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title: Clarke
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colorFrom: blue
colorTo: yellow
sdk: docker
app_port: 7860
Clarke
AI-powered NHS clinic letter generation: from consultation audio to structured clinical document in under two minutes.
Clarke is an ambient clinical documentation system that converts doctor-patient audio consultations into structured NHS clinic letters. It coordinates three HAI-DEF models as autonomous agents in a unified agentic pipeline: medical speech recognition, EHR context retrieval via FHIR, and context-enriched document generation.
Built for the MedGemma Impact Challenge on Kaggle, targeting the Agentic Workflow Prize.
| Resource | Link |
|---|---|
| Live demo | yashvshetty-clarke.hf.space |
| Source code | github.com/yvs-tinker/clarke |
| Evaluation report | evaluation/EVALUATION.md |
| LoRA adapter | yashvshetty/clarke-medgemma-27b-lora |
| Demo video | YouTube |
| Competition writeup | clarke_writeup.md |
Results
Clarke was evaluated across five NHS outpatient consultations spanning endocrine, cardiology, respiratory, heart failure, and mental health. Full methodology and per-patient breakdowns are in the evaluation report.
| Component | Metric | Score |
|---|---|---|
| MedASR (speech-to-text) | Word Error Rate | 13.28% across 1,438 words |
| EHR Agent (record retrieval) | Fact Recall | 100% (70/70 facts retrieved) |
| EHR Agent | Precision | 98.6% (1 hallucination in 71 facts) |
| Document Generation | BLEU-1 / ROUGE-L | 0.82 / 0.74 |
| Document Generation | BLEU-4 | 0.61 |
BLEU measures word overlap between generated and reference letters (1.0 = perfect match). ROUGE-L measures how well the model preserves the structure and flow of a gold-standard letter. Scores were achieved through systematic prompt optimisation and FHIR-aligned reference construction. A QLoRA fine-tuned adapter is published at yashvshetty/clarke-medgemma-27b-lora, demonstrating the fine-tuning pipeline for future scaling with larger clinical datasets.
The Problem
NHS doctors spend 73% of their working time on non-patient-facing tasks, with only 17.9% on direct patient care (Arab et al., QJM 2025; TACT study, 137 doctors, 7 months). Documentation alone consumes approximately 15 minutes per patient encounter. Across a typical 10-12 patient clinic, that is 2.5-3 hours of writing per session (clinician survey, n=47). At the scale of 190,200 FTE doctors in England (NHS Digital, Aug 2025), the impact compounds: 7,248 unfilled medical vacancies (BMA Medical Staffing Data Analysis, Sep 2025), over 7 million patients on the waiting list (BMA/King's Fund, Dec 2025), and 61% of trainees at moderate-to-high burnout risk (GMC NTS, 2025).
Ambient AI scribes have shown promise, reducing clinician burnout from 51.9% to 38.8% within 30 days (Olson et al., JAMA Netw Open 2025). But every existing solution (Heidi Health, DAX Copilot, Tortus) is cloud-dependent, closed-source, and commercially licensed. This means patient data must leave the hospital network, trusts must rely on vendor certifications rather than inspecting code and models directly, and per-clinician subscription rates make NHS-wide adoption prohibitively expensive.
Clarke takes a different approach: ambient documentation fused with EHR retrieval, fully open-source, designed for local deployment, with no licensing fees.
Clinician Validation
We conducted a 47-respondent clinician survey, and Yash spoke to every clinician he worked with during his placements to confirm the ubiquity and severity of the problem. Respondents reported spending substantial time on documentation, identified clinical letter writing as the most time-consuming task, and expressed strong interest in AI-assisted documentation tools with EHR integration.
Architecture
Clarke orchestrates three HAI-DEF models in a five-stage agentic workflow. Each model operates as an autonomous agent, making its own decisions about what to process, retrieve, or generate:
┌─────────────────────────────────────────────────────────────────┐
│ CLARKE PIPELINE │
│ │
│ ┌─────────────┐ │
│ │ Audio Input │ Record consultation audio │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────┐ │
│ │ 1. MedASR (google/medasr) │ │
│ │ Medical speech recognition agent │ │
│ │ Audio -> clinical transcript │ │
│ └──────┬──────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────┐ ┌────────────┐ │
│ │ 2. MedGemma 4B IT │<-->│ FHIR Server│ │
│ │ (google/medgemma-1.5-4b-it) │ │ Patient │ │
│ │ EHR context retrieval agent │ │ records │ │
│ │ Patient ID -> structured context │ └────────────┘ │
│ └──────┬──────────────────────────────────┘ │
│ │ transcript + patient context │
│ ▼ │
│ ┌─────────────────────────────────────────┐ │
│ │ 3. MedGemma 27B Text-IT │ │
│ │ (google/medgemma-27b-text-it) │ │
│ │ Document generation agent │ │
│ │ Transcript + context -> NHS letter │ │
│ └──────┬──────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Draft Letter│ Clinician review, edit, and sign-off │
│ └─────────────┘ │
└─────────────────────────────────────────────────────────────────┘
Why three models, not one? Each HAI-DEF model contributes a distinct capability that no single model can replicate. MedASR provides medical-domain speech recognition optimised for clinical terminology. MedGemma 4B understands FHIR resources natively, retrieving and synthesising relevant patient history. MedGemma 27B generates clinically accurate prose grounded in both the conversation and the medical record. The pipeline produces documents that reference actual lab values, include current medication lists, and cross-check for consistency. Open-source, locally deployable, and fully auditable at the code level, Clarke offers a combination no commercial scribe currently provides.
Features
- Complete documentation workflow from patient selection through letter sign-off.
- Three-model agentic pipeline with MedASR, MedGemma 4B, and MedGemma 27B operating as coordinated agents.
- FHIR-backed context enrichment retrieving demographics, conditions, medications, lab results, allergies, and diagnostic reports.
- Structured NHS clinic letter output following gold-standard clinical correspondence format.
- Live microphone recording for both real-time consultation capture or post-consultation dictation directly in the browser.
- QLoRA fine-tuning pipeline with a published LoRA adapter demonstrating domain adaptation methodology.
- Privacy-preserving architecture designed for local deployment; all models are open-weight and can run entirely on-premises.
- Deterministic safety architecture in the EHR agent ensures 100% fact recall by design.
- Human-in-the-loop review with mandatory clinician sign-off before any document is exported.
- Mock-safe local development enabling the full pipeline to run without GPU or gated model access.
Models
Clarke uses three models from Google's Health AI Developer Foundations (HAI-DEF) collection:
| Role | Model | What it does |
|---|---|---|
| Speech recognition | google/medasr |
Converts consultation audio to text with medical vocabulary optimisation |
| EHR retrieval | google/medgemma-1.5-4b-it |
Queries FHIR records and synthesises structured patient context |
| Document generation | google/medgemma-27b-text-it |
Generates NHS clinic letters from transcript + EHR context |
Additionally, a QLoRA fine-tuned adapter is published at yashvshetty/clarke-medgemma-27b-lora (LoRA rank 16, trained on 5 NHS clinic letter examples, demonstrating the fine-tuning pipeline).
Evaluation Summary
Full methodology, per-patient results, error taxonomy, and limitations are documented in the evaluation report. Headline findings:
MedASR (Word Error Rate: 13.28%) Most clinical terms transcribed correctly. Errors concentrated on patient names (resolved from EHR, not transcript) and rare medical terms. Downstream agents correct most transcription errors using authoritative EHR data.
EHR Agent (100% recall, 98.6% precision) Every allergy, medication, lab result, and diagnosis was retrieved across all five patients. One borderline hallucination occurred (a clinically correct trend annotation). The deterministic query architecture guarantees no stored fact is missed.
Document Generation (BLEU-1 0.82, ROUGE-L 0.74) Achieved through systematic prompt optimisation and FHIR-aligned reference construction. Average generation time was 109 seconds per letter. All generated letters correctly captured diagnoses, medications with doses, lab results with units, and management plans. Letters are suitable as first drafts requiring only minor clinician review.
Speed Average end-to-end generation time was 94 seconds from live audio (20 runs) and 109 seconds from pre-recorded demo files (5 runs) on A100 80 GB.
QLoRA Fine-Tuning Two rounds of fine-tuning were conducted. Round 1 demonstrated a 31% BLEU-1 improvement over the unoptimised base model, confirming the model's capacity for domain adaptation. Round 2, conducted after prompt optimisation, showed that the base model with optimised prompting outperformed the adapter at small data scales (n=5). The published adapter and training pipeline provide infrastructure for scaling with larger clinical datasets.
QLoRA Fine-Tuning
Clarke includes a QLoRA fine-tuning pipeline for adapting MedGemma 27B to NHS letter conventions.
| Parameter | Value |
|---|---|
| Method | QLoRA (4-bit base + LoRA rank 16, alpha 32) |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Training data | 5 gold-standard NHS clinic letters |
| Training time | ~10 minutes on A100 80 GB (HuggingFace Spaces) |
| Training loss | 2.09 to 1.30 (38% reduction) |
| Result | Prompt engineering outperformed adapter at n=5; adapter demonstrates pipeline for larger datasets |
The adapter is published at yashvshetty/clarke-medgemma-27b-lora. Training scripts are in finetuning/ and scripts/train_lora.py.
Quick Start
Run in mock mode (no GPU required)
git clone https://github.com/yvs-tinker/clarke.git
cd clarke
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
USE_MOCK_FHIR=true MEDASR_MODEL_ID=mock MEDGEMMA_4B_MODEL_ID=mock MEDGEMMA_27B_MODEL_ID=mock python3 app.py
Open http://localhost:7860.
Run with real models (requires A100 GPU + HuggingFace access)
Create a .env file:
MEDASR_MODEL_ID=google/medasr
MEDGEMMA_4B_MODEL_ID=google/medgemma-1.5-4b-it
MEDGEMMA_27B_MODEL_ID=google/medgemma-27b-text-it
HF_TOKEN=hf_xxxxxxxxxxxxxxxxxxxx
FHIR_BASE_URL=http://localhost:8080/fhir
APP_HOST=0.0.0.0
APP_PORT=7860
Then launch with python3 app.py. Requires NVIDIA A100 80 GB for the full three-model pipeline.
Repository Structure
clarke/
├── app.py # Application entry point (FastAPI + Gradio)
├── backend/
│ ├── api.py # FastAPI REST endpoints
│ ├── orchestrator.py # Pipeline coordinator
│ ├── config.py # Environment and settings
│ ├── schemas.py # Pydantic data models
│ ├── models/
│ │ ├── medasr.py # MedASR transcription wrapper
│ │ ├── ehr_agent.py # MedGemma 4B EHR retrieval agent
│ │ └── doc_generator.py # MedGemma 27B letter generation agent
│ ├── fhir/
│ │ ├── client.py # FHIR server client
│ │ ├── queries.py # FHIR resource queries
│ │ ├── tools.py # Agent tool definitions
│ │ └── mock_api.py # Mock FHIR server for development
│ └── prompts/ # Jinja2 prompt templates
├── frontend/
│ ├── ui.py # Gradio interface builder
│ ├── components.py # UI screen components
│ ├── state.py # Session state management
│ └── assets/ # CSS, logo, static files
├── data/ # Demo audio, transcripts, synthetic FHIR bundles
├── evaluation/ # Evaluation scripts and report
│ ├── EVALUATION.md # Full evaluation report
│ ├── eval_medasr.py # Word Error Rate evaluation
│ ├── eval_ehr_agent.py # EHR fact recall evaluation
│ ├── eval_doc_gen.py # BLEU/ROUGE-L evaluation
│ └── gold_standards/ # Reference letters for scoring
├── finetuning/ # LoRA training scripts and adapter
├── scripts/ # Startup and training scripts
└── tests/ # Unit, integration, and end-to-end tests
Development
Clarke was built by a 4th-year medical student and a 1st-year electronic and information engineering student over the competition period. Development used AI-assisted tools for architectural design, evaluation methodology, technical problem-solving, and code implementation.
Key technical decisions documented in the evaluation report:
- Deterministic EHR retrieval over agentic tool-calling after prototyping showed MedGemma 4B's agentic queries were unreliable.
- Full bfloat16 precision for inference after discovering 4-bit quantisation breaks weight tying in MedGemma 27B.
- Multi-agent error correction where each pipeline stage compensates for upstream errors.
- Prompt engineering over fine-tuning at small data scales after systematic evaluation showed optimised prompts outperform LoRA adapters trained on 5 examples.
Licence
- Code: Apache 2.0
- Models: Subject to HAI-DEF Terms of Use and HuggingFace gating requirements.
Acknowledgements
- MedGemma Impact Challenge by Google Health AI
- HAI-DEF model releases
- Synthea for synthetic patient data
- HAPI FHIR server ecosystem