Architecting AI-Powered Data Pipelines: Turning Raw Clinical Data into Intelligent Insights

Modern healthcare systems produce massive volumes of structured and unstructured data — yet much of it remains underutilized due to integration complexity, data silos, and governance constraints. This session explores how to design scalable, AI-driven data pipelines that transform raw data into actionable intelligence using Salesforce Health Cloud, MuleSoft, AWS, and advanced AI models. Drawing from real-world implementations, Avi will share how his team architected a HIPAA-compliant data platform that automates ingestion from EMRs, performs real-time processing, and leverages AI summarization models to assist clinical decision-making. Attendees will learn practical strategies for: Designing event-driven architectures for healthcare data flow. Implementing data governance and security controls in regulated environments. Applying AI models to summarize and extract context from clinical records. Achieving near real-time insights through cross-cloud data orchestration. This talk combines architectural thinking, implementation insights, and lessons learned from scaling data pipelines in a mission-critical healthcare setting.
Who is this presentation for?
This session is ideal for Data Architects, Solution Architects, AI Engineers, and Technical Leaders working in data-heavy industries (healthcare, finance, or enterprise SaaS) who want to bridge the gap between big data architecture and AI-driven analytics.
Prerequisite knowledge:
Basic understanding of cloud data architecture and API-driven integrations. Familiarity with data transformation frameworks (ETL/ELT). Introductory knowledge of machine learning or AI model workflows (helpful but not required).
What you'll learn?
How to architect an AI-powered data pipeline from ingestion to insight. Techniques to orchestrate multi-cloud data flows with compliance and scalability in mind. Practical ways to embed AI summarization models into existing data ecosystems. Frameworks to balance performance, governance, and innovation in big data environments. Lessons learned from real-world healthcare implementations that can be applied across industries.

Fnu Avi is a Senior Software Engineer and Solution Architect with extensive experience designing and implementing enterprise-scale, data-driven solutions across healthcare and technology domains. At Keck Medicine of USC, he leads architecture and development efforts involving Salesforce, MuleSoft, and AWS, focusing on intelligent automation, data integration, and AI-driven analytics. Avi has delivered more than two dozen cross-platform projects, including predictive analytics and secure interoperability frameworks connecting EMRs, APIs, and cloud systems. His recent work centers on leveraging AI and big data to streamline clinical workflows and optimize patient outcomes. He is also a published researcher in the field of data security and text steganography, with a strong passion for exploring how AI and big data can responsibly drive digital transformation in regulated industries.