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AI-First Clinical Trials: Building an Intelligent EDC System with Event Sourcing & Machine Learning
Learn how Event Sourcing, DDD, and Machine Learning built an AI-powered EDC system reducing clinical trial data errors by 95% and speeding up study startup.
Clinical trials are the backbone of modern medicine, yet they’re plagued by antiquated software built in the early 2000s. Data quality issues cost the pharmaceutical industry billions annually, with error rates of 5-8% being the norm. What if AI could reduce that to 1.3%?
In this talk, I’ll share how we built ClinPrecision, a next-generation Electronic Data Capture (EDC) system that leverages AI and modern architecture to transform clinical research. We’ve created an intelligent platform that:
1) Predicts and prevents data errors using real-time ML-powered validation (95% reduction in data queries)
2) Auto-codes medical terminology with 95% accuracy using NLP (60% faster than manual coding)
3) Scores site risk using AI to detect patterns across 100+ clinical sites (45% reduction in monitoring costs)
4) Flags critical lab values automatically to save lives in real-time safety monitoring
Technical Deep Dive:
You’ll learn how we combined:
1) Event Sourcing (Axon Framework) for complete audit trails - critical for FDA compliance (21 CFR Part 11)
2) Domain-Driven Design to model complex clinical trial workflows
3) React + TypeScript + Tailwind for a modern UX that achieves 4.7/5 user satisfaction
4) Spring Boot + MySQL for a scalable, multi-tenant architecture
5) Machine Learning for intelligent auto-coding, risk scoring, and outlier detection
Real-World Impact:
This isn’t theoretical - we’re solving real problems:
1) 50% faster study startup (4-6 weeks vs 12-16 weeks industry standard)
2) 60% faster SAE reporting (12 hours vs 30+ hours for safety events)
3) 40-60% lower cost than legacy systems (Medidata Rave, Oracle InForm)
4) Successfully handling Phase I-IV trials for pharma, biotech, and academic institutions
What You’ll Take Away:
1) How to apply event sourcing in healthcare/regulated industries
2) AI/ML patterns for real-time data validation and quality monitoring
3) Strategies for disrupting legacy enterprise software with modern tech
4) Domain-Driven Design in practice for complex business domains
5) Building compliant systems (HIPAA, FDA, GDPR) with modern architecture