': 'hypertension',
'DM': 'diabetes mellitus',
'GERD': 'gastroesophageal reflux disease'EHR Integration (Epic/Cerner FHIR)
Use FHIR API to create/update Patient and Observation resources
from fhir.resources.patient import Patient
from fhir.resources.observation import Observation
import requests
def sync_to_ehr(extracted_data: dict, ehr_base_url: str, auth_token: str):
# Create FHIR Patient resource
patient = Patient(
name=[{"family": extracted_data['patient_name'].split()[-1],Patient Consent Management
Store consent records in database, check before processing
from sqlalchemy import Column, String, Boolean, DateTime
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class PatientConsent(Base):
__tablename__ = 'patient_consents'
Adjust Your Numbers
❌ Manual Process
✅ AI-Automated
You Save
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