case study
Automating medical transcription at scale with the right LLM
An hourly speech-to-text service with speaker diarization and fault tolerance, built for a secure, multi-cloud healthcare environment.
A manual quality-management process couldn't scale to call volume - reviewing recordings by hand was slow, costly, and a bottleneck.
About
- Client: Healthcare RCM
- Industry: Healthcare
- Location: USA
- Year: 2026*
Scope of work
- AI / LLM consulting
- Speech-to-text transcription
- System architecture & integration
Tech stack
- OpenAI Whisper
- Azure Batch
- Azure AI
- AWS Lambda
- Step Functions
- S3 (BYOS)
- Python
Scaling transcription quality without scaling manual effort.
A specialized provider of medical billing, coding, and business analytics services had a transcript Quality Management System (QMS) that couldn't scale to call volume — and reviewing recordings by hand was inefficient and time-consuming. They engaged Sedin through an Agile, consulting-led engagement to pick the right LLM, redesign the system architecture, and integrate the services, all within a secure multi-cloud (Azure + AWS) environment.
- Make transcription scalable and cost-effective
- Keep operations uninterrupted with robust failure recovery
- Maintain healthcare-grade security across a multi-cloud (Azure + AWS) setup
Common questions
Through an Agile, consulting-led engagement. The solution pairs OpenAI's Whisper with Azure Batch Transcription, delivered as an hourly service integrated into the client's existing QMS.
Audio data is stored within the client's own private network using AWS S3 BYOS, meeting healthcare privacy standards while operating across the multi-cloud setup.
Advanced fault tolerance built with Python, AWS Lambda, and AWS Step Functions keeps operations uninterrupted, while diarization identifies individual speakers for precise audits.
Hand-reviewing call recordings didn't scale. By choosing the right LLM and rebuilding the pipeline around it, transcription became accurate enough to audit against directly. Quality went up as the manual load came down.
Pick the right model, then build around it reliably.
STEP
01
Consulting & LLM selection
An Agile, consulting-led engagement to choose the right model for healthcare transcription.
02
Architecture redesign
Rebuilt the system architecture and integrated the service with the existing QMS.
03
Hourly transcription service
OpenAI Whisper with Azure Batch Transcription, scaling to large volumes of audio.
04
Speaker diarization
Identifying individual speakers in transcriptions for precise, auditable records.
05
Fault tolerance & security
Python, AWS Lambda, and Step Functions for uninterrupted ops; AWS S3 BYOS in the client's private network.
Less manual effort, higher quality, reliable scale.
50%
Less manual review
Time and effort spent hand-reviewing call recordings cut in half.
Hourly
Automated transcription
An hourly service that scales to large volumes of audio as needed.
Multi-cloud
Secure by design
Azure + AWS, with audio stored in the client's private network to meet healthcare privacy standards.
The bottom line
Transcription quality up 75%, manual review effort down 50%, and large audio volumes handled reliably - with healthcare-grade security and uninterrupted operations.
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