A hybrid AI system that reduces nurse shift-change documentation time from 20+ minutes to under 5 seconds. Combines rule-based critical detection with LLM-powered summarization for reliable, intelligent clinical handoffs.
- Vital Signs Tracking with real-time critical alerts (BP, HR, Temp, O2)
- Medication Change Logging (started, held, increased, decreased, discontinued)
- Timestamped Clinical Notes with collaborative documentation
- Previous Shift Handoffs for continuity of care
- Rule-Based Critical Detection — 100% reliability on life-critical vitals
- BP < 90 or > 140 mmHg → Hypotension/Hypertension
- HR < 60 or > 100 BPM → Bradycardia/Tachycardia
- Temp > 38.0°C → Febrile
- O2 < 95% → Hypoxic
- LLM-Powered Summarization — Llama 3.2 generates natural clinical narratives
- Privacy by Design — Runs entirely on local Ollama (no cloud API calls)
- Critical Items — Auto-flagged abnormal vitals and medication changes
- Stable Items — AI-extracted routine observations (only when no critical alerts)
- Pending Tasks — Explicit tasks + AI-extracted from notes
- Narrative Summary — 2-3 sentence clinical overview
- Dashboard with active shifts, pending handoffs, and patient overview
- Toast Notifications for all actions (no browser alerts)
- Modal Confirmations with frosted glass backdrop
- Form Validation with red borders and inline error messages
- Delete Functionality for patients, medications, and notes
- Auto-Replace Vitals — Only latest vitals used (no stale data)
- React — Component-based UI with hooks
- React Router — Client-side routing
- Tailwind CSS — Utility-first styling
- Axios — HTTP client for API calls
- Custom Components — Modal, Toast, VitalsForm, MedicationList, etc.
- FastAPI — Modern Python web framework
- SQLAlchemy — SQL ORM with Alembic migrations
- Pydantic — Data validation and serialization
- SQLite — Lightweight database (PostgreSQL-ready)
- Uvicorn — ASGI server
- Llama 3.2 (3B) — Meta's open-source LLM
- Ollama — Local LLM runtime (no cloud APIs)
- Hybrid Logic — Rules + AI for reliability + intelligence
# Navigate to backend directory
cd backend
# Create virtual environment (recommended)
python -m venv venv
# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Run the backend server
uvicorn app.main:app --reload
Open a new terminal (keep backend running):
# Navigate to frontend directory
cd frontend
# Install dependencies
npm install
# Start development server
npm run dev
---
### Ollama Setup (AI Model)
Open a **third terminal**:
```bash
# Install Ollama (if not already installed)
# macOS/Linux:
curl -fsSL https://ollama.com/install.sh | sh
# Windows: Download from https://ollama.com/download
# Start Ollama server
ollama serve
# Pull Llama 3.2 model (one-time download, ~2GB)
ollama pull llama3.2:3b