The Problem
On Monday you tested the 3 prompts in ChatGPT. You saw how data extraction → risk analysis → success prediction works. But here's the reality: your research team can't manually copy-paste data for 200 trials. One analyst spending 3 hours per trial running prompts? That's $450/trial in labor costs. Multiply that across a busy pharma company and you're looking at $1.2M/year just on trial analysis. Plus the human error rate that leads to missed red flags and failed trials.
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
0-50 trials/month
- Direct OpenAI/Claude API calls
- Sequential processing (one at a time)
- Basic error handling
- Manual result review
- Cost: ~$0.15 per trial
Add Reliability Layer
50-500 trials/month
- Retry logic with exponential backoff
- Database storage (PostgreSQL)
- Structured logging
- Input validation (Zod schemas)
- Batch processing (10 concurrent)
- Cost: ~$0.12 per trial (volume discount)
Queue-Based System
500-2000 trials/month
- Redis queue with priorities
- Async processing (50+ concurrent)
- Worker pool management
- Real-time status tracking
- Automatic retries and dead letter queue
- Performance monitoring
- Cost: ~$0.10 per trial
Multi-Agent System
2000+ trials/month
- Specialized agents (extraction, risk, prediction)
- Load balancing across multiple LLM providers
- Caching for similar trials
- Real-time analytics dashboard
- API for integration
- SLA monitoring and alerting
- Cost: ~$0.08 per trial (bulk rates)
Healthcare Tech-Specific Challenges
Real issues you'll hit. Here's how to handle them.
FDA Regulatory Compliance
Log every API call with timestamps, model versions, and confidence scores. Store raw inputs/outputs.
# FDA-compliant logging
import hashlib
import json
from datetime import datetime
def log_fda_audit_trail(trial_id: str, step: str, input_data: dict, output_data: dict, model: str):
"""Create immutable audit log entry"""
audit_entry = {Multi-Site Data Inconsistencies
Normalize data before extraction. Use standardized medical ontologies (SNOMED CT, MedDRA).
# Data normalization layer
import re
from datetime import datetime
class TrialDataNormalizer:
"""Standardize trial data before AI processing"""
# Standard adverse event terms (MedDRA)Handling Missing or Incomplete Data
Build confidence scores into extraction. Flag low-confidence fields for human review.
# Confidence-aware extraction
from typing import Dict, Any, Tuple
def extract_with_confidence(trial_text: str) -> Tuple[Dict, float]:
"""Extract data and calculate field-level confidence"""
# Required fields for complete analysis
REQUIRED_FIELDS = [Real-Time vs Batch Processing Trade-offs
Use priority queues. Route urgent trials to fast lane (higher cost), routine to batch (lower cost).
# Priority-based routing
import asyncio
from enum import Enum
from dataclasses import dataclass
from typing import Optional
class TrialPriority(Enum):
URGENT = 1 # Safety concerns, FDA inquiryModel Drift and Version Control
Lock model versions. Test new versions in parallel before switching. Maintain version history.
# Model version management
from enum import Enum
from typing import Dict, Optional
import hashlib
class ModelVersion(Enum):
GPT4_0613 = 'gpt-4-0613' # June 2023 snapshot
GPT4_1106 = 'gpt-4-1106-preview' # Nov 2023 previewAdjust Your Numbers
❌ Manual Process
✅ AI-Automated
You Save
2026 Randeep Bhatia. All Rights Reserved.
No part of this content may be reproduced, distributed, or transmitted in any form without prior written permission.