REAL-TIME HEALTHCARE DATA INTEGRATION: A CLOUD-NATIVE ARCHITECTURE FOR MERGING EHR AND IOT DEVICE DATA

Abstract

The increasing complexity of modern healthcare systems demands sophisticated solutions for integrating diverse data streams from Electronic Health Records (EHR) and Internet of Things (IoT) medical devices. This article proposes a novel cloud-native architecture that aims to address the critical challenges of real-time healthcare data integration while maintaining strict compliance with privacy regulations. The proposed framework introduces an event-driven, microservices-based approach organized in a three-tier processing pipeline, designed to enable seamless integration of heterogeneous data formats from various clinical sources. At its core, the architecture features a dynamic schema mapping system coupled with automated FHIR (Fast Healthcare Interoperability Resources) conversion capabilities, intended to facilitate real-time data harmonization across disparate healthcare systems. The proposed implementation would leverage containerized services orchestrated through Kubernetes, incorporating automatic scaling mechanisms to handle variable workloads while targeting consistent sub-second processing latencies. A significant contribution of this work is the proposed development of a privacy-preserving protocol that aims to ensure HIPAA compliance throughout the data lifecycle while enabling real-time analytics and clinical decision support. Based on existing healthcare implementations [13, 14], this architecture could potentially improve data integration efficiency and clinical workflow optimization compared to traditional approaches. The proposed solution's versatility could support various healthcare settings, including acute care hospitals, remote patient monitoring systems, and telemedicine platforms.