John Snow Labs 37d ago

Manual chart abstraction is the bottleneck for real-world evidence. Specialty-specific point tools fragment the work across cardiology, oncology, neurology, and rare disease teams, each with its own schema, vendor, and review process. The result: duplicated effort, inconsistent data, and registries that take months to stand up and longer to keep current. The John Snow Labs Patient Journey Intelligence (PJI) Platform takes a different approach. Every registry runs on the same platform, with one terminology service, one audit trail, and one governance model, so cross-registry analyses work without reconciliation. Building a new registry is a no-code process that a clinical or data team can run in 2–4 weeks, an order of magnitude faster than custom-built alternatives. This webinar walks through the six steps required to build and run a regulatory-grade patient registry end to end, and shows how PJI automates each one inside a single environment. 1. Define the registry schema and per-field guidelines. Specify entities, value sets, and abstraction rules for any therapeutic area. Map every field to standard terminologies (SNOMED, ICD-10, RxNorm, LOINC) using a no-code interface. 2. Case identification. Run case-finding queries across structured and unstructured data to identify eligible patients against the registry’s inclusion and exclusion criteria. 3. Auto-fill each case. Deploy #aiagents that read clinical notes, PDFs, DICOM, and FHIR resources, extract the registry fields, and populate them with source-to-concept provenance for every value. 4. Review with human-in-the-loop and a regulatory-grade audit trail. Route cases to clinical reviewers in Generative #ai Lab. Every edit, approval, and rejection is logged with reviewer identity, timestamp, and the model version that produced the original value. 5. Version and export. Snapshot each registry release with full lineage. Export to OMOP, FHIR, REDCap, or registry-specific formats; reproduce any prior version on demand. 6. Monitor and maintain. Re-run agents as new notes arrive, flag drift in field accuracy, and update the schema as the registry evolves, without rebuilding the pipeline.