Healthcare organizations generate vast amounts of data from electronic health records (EHRs), medical imaging, laboratory systems, wearable devices, and patient applications. Turning this data into actionable insights requires a modern, secure, and scalable data platform.

In this session, we'll explore how to build an AI-powered healthcare data lake on AWS that centralizes structured and unstructured data while enabling advanced analytics and generative AI use cases. You'll learn how AWS services can be combined to securely ingest, catalog, govern, analyze, and visualize healthcare data, while maintaining compliance and preparing it for AI-driven applications.

Whether you're a cloud architect, data engineer, developer, healthcare technologist, or AI enthusiast, this session will provide practical architecture patterns and best practices for designing modern healthcare data platforms on AWS.

What you'll learn:
1. How to design a scalable healthcare data lake architecture on AWS
2. Best practices for ingesting, storing, and governing healthcare data using Amazon S3, AWS Glue, AWS Lake Formation, and Amazon Athena
3. Approaches to building secure, compliant data platforms with encryption, IAM, auditing, and fine-grained access controls
4. How to integrate generative AI and analytics using Amazon Bedrock, Amazon QuickSight, and other AWS AI services
5. Real-world healthcare use cases such as patient analytics, clinical dashboards, document summarization, and population health insights
6. Cost optimization, monitoring, and operational best practices for production-ready healthcare data platforms

By the end of this session, you'll understand how to build a secure, scalable, and AI-ready healthcare data lake on AWS that transforms raw healthcare data into meaningful insights and intelligent applications.