Automating Regulatory-Grade Patient Registries with Medical LLMs and Agentic Workflows

Manual clinical data abstraction is the ultimate bottleneck for real-world evidence. While the industry has proven automation works for specific use cases like oncology, the real ROI comes from scaling regulatory-grade accuracy across the entire enterprise. Fragmented tools for different specialties create data silos and skyrocketing costs; the future belongs to a unified, programmable approach. Join us for a technical deep dive into how John Snow Labs’ Patient Journey Intelligence (PJI) Platform is used to architect multiple automated end-to-end registries:

• The Unified Registry Factory: How to build, deploy, and manage multiple automated registries (Cardiology, Neurology, Rare Disease) within a single platform, significantly reducing the time, effort, and costs of fragmented specialty tools.
• Custom Registry Architecture: Defining specialized schemas and terminologies for any therapeutic area using no-code/low-code interfaces.
• Agentic Data Curation: Deploying AI agents for automated case finding, entity resolution, and clinical coding (SNOMED, ICD-10, RxNorm).
• Regulatory-Grade Governance: Implementing a “Trust Stack” architecture to ensure de-identified, harmonized, and “OMOP-ready” datasets.
• Integrated Human-in-the-Loop (HITL): Ensuring data integrity through tightly integrated expert reviews with a full audit trail, versioning, and source-to-concept provenance.
• Multimodal Ingestion: Leveraging Visual LLMs to unlock the 40% of clinical context trapped in images, PDFs, and handwritten documents.

Learn how to bypass the limitations of general-purpose AI and build end-to-end, automated, and governed registry solutions that turns raw data into trustworthy clinical intelligence.