Building a Real-Time Data Pipeline for Mental Health Outcome Tracking

In the Mental Health & Wellness industry, timely data-driven insights are crucial for improving care delivery and outcomes. Our enterprise-level company is embarking on a project to develop a real-time data pipeline that tracks and analyzes patient outcomes. The goal is to integrate diverse data sources, including electronic health records (EHRs), patient feedback platforms, and wearable device data, into a unified system that provides actionable insights. This project will involve setting up a data mesh using Apache Kafka for event streaming, transforming and processing data with Spark and Airflow, and storing it in a cloud-based data warehouse such as Snowflake or BigQuery. Additionally, we'll implement MLOps practices to ensure continuous integration and deployment of machine learning models that predict patient outcomes and spot potential areas for intervention. By facilitating real-time data observability, the pipeline aims to enhance proactive care management, ultimately leading to better patient outcomes.