From Clinical Documents to Automated Patient Registries

Patient registries require far more than extracting fields from clinical documents. A cancer registry involves inferring hundreds of data elements governed by thousands of pages of evolving specifications. The information needed to populate those fields is scattered across pathology reports, radiology findings, clinical notes, genomic tests, and structured EHR records — often containing incomplete, conflicting, or outdated evidence.
How can healthcare organizations automate this complex clinical reasoning while maintaining the accuracy, traceability, and human oversight required for regulatory reporting?
In this webinar, we demonstrate how John Snow Labs’ Patient Journey Intelligence (PJI) combines specialized Medical Language Models, agentic clinical reasoning, and a continuously enriched OMOP data foundation to build and maintain multiple patient registries without coding.
Rather than treating every document or registry as an isolated abstraction task, PJI constructs longitudinal patient journeys, reconciles clinical evidence across encounters, and applies registry-specific guidelines to produce standardized, evidence-grounded records. These can then be reviewed by domain experts – including versioning, explainability, and a full audit trail – and then exported to made available to AI agents.
What you’ll see:

We will walk through the journey from fragmented, multimodal, raw clinical documentation to validated registry records – showing how AI agents, models, rules, and expert oversight work together within a privately deployed environment.
This webinar is intended for registry managers, oncology data specialists, clinical informatics teams, healthcare data scientists, and research leaders looking to automate complex registry abstraction and reuse clinical intelligence across multiple programs.