Regulatory-Grade Clinical Data Abstraction on Databricks: No Code, Private AI, Built to Scale

From the PJI interface, you connect your clinical data sources, ingest documents, build a patient cohort, select the registry or ontology to extract, and launch a data curation job. No coding. No manually built pipelines. PJI's agentic AI translates your target data model into a clinical information extraction workflow and runs it at scale. Small, deterministic Healthcare NLP models do the high-volume work: identifying clinical signals, filtering low-value content, and ranking the most informative evidence in each longitudinal patient record.

This session includes a live demo and a benchmark of this architecture against the brute-force approach on the same abstraction task and source dataset, comparing extraction accuracy, total processing time, cost per patient, and traceability of supporting evidence, including what happens as the patient record grows from tens to thousands of pages. You'll leave knowing how to move from raw clinical documents to structured, evidence-backed, auditable data in a few clicks, combining healthcare-specific NLP, private Medical LLM inference, and complete governance in one workflow.