HospitalIQ 🏥

DevHub@iLab's SummerBuild 2025 Project

HospitalIQ is a comprehensive hospital management system that combines intelligent patient flow prediction, room allocation optimization, and AI-powered medical assistance. Built with modern web technologies and machine learning capabilities.

🌟 Features

🤖 AI-Powered Medical Chatbot

• LangGraph-based Conversational AI with conditional database querying
• Smart Query Routing - automatically determines whether to access database or provide general responses
• Vector Search Integration for medical skills and specialties
• DateTime Awareness - provides current time/date information
• Database Integration with medical staff and skills data

📊 Patient Flow Prediction

• LSTM Machine Learning Model for predicting patient admission patterns
• Multi-feature Time Series Analysis with cyclical encoding (hourly, daily patterns)
• 24-hour Forecast Horizon for capacity planning
• Flask API for real-time predictions

🏨 Hospital Management Dashboard

• Real-time Room Availability with interactive floor plans
• Patient Entry Management with severity classification
• Room Assignment System with visual grid interface
• Patient Database with full CRUD operations
• IoT Integration for alerts and monitoring

🎯 Key Management Functions

• Patient Allocation - Assign patients to available rooms
• Capacity Planning - Predict and manage hospital capacity
• Staff Management - Track medical staff specialties and skills
• Real-time Monitoring - Dashboard with KPIs and alerts

🏗️ Architecture

HospitalIQ/
├── backend/              # Django REST API
│   ├── chatbot/         # AI Chatbot with LangGraph
│   ├── users/           # Patient & Staff Management
│   └── hospitaliq/      # Main Django App
├── frontend/            # Next.js React Application
│   └── hospitaliq/      # Modern UI with Material-UI
├── notebooks/           # ML Development & Analysis
│   ├── model.py         # LSTM Model Architecture
│   ├── app.py           # Flask Prediction API
│   └── *.ipynb          # Jupyter Notebooks
└── output/              # Generated Data (FHIR, Metadata)

🚀 Tech Stack

Backend

• Django 5.2.3 - REST API framework
• Django REST Framework - API development
• Knox Authentication - Token-based auth
• SQLite - Database (doctors.db for chatbot)
• CORS Headers - Cross-origin support

AI & Machine Learning

• LangChain & LangGraph - AI orchestration framework
• OpenAI GPT-4o-mini - Language model
• ChromaDB - Vector database for embeddings
• PyTorch - Deep learning framework
• Scikit-learn - ML utilities
• LSTM Networks - Time series prediction

Frontend

• Next.js 15.3.3 - React framework with App Router
• Material-UI (MUI) - Component library
• TanStack Query - Data fetching and caching
• Axios - HTTP client
• TypeScript - Type safety
• Tailwind CSS - Utility-first CSS

Data Science

• Jupyter Notebooks - Analysis and development
• Pandas & NumPy - Data manipulation
• Flask - ML model serving
• Pickle - Model serialization

📦 Installation

Prerequisites

• Python 3.11+
• Node.js 18+
• OpenAI API Key

Backend Setup

1. Clone and navigate to backend

   cd backend

2. Create virtual environment

   python -m venv .venv
   .venv\Scripts\activate  # Windows
   source .venv/bin/activate  # macOS/Linux

3. Install dependencies

   pip install -r requirement.txt

4. Environment setup

   # Create .env file with your OpenAI API key
   echo "OPENAI_API_KEY=your_openai_api_key" > .env

5. Database setup

   python manage.py migrate
   python manage.py createsuperuser

6. Run Django server

   python manage.py runserver

Frontend Setup

1. Navigate to frontend

   cd frontend/hospitaliq

2. Install dependencies

   npm install

3. Environment setup

   # Create .env.local
   echo "NEXT_PUBLIC_BACKEND_URL=http://localhost:8000" > .env.local

4. Run development server

   npm run dev

ML Model Setup

1. Navigate to notebooks

   cd notebooks

2. Run Flask prediction API

   python app.py

🎮 Usage

Access Points

• Frontend Dashboard: http://localhost:3000
• Backend API: http://localhost:8000
• ML Prediction API: http://localhost:5000
• Django Admin: http://localhost:8000/admin

User Roles

1. Medical Staff - Full dashboard access with patient management
2. General Users - Chatbot access only

Key Workflows

1. Patient Management

1. Add new patients via dashboard
2. Create patient entries with severity levels
3. Assign rooms using interactive floor plan
4. Monitor patient status and room availability

2. AI Chatbot Interaction

1. Access chatbot interface
2. Ask questions about:
   • Medical staff information
   • Hospital specialties
   • Current date/time
   • General medical knowledge
3. Get intelligent responses with database integration

3. Capacity Prediction

1. Send POST request to /api/predict with current patient count
2. Receive 24-hour admission forecast
3. Use predictions for capacity planning

🧪 Testing

Chatbot Testing

cd backend/chatbot
python test_datetime_tools.py
python test_datetime_chatbot.py
python enhanced_test.py

Interactive Chatbot Demo

cd backend/chatbot
python langgraph_chatbot.py

📊 API Endpoints

Authentication

• POST /api/auth/login/ - User login
• POST /api/auth/logout/ - User logout

Patient Management

• GET/POST /patient/ - List/Create patients
• GET/PUT/DELETE /patient/{id}/ - Patient CRUD
• GET/POST /patient_entry/ - Patient entries
• POST /patient_entry/{id}/assign_room/ - Room assignment

Room Management

• GET /room/availability/ - Floor availability
• GET /room/floor/{floor}/ - Rooms by floor

Chatbot

• POST /chatbot/chatbot/ - Chat with AI assistant

ML Prediction

• POST /api/predict - Get patient admission forecast

🗃️ Database Schema

Key Models

• Patient - Patient information (name, DOB, etc.)
• PatientEntry - Hospital admissions with severity
• Room - Hospital rooms with capacity and occupancy
• User - Staff and user accounts
• Doctor - Medical staff with specialties (chatbot DB)
• Skills - Medical skills database (chatbot DB)

🔧 Configuration

Environment Variables

# Backend (.env)
OPENAI_API_KEY=your_openai_api_key
SECRET_KEY=your_django_secret_key

# Frontend (.env.local)
NEXT_PUBLIC_BACKEND_URL=http://localhost:8000

Model Configuration

• LSTM Model: 64 hidden units, 24-hour horizon
• Vector Store: ChromaDB with text-embedding-3-small
• Chat Model: GPT-4o-mini for cost efficiency

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

📈 Future Enhancements

Planned Features

• Real-time IoT Integration for medical devices
• Advanced Analytics Dashboard with predictive insights
• Mobile Application for on-the-go access
• Integration with Hospital Information Systems
• Multi-language Chatbot Support
• Advanced ML Models for outcome prediction

Technical Improvements

• Containerization with Docker
• Cloud Deployment (Azure/AWS)
• Performance Optimization for large datasets
• Enhanced Security with OAuth2
• Real-time Notifications with WebSockets

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🎯 Project Context

SummerBuild 2025 - DevHub@iLab

• Duration: Summer 2025 Development Program
• Focus: Healthcare Technology Innovation
• Team: Student developers and researchers
• Goal: Create practical solutions for hospital management

📞 Support

For questions and support:

• Project Issues: Use GitHub Issues
• Documentation: Check inline code comments
• Development: Follow setup instructions above

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Built with ❤️ by summerCubs

Revolutionizing hospital management through AI and modern web technologies