AI-Powered Patient Risk Analytics in Healthcare: Leveraging Cloud Data Architecture for Improved Clinical Outcomes

Abstract

This article presents a comprehensive technical architecture for an innovative healthcare analytics system that leverages artificial intelligence to identify patient deterioration risks in real-time. Built on AWS HealthLake, the solution integrates diverse clinical data sources, including electronic medical records, intensive care unit sensor streams, and laboratory results within a unified cloud data lakehouse. The architecture implements FHIR-compliant streaming pipelines connecting Amazon Kinesis, AWS Lambda, Amazon Redshift Serverless and Amazon QuickSight, enabling healthcare providers to access critical patient insights through interactive dashboards powered by Generative AI for faster decision-making. Advanced features include automated schema evolution for clinical coding systems, AI-driven query optimization for responsive alerts, dynamic compute scaling during high-demand periods, and QuickSight's natural language capabilities that allow clinicians to interact with patient data through conversational queries. The system's implementation has resulted in a significant reduction of ICU transfers through early intervention, while maintaining strict HIPAA compliance through dynamic data masking. This case study offers valuable lessons on designing healthcare analytics platforms that balance performance requirements with regulatory compliance and clinical feedback integration.