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Case StudyDigital Health · AI-Powered Clinical Care
Telehealth Technology Platform (Confidential)

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.

3 min readLive in production
Primary Impact
70%
Less Time on Documentation
70%
Less Time on Documentation
Per-session documentation time cut by generating structured notes automatically
Real-time
Session Transcription
Medical-vocabulary-accurate transcription as the session happens
Minutes
Note Turnaround
Structured SOAP notes generated within minutes of session end
100%
Clinician-Reviewed
Every AI-drafted note is reviewed and approved by a clinician before it's final

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.