@gamechangerai: A healthcare consulting client wanted software that listens to a patient visit and produces the note plus the procedure and diagnosis codes that justify it. Transcription is the easy half. The note has to support the code, because an incorrectly filled claim means lost revenue, penalties, and in that market a lawsuit. So I extracted entities rather than a transcript. The system pulls specific fields out of the recording. ⠀ Patient metadata such as height, weight and date of birth, then diagnoses, allergies and individual symptoms, then the procedure and billing codes. Each entity can be validated, corrected in a form field, and exported into whatever billing system the practice already runs. A wall of prose gives you none of that. The other move was shrinking the code space before searching it. The practitioner picks a specialty in settings and the software then only considers codes that apply to that treatment type. ⠀ Narrowing the candidate set was cheaper and steadier than making the model bigger. Compliance shaped the rest, because audio of a patient visit is regulated data. When my part finished the system was in real world testing with independent practitioners and nursing services, and accuracy was still being tuned. Judge a pilot like this on claim rejection rate, not word accuracy. What metric would you hold it to? ⠀ Follow @gamechangerai for more. ⠀ #ai #machinelearning #healthtech #digitalhealth #nlp

Rahul Kumar

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Region: PT

Wednesday 23 September 2026 07:00:22 GMT