Patient7 is a next-generation healthcare technology company focused on improving the efficiency, accuracy, and accessibility of clinical workflows through intelligent automation. By combining real-time speech recognition with generative AI, Patient7 empowers clinicians to capture and document patient interactions seamlessly.
The problem: manual documentation burden slowed clinicians and reduced patient focus
Clinical documentation remains one of the most time-consuming and error-prone tasks in healthcare. At Patient7’s partner clinics, physicians spent significant time typing or dictating notes after consultations, often cutting into patient time and after-hours.
This manual process led to inconsistent SOAP notes, missed details, and mounting administrative fatigue. With increasing HIPAA compliance demands, maintaining secure and auditable records has become even more complex. These challenges underscored the need for a reliable AI medical transcription solution to automate notetaking, ensure accuracy, and restore clinicians’ focus on patient care.
The solution: an AI medical transcription engine built with AWS Transcribe and Amazon Bedrock
To address these issues, GoML partnered with Patient7 to design and deploy a cloud-native AI medical transcription PoC that captures doctor–patient conversations in real time, transcribes them securely, and automatically generates structured SOAP notes using generative AI.
Core components of the solution:
Real-time audio capture and streaming
- Patient7’s web interface records live audio from doctor–patient conversations.
- Audio streams securely to backend APIs via HTTPS, ensuring encrypted transmission.
AI medical transcription pipeline
- Uses AWS Transcribe Medical for high-accuracy speech-to-text conversion in English and French.
- Handles domain-specific medical terms, speaker identification, and noise suppression.
LLM-powered SOAP note generation
- Integrated Claude 3.5 via Amazon Bedrock to structure transcribed text into SOAP (Subjective, Objective, Assessment, Plan) format.
- Generates contextually consistent and clinically coherent notes.
Secure data storage and compliance
- Audio and generated notes stored in AWS S3 with controlled access and auto-deletion within 24 hours.
- Role-based access control and encryption ensure full HIPAA compliance.
Deployment and DevOps integration
- Backend built using FastAPI, deployed on AWS EC2 containers.
- Frontend developed with React, integrated with real-time transcription APIs.
- CI/CD via GitHub Actions automates testing and deployment workflows.
The impact: transforming medical documentation with AI medical transcription
The AI medical transcription PoC helped Patient7 streamline the clinical documentation workflow, cutting down transcription time, and improving note accuracy.
Key outcomes
- 75% reduction in time spent on clinical documentation
- >90% transcription accuracy for medical conversations
- 100% HIPAA-compliant storage and data handling
Lessons for other healthcare organizations
Common pitfalls to avoid
- Overreliance on generic speech-to-text models without medical tuning
- Ignoring compliance requirements during PoC stages
- Missing validation loops for LLM-generated clinical notes
Advice for healthcare teams adopting AI medical transcription
- Start with one or two specialties (e.g., general practice, pediatrics) to validate accuracy and workflow fit
- Integrate human-in-the-loop review for quality assurance in early deployments
- Leverage LLMs like Claude 3.5 for structured SOAP generation rather than plain text output
- Implement audit logging and automatic data deletion to maintain trust and compliance
Do you want to automate your clinical documentation with AI medical transcription?
Let GoML help you build an AI-powered transcription and clinical note generation engine, just like we did for Patient7, combining LLM intelligence with real-time accuracy and compliance.
