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· Afraz Khan

AI medical scribe notes: a real example (audio to SOAP note)

The fastest way to understand an AI medical scribe is to see what it produces. The technology sounds abstract until you look at a real note. So here is an illustrative, end-to-end example: a snippet of a patient-doctor conversation, and the structured SOAP note an AI scribe generates from it. (The example below is illustrative and contains no real patient data.)

The input: a snippet of conversation

Doctor: What brings you in today? Patient: I’ve had a sore throat for about three days, and since yesterday a bit of a fever. Doctor: Any cough or trouble swallowing? Patient: Some pain swallowing, no real cough. Doctor: Your temperature is 38.1 today and I can see some redness in the throat. Let’s do a rapid strep test and I’ll start you on rest and fluids.

The output: a structured SOAP note

Subjective: Patient reports a 3-day history of sore throat with pain on swallowing, and subjective fever since the previous day. Denies cough.

Objective: Temperature 38.1 C. Oropharyngeal erythema noted on exam.

Assessment: Acute pharyngitis, rule out streptococcal infection.

Plan: Rapid strep test ordered. Advised rest and fluids. Follow up pending test results.

That structured note, plus the specific fields your EMR requires, is generated automatically from the conversation, ready for the clinician to review and sign.

How the mapping works

Turning that dialogue into that note is a pipeline, not a single call:

  1. Speech-to-text with a clinical model (Deepgram Nova-2 Medical) that handles terminology, accents and background noise.
  2. Clinical entity extraction to pull the symptoms, findings, vitals and instructions out of the transcript.
  3. Field mapping that places each entity into the right SOAP section and into the 10+ mandatory forms your workflow uses.
  4. Confidence scoring, so any uncertain field is flagged for the clinician rather than silently filled.

Why this matters for medical charting

Charting is where clinicians lose hours every day. The value of an AI scribe is not “transcription,” it is structure: mapping a natural conversation to the exact fields a chart requires, with the clinician as the final reviewer. That is the difference between a raw transcript and a note you can actually put in a record.

The human stays in charge

Every field is reviewable, and low-confidence extractions are surfaced for confirmation. The scribe removes the typing, not the clinical judgment. That is the only way this belongs in real care.

EpochC builds AI scribing and clinical documentation systems for healthcare. See our healthcare AI work, or start a project.