Project Overview
In collaboration with a leading healthcare provider, Cubet delivered an AI-powered ambient transcription system designed to ease clinical documentation and improve the accuracy of electronic medical records (EMRs). This solution passively listens to doctor–patient conversations in real time and converts them into structured, editable consultation notes that integrate directly with EMR systems. The result: reduced physician burnout, enhanced documentation quality, and more uninterrupted face time between clinicians and patients.
Industry
Healthcare Technology / Health IT / Clinical Documentation
The Client
A reputed healthcare provider managing high-volume outpatient consultations across multiple specialties. The organisation was looking for a solution to reduce the increasing documentation workload faced by clinicians while ensuring consistent, high-quality medical records.
Challenges Addressed
Physicians in today’s healthcare systems are under constant pressure to document every patient encounter thoroughly and accurately. Traditional methods—manual note-taking, after-hours dictation, or hiring human scribes—come with significant downsides:
- Time-consuming and error-prone, especially when notes are recorded post-consultation
- Reduced quality of interaction between doctor and patient due to constant note-taking
- Increased clinician fatigue and risk of burnout
- Inconsistent documentation across departments and medical specialties
- Legacy dictation tools lack contextual understanding and seamless EMR integration
The client needed a solution that could:
- Accurately transcribe consultations in real time
- Understand medical context across specialties
- Structure notes according to clinical standards
- Integrate directly with existing EMR infrastructure
- Support physician oversight without adding workflow friction
Collaboration in Action
Cubet built and deployed a production-grade AI-driven transcription platform tailored for clinical consultations. The system passively listens during a doctor–patient session, transcribes speech to text using healthcare-optimised ASR models, and applies medical language understanding to generate structured summaries.
The physician remains in full control. Each consultation note is first presented to the doctor for approval or edits, ensuring trust, accuracy, and alignment with medical standards. Once approved, the note is seamlessly pushed to the EMR, eliminating double data entry.
Technologies Deployed
- Frontend: React-based clinician dashboard that displays a live view of transcribed notes for real-time review and editing
- Backend:
- Microservices architecture built in Python
- FastAPI used for service orchestration and API management
- HL7 FHIR or custom APIs for secure EMR integration
- AI/ML Models:
- Whisper or custom ASR models fine-tuned for medical audio
- GPT-based summarization engine enhanced with clinical logic
- Named Entity Recognition to detect symptoms, drug names, dosages, ICD codes
- Infrastructure:
- Azure Kubernetes Service (AKS) for scalable deployment
- Celery + Redis for real-time transcription queue management
- Data Storage:
- PostgreSQL for storing structured medical notes
- Encrypted blob storage for storing consultation audio, with configurable retention
Innovative Feature
The standout innovation lies in the system’s context-aware summarization engine. It doesn’t just convert speech to text; it understands the flow and structure of clinical consultations. Using a fine-tuned medical language model, the system organises the output into the SOAP (Subjective, Objective, Assessment, Plan) format and auto-identifies relevant entities—symptoms, prescriptions, dosages, diagnostics—with precision.
Additionally, its Physician-in-the-Loop approach ensures that clinicians can verify and control what is saved into the EMR without disrupting their consultation flow. This blend of automation and human validation was key to adoption and trust.
Value Delivered
- 60–70% reduction in time spent on clinical documentation per patient visit
- Improved EMR accuracy, with fewer omissions or backdated entries
- Natural, uninterrupted consultations led to greater patient satisfaction
- Faster EMR entry ensured that notes were available in near real-time across departments
- Scalable design adaptable for future use cases, including telehealth, multilingual transcription, and post-visit follow-ups
User Feedback
Clinicians appreciated the ability to focus more on their patients and less on their screens. The real-time preview allowed them to quickly verify the generated summaries without feeling like they had relinquished control. Adoption was smooth across departments, with minimal disruption to existing workflows. IT teams acknowledged the robust API design and smooth EMR integration as critical for system compatibility.
Conclusion
The solution delivered by Cubet proved that ambient AI in healthcare isn’t about replacing clinicians—it’s about empowering them. By bridging the gap between conversation and documentation, the system helped restore the physician’s focus on care while ensuring that every word spoken in the consultation room became part of a trustworthy, structured medical record.
Impact Made
This AI-powered transcription system not only reduced documentation fatigue but also transformed the quality and speed of clinical data capture. For hospital executives, CMIOs, and digital health leaders, it represents a future-ready leap in operational efficiency, clinical accuracy, and digital maturity—while preserving what matters most: the doctor–patient connection.