Our project is an adversarial auditor that verifies clinical notes against two independent sources of truth: the actual visit transcript and the patient's structured FHIR chart. It flags any claim that is not supported by either source and sends disputed claims to a panel of independent AI judges that surface disagreement instead of averaging it away. The result is a scored, evidence-cited safety report for every note. This matters because ambient scribing tools can automatically turn spoken conversations into permanent medical records, often without anyone checking the note word for word against what was actually said. A fabricated detail, a missed disclosure, or an overstated finding can then remain in a patient's chart without anyone noticing. When we tested our system on real, unmodified encounters, we found genuine errors that had gone unflagged, including a physical exam finding documented as performed when only imaging was discussed, a patient's age listed one year off from her actual birth date, a lab value labeled as "fasting" even though that word never appeared in the conversation, and a fabricated demographic detail with no supporting source. These examples showed us that an independent, evidence-based second pass can catch the kind of silent documentation errors that can put patient safety at risk before they ever become part of the medical record.
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