Healthcare Data Integration Platform

Project Overview

The Healthcare Data Integration Platform is a scalable solution designed to handle large volumes of healthcare data, integrating multiple data sources, and processing both real-time and batch data. The platform ingests, processes, and analyzes healthcare data in FHIR (Fast Healthcare Interoperability Resources) format, enabling seamless data integration and advanced analytics for improved healthcare outcomes.

This project focuses on building a robust backend for data ingestion and processing, integrating machine learning models for predictive analytics, and providing a React-based frontend dashboard for real-time data visualization.

Project Objective

• To build a comprehensive data integration platform for healthcare data in compliance with FHIR standards.
• To implement real-time and batch data processing pipelines using Azure Databricks, Kafka, and PySpark.
• To develop machine learning models for predictive analytics on patient data.
• To create an interactive React-based frontend for healthcare data insights and visualization.

Tech Stack

Core Technologies

Category	Tools/Technologies
Programming Languages	Python, JavaScript (React JS), SQL
Data Processing	PySpark, Apache Kafka, Azure Databricks
Cloud Services	Azure Data Factory, Azure Data Lake, Azure SQL, Azure Functions, Cosmos DB
Orchestration	Apache Airflow, Azure Data Factory
Machine Learning	Scikit-learn, TensorFlow, Hugging Face Transformers
API Development	FastAPI, OAuth 2.0, JWT
Containerization	Docker, Kubernetes, Terraform
Frontend	React JS, Material UI, Redux
DevOps	GitHub Actions, Terraform, Azure DevOps
Data Governance	Azure Purview, Great Expectations

Other Tools and Libraries

• Logging and Monitoring: Azure Monitor, Prometheus, Grafana
• Search Optimization: Elasticsearch
• CI/CD: GitHub Actions, Azure Pipelines
• Data Warehouse: Azure Synapse Analytics, Snowflake

Features

1. Real-time Data Ingestion:

   • Ingest healthcare data in real-time using Apache Kafka.
   • Stream patient data from FHIR-compliant APIs to Azure Data Lake for storage.

2. Batch Data Processing:

   • Scheduled ETL pipelines using Azure Data Factory and Apache Airflow.
   • Process and transform data using PySpark on Azure Databricks.

3. Machine Learning and Predictive Analytics:

   • Implement predictive models for patient outcome predictions (e.g., readmission rates).
   • Use NLP models for summarizing clinical notes and extracting key medical information.

4. API Integration:

   • Develop secure RESTful APIs using FastAPI for data submission and retrieval.
   • Implement OAuth 2.0 and JWT for secure API access.

5. Data Visualization Dashboard:

   • Create a responsive React JS frontend for data insights and visualization.
   • Provide real-time analytics and alerts for patient data metrics.

6. Data Governance and Compliance:

   • Implement data quality checks using Great Expectations.
   • Ensure data compliance with healthcare standards like HIPAA, HL7, and FHIR.

Architecture Diagram

The architecture includes the following components:

• Data Ingestion Layer: Uses Apache Kafka for streaming data from FHIR APIs.
• Data Processing Layer: Leverages Azure Databricks and PySpark for data transformation and analysis.
• Storage Layer: Utilizes Azure Data Lake and Azure SQL Database for raw and processed data storage.
• Machine Learning Layer: Integrates ML models for predictive analytics and NLP-based summarization.
• API Layer: Provides RESTful APIs for data interaction and secure access using FastAPI.
• Orchestration Layer: Manages ETL workflows using Apache Airflow and Azure Data Factory.
• Frontend Layer: React JS dashboard for data visualization and user interaction.

Project Structure

Healthcare-Data-Integration-Platform/
├── backend/
│   ├── ingestion/
│   ├── processing/
│   ├── api/
├── machine_learning/
│   ├── models/
│   ├── pipelines/
├── frontend/
│   ├── src/
│   ├── public/
├── config/
│   └── .env
├── data/
│   ├── raw/
│   ├── processed/
├── terraform/
│   ├── main.tf
├── tests/
│   ├── unit/
│   ├── integration/
├── README.md
└── requirements.txt

Getting Started

Prerequisites

• Python (3.10 or above)

• Node.js (for React frontend)

• Docker (for containerization)

• Terraform (for cloud resource provisioning)

• Azure Account (for cloud services)

Installation

1. Clone the Repository:

git clone https://github.com/yourusername/Healthcare-Data-Integration-Platform.git
cd Healthcare-Data-Integration-Platform

2. Set Up Python Virtual Environment:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

3. Set Up Environment Variables:

   • Create a .env file in the config directory with the following:

AZURE_STORAGE_ACCOUNT_NAME=your_storage_account
AZURE_STORAGE_ACCOUNT_KEY=your_storage_key
KAFKA_BROKER_URL=localhost:9092

4. Run Docker Compose:

docker-compose up -d

Contributing

Contributions are welcome! Please submit a pull request or open an issue if you have suggestions for improving the project.

License

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

Contact

For any inquiries, please reach out to:

• Name: Ponchanon Datta Rone

• Email: [email protected]

• LinkedIn: linkedin.com/in/ponchanon