Closing the Digital Data Flow Gap: AI-Powered Protocol Digitalization with Pluggable EDC Adapters for Automated Study Builds

About this session

Part of Session 5, Track B: Digital Data Flow (Chair: Erin Muhlbradt, NIH/NCI).

Clinical study builds still rely on manual interpretation of protocol PDFs, creating inefficiencies and inconsistencies across study design. We present a multi-agent AI system that transforms protocol PDFs into USDM-compliant, machine-actionable study definitions. Specialized agents handle document parsing, semantic extraction, terminology alignment (NCIt, MedDRA, CDISC CT), and validation. Every extracted element is traceable to its exact source location, ensuring transparency and auditability, while a human review layer maintains quality and control.
Beyond extraction, a pluggable adapter layer generates EDC-ready study builds, leveraging sponsor and therapeutic area-specific form libraries to produce tailored eCRF designs aligned with platform-specific requirements. New EDCs need only a new adapter.
Recognized as a runner-up in the CDISC AI Innovation Challenge, our solution demonstrates a scalable approach to protocol digitalization. We share implementation insights, lessons learned, and key challenges, including interpreting complex study schedules, resolving footnote-driven logic, and handling legacy documents not originally designed for machine consumption.

Speakers

• UA

  Uma Maheswari Adiseshan

  Zifo Technologies

  Speaker

• 

  Sai Sri Sravya Deevi

  Clinical Consultant · Zifo Technologies

  Speaker

• EM

  Erin Muhlbradt

  NIH/NCI

  Chair