CodeSilver is an ambient AI prototype that translates clinical discussions into operational intelligence — without interrupting clinicians or adding workflow friction.
Instead of building another chatbot or scribe, CodeSilver focuses on the invisible gap between clinical intent and hospital operational reality.
🌐 Live App: https://silent-protocol-translator.lovable.app/
Hospitals run on two parallel languages:
- Clinical Language — diagnoses, treatment decisions, medical reasoning.
- Operational Language — DRGs, admission status, documentation requirements, prior authorization rules.
Today, these worlds rarely intersect in real time.
Resulting issues:
- Claim denials due to missing documentation language
- Observation vs. inpatient billing errors
- Length-of-stay overruns
- Delayed prior authorization discovery
CodeSilver introduces a silent translation layer that converts clinical narratives into operational insights for utilization review and revenue cycle teams.
Most healthcare AI tools focus on:
- Patient chatbots
- Physician documentation
- Clinical diagnosis
CodeSilver focuses on the operational middle layer — the space between clinical speech and billing workflows.
Key principles:
- 🧠 Zero interruption: No pop-ups or clinical alerts
- 🧾 Operational visibility: Generates summaries for non-clinical teams
- 🔐 Synthetic/Public Data Only: No PHI required
- Real-time clinical → operational translation
- Documentation gap detection
- Admission vs observation status insights
- DRG prediction (prototype level)
- Prior authorization rule awareness
- Denial risk indicator (experimental)
Clinical Transcript (MIMIC-IV / Synthetic Notes) │ ▼ LLM Translation Engine │ ▼ Structured Operational Summary │ ├── DRG Suggestions ├── Documentation Flags ├── Status Insights └── Prior Auth Indicators
| Layer | Technology |
|---|---|
| Backend API | FastAPI (Python) |
| LLM Engine | Llama-3 / Mixtral / GPT API (prototype) |
| Data Sources | MIMIC-IV + Public CMS Rules |
| Frontend | Optional React / Static Demo UI |
| Deployment | Docker (recommended) |
codesilver/ │ ├── app/ │ ├── main.py # FastAPI entry point │ ├── prompts/ # Prompt templates │ ├── models/ # LLM interface │ └── utils/ # Parsing + formatting logic │ ├── data/ │ ├── synthetic_notes/ │ └── cms_rules/ │ ├── evaluation/ │ └── metrics.py │ ├── frontend_demo/ # Optional UI │ └── README.md
git clone https://github.com/yourusername/codesilver.git
cd codesilver
2️⃣ Create Environment
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
3️⃣ Configure Model
Create .env:
MODEL_PROVIDER=openai
OPENAI_API_KEY=your_key_here
or switch to local open-source models.
4️⃣ Run API
uvicorn app.main:app --reload
Open:
http://localhost:8000/docs
🧪 Example Input
"Patient with worsening COPD exacerbation, not responding to nebulizers.
Starting prednisone. Observe for 24 hours."
Example Output (Prototype)
Suspected DRG: COPD with CC
Documentation Gap: Severity not specified
Status Alert: Observation Day 1
Prior Auth: None detected
Denial Risk: Moderate
📊 Dataset Strategy
This project intentionally avoids private clinical data.
Public sources used:
MIMIC-IV synthetic clinical notes
CMS DRG documentation
ICD-10-CM coding guidelines
Medicare 2-Midnight Rule
LCD/NCD coverage policies
Synthetic annotation pairs are generated for training and evaluation.
📈 Evaluation Approach
Prototype evaluation compares:
Raw clinical notes (baseline)
Human-coded annotations
CodeSilver translation outputs
Metrics include:
Documentation gap detection rate
Admission status alignment
DRG mapping consistency
###🔒 Safety & Limitations
###Synthetic/Public Data Only
Not validated on live EHR systems.
###Not Clinical Decision Support
Does not recommend treatment.
###Human Review Required
Outputs are informational, not automated billing actions.
###Coder Replacement Not Intended
Designed to reduce operational friction.
###Dataset Bias Risk
MIMIC-IV primarily reflects academic ICU settings.
###Regulatory Status
Educational and operational prototype only.
🎯 Hackathon Scope
This repository represents a 48-hour prototype, not a production system.
Goals:
Demonstrate feasibility of clinical → operational translation
Visualize documentation gaps
Show real-time insight generation
Non-goals:
Full revenue cycle automation
Real EHR integration
Regulatory-ready deployment
🤝 Contributing
This project welcomes:
Healthcare operations experts
Medical coders
ML engineers
Clinical informatics researchers
Open an issue or submit a pull request.
📜 License
MIT License — see LICENSE file.
🧭 Vision
Everyone is building AI to make clinicians faster.
CodeSilver explores how AI can make healthcare systems understand clinicians better — without adding clicks, alerts, or cognitive load.
Built for research, experimentation, and responsible innovation.