https://github.com/danieladdisonorg/healthcare-agent
A comprehensive healthcare management system that provides a REST API for accessing ICU patient data and medical appointments. The system leverages AWS Bedrock for AI-powered clinical assistance and real-time alert generation for critical patient conditions.
https://github.com/danieladdisonorg/healthcare-agent
ai-agents apache-kafka avro-schema aws-bedrock fastapi healthcare
Last synced: 3 months ago
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A comprehensive healthcare management system that provides a REST API for accessing ICU patient data and medical appointments. The system leverages AWS Bedrock for AI-powered clinical assistance and real-time alert generation for critical patient conditions.
- Host: GitHub
- URL: https://github.com/danieladdisonorg/healthcare-agent
- Owner: danieladdisonorg
- License: mit
- Created: 2025-06-20T18:48:12.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-20T19:00:10.000Z (4 months ago)
- Last Synced: 2025-06-20T19:55:37.956Z (4 months ago)
- Topics: ai-agents, apache-kafka, avro-schema, aws-bedrock, fastapi, healthcare
- Language: Python
- Homepage:
- Size: 17.6 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Healthcare AI Agent
A comprehensive healthcare management system that provides a REST API for accessing ICU patient data and medical appointments. The system leverages AWS Bedrock for AI-powered clinical assistance and real-time alert generation for critical patient conditions.
## Overview
The Healthcare AI Agent is designed to streamline healthcare operations by providing:
- Real-time ICU patient monitoring and data management
- Intelligent appointment scheduling and tracking
- AI-powered clinical decision support through chat interface
- Automated alert system for critical patient conditions
- Scalable data ingestion via Apache Kafka
## Architecture
The system utilizes a microservices architecture with the following components:
- **FastAPI REST API**: Provides endpoints for data access and AI interactions
- **Apache Kafka Integration**: Real-time data streaming via Confluent Cloud
- **AWS Bedrock**: AI/ML services for chat assistance and data generation
- **Avro Schema**: Structured data serialization for reliable message processing
## Project Structure
```
healthcare_agent/
├── Dockerfile # Container configuration
├── README.md # Project documentation
├── .gitignore # Git ignore rules
└── src/
├── agent.py # Core agent logic and Kafka consumer
├── main.py # FastAPI application and API endpoints
├── dummy_data_producer.py # Test data generator
├── healthcare_record.avsc # Avro schema definition
├── requirements.txt # Python dependencies
└── .env.example # Environment configuration template
```
## Prerequisites
- Python 3.8 or higher
- Docker (optional, for containerized deployment)
- AWS Account with Bedrock access
- Confluent Cloud Kafka cluster
- Valid AWS credentials configured
## Installation & Setup
### 1. Environment Setup
Clone the repository and navigate to the source directory:
```bash
cd src
```
Create and activate a Python virtual environment:
```bash
python -m venv venv
```
**Windows:**
```bash
.\venv\Scripts\activate
```
**macOS/Linux:**
```bash
source venv/bin/activate
```
### 2. Install Dependencies
```bash
pip install -r requirements.txt
```
### 3. Configuration
Create a `.env` file in the `src` directory with the following configuration:
```env
# Confluent Cloud Kafka Configuration
BOOTSTRAP_SERVERS="pkc-xxxx.region.provider.confluent.cloud:9092"
SCHEMA_REGISTRY_URL="https://sr-xxxx.region.provider.confluent.cloud"
SCHEMA_REGISTRY_API_KEY="YOUR_SR_API_KEY"
SCHEMA_REGISTRY_API_SECRET="YOUR_SR_API_SECRET"
CLUSTER_API_KEY="YOUR_CLUSTER_API_KEY"
CLUSTER_API_SECRET="YOUR_CLUSTER_API_SECRET"
# AWS Bedrock Configuration
BEDROCK_MODEL_ID="anthropic.claude-sonnet-4-20250514-v1:0"
AWS_DEFAULT_REGION="us-east-1"
AWS_ACCESS_KEY_ID="YOUR_AWS_ACCESS_KEY"
AWS_SECRET_ACCESS_KEY="YOUR_AWS_SECRET_KEY"
```
> **Note**: Ensure your AWS credentials have appropriate permissions for Bedrock service access.
## Deployment
### Local Development
1. **Start the Data Producer** (Terminal 1):
```bash
python dummy_data_producer.py
```
2. **Start the Healthcare Agent** (Terminal 2):
```bash
uvicorn main:app --reload --port 8000
```
The API will be available at `http://localhost:8000` with interactive documentation at `http://localhost:8000/docs`.
### Docker Deployment
Build the Docker image:
```bash
docker build -t healthcare-agent .
```
Run the containerized application:
```bash
docker run -p 8000:8000 --env-file ./src/.env healthcare-agent
```
## API Reference
### Patient Management
| Endpoint | Method | Description |
|----------|--------|-------------|
| `/icu/patients` | GET | Retrieve all ICU patient data |
| `/icu/patients/{patient_id}` | GET | Get specific patient information |
### Appointment Management
| Endpoint | Method | Description |
|----------|--------|-------------|
| `/appointments/{doctor_id}` | GET | Get doctor's upcoming appointments |
### Alert System
| Endpoint | Method | Description |
|----------|--------|-------------|
| `/alerts` | GET | Retrieve critical patient alerts |
### AI Chat Interface
| Endpoint | Method | Description | Request Body |
|----------|--------|-------------|--------------|
| `/chat` | POST | AI-powered clinical assistance | `{"session_id": "string", "query": "string"}` |
### Example API Usage
**Chat with AI Assistant:**
```json
POST /chat
{
"session_id": "session_001",
"query": "What is the current status of patient P123?"
}
```
**Get Patient Data:**
```bash
curl -X GET "http://localhost:8000/icu/patients/P123"
```
## Data Schema
Healthcare events follow the Avro schema defined in `healthcare_record.avsc`, supporting:
- ICU patient updates with vital signs and medical status
- Appointment scheduling with doctor and patient details
- Structured data validation and serialization
## Monitoring & Alerts
The system automatically generates alerts for:
- Critical vital sign thresholds
- Abnormal patient conditions
- System health and connectivity issues
Alerts are timestamped and stored for historical analysis and reporting.
## Security Considerations
- All API endpoints should be secured with appropriate authentication in production
- Environment variables contain sensitive credentials and should be properly managed
- AWS IAM roles and policies should follow the principle of least privilege
- Network security groups should restrict access to necessary ports only
## Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/new-feature`)
3. Commit your changes (`git commit -am 'Add new feature'`)
4. Push to the branch (`git push origin feature/new-feature`)
5. Create a Pull Request
## Support
For technical support or questions:
- Review the API documentation at `/docs` endpoint
- Check system logs for error details
- Verify environment configuration and credentials
## License
This project is licensed under the [MIT License](./LICENSE)