Writing Itself: AI-Powered Clinical Documentation for Telehealth
Clinicians didn't sign up to be typists. See how generative AI turns every telehealth session into a structured, accurate clinical note — automatically, in minutes, and always with a clinician's final sign-off.
The Challenge
“Clinicians were spending 30–40% of their time on documentation instead of patient care. Session notes were often incomplete, delayed, or inconsistent — and free-text formats made it difficult to search prior diagnoses, medications, or patterns across visits. Every delay in documentation meant a delay in referral letters and patient records reaching the people who needed them. And any AI system stepping into this workflow had to be clinically precise enough for a physician to trust and sign off on — there was no room for hallucinated or vague clinical language.”
The Solution
Every telehealth session is now automatically transcribed, analyzed, and summarized using a purpose-built AI pipeline — turning a recorded conversation into a structured, reviewable clinical note within minutes, with the clinician always making the final call.
Implementation
Capture: Real-Time Medical Transcription
Amazon Transcribe Medical converts live session audio into text in real time, tuned for medical vocabulary so drug names, procedures, and clinical terminology are captured accurately as the conversation happens — no post-session re-listening required.
Extract: Structuring the Clinical Signal
Amazon Comprehend Medical processes the transcript to extract clinical entities — diagnoses mapped to ICD-10, medications mapped to RxNorm, procedures, anatomical references, and test results — turning free-flowing conversation into structured, machine-readable clinical data.
Generate & Review: From Draft to Approved Note in Minutes
Amazon Bedrock (Claude) drafts a structured SOAP note — Subjective, Objective, Assessment, Plan — within minutes of the session ending. The clinician reviews and approves the note rather than writing from scratch, and the finished, entity-tagged transcript is added to a fully searchable clinical archive.
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