Patient Timeline with Full Provenance and Source Links | Patient Journey Intelligence

John Snow Labs is a healthcare AI company and the industry leader in medical language models, with 150M+ downloads of its open-source libraries and models, 500+ enterprise customers, and 30+ peer-reviewed papers. Its software runs inside your own environment, on-premises or in your private cloud, with no data movement and no external API calls. Patient Journey Intelligence is the John Snow Labs platform for secondary use of clinical data. It ingests multimodal patient data (clinical notes, PDFs, FHIR, CDA, DICOM, and structured EHR records), extracts 400+ medical entities from unstructured text, harmonizes everything to the OMOP Common Data Model, and de-identifies PHI with 99%+ accuracy. Every extracted fact carries a confidence score and a link back to the source document it came from. Learn more about Patient Journey Intelligence: https://www.johnsnowlabs.com/patient-journey-intelligence/ IN THIS VIDEO Every event on a Patient Journey Intelligence Patient Timeline links to the source document and the text span that produced it, so verifying an extracted clinical value takes seconds instead of a chart pull. This walkthrough shows the full verification loop, including how a correction flows back to the workflow that raised it. WHY THIS WORKFLOW MATTERS During evaluation, a clinical data scientist samples 50 extracted stage values and checks each against its pathology report. During production, a registrar disagrees with an extracted recurrence date and needs to see what the model read. During an audit, someone asks where a submitted figure came from. All three are the same question: show me the source. A structured value with no path back to the document it came from cannot be defended to an IRB, a registry auditor, or a skeptical oncologist, and a number that cannot be defended does not get used. WHAT THE VIDEO SHOWS - Opening a patient from an active workflow - Reviewing the patient timeline and its clinical events - Locating the relevant clinical event - Opening the supporting source documents - Comparing the displayed value against the source evidence - Correcting or escalating a data-quality issue - Returning to the original workflow and confirming the update carried through WHO THIS IS FOR Anyone who has to defend an extracted value: - Data quality analysts and curation reviewers - Clinical data scientists running acceptance testing during a pilot or evaluation - Registrars and abstractors validating a field before submission - AI governance and compliance teams documenting how output was checked - Clinicians and subject matter experts reviewing a sample before the data is used - Analysts about to publish a cohort count someone will question The trigger differs, the check is identical: open the value, open the document behind it, compare. QUESTIONS THIS VIDEO ANSWERS - How do I trace an extracted clinical value back to its source document? - How do I validate AI-extracted clinical data during a pilot evaluation? - How do I correct or escalate a data quality issue in a patient record? LINKS Tutorial: https://www.johnsnowlabs.com/pji/tutorials/scenarios/patient-timeline-to-source-document-review Patient Journey Intelligence: https://www.johnsnowlabs.com/patient-journey-intelligence/ All patient data shown is synthetically generated for illustration. #PatientTimeline #ClinicalEvidence #DataQuality