An Event-Driven Cloud-Native Wearable Analytics Framework for Real-Time Clinical Workloads

2026-08-11

AI Summary

This work addresses the integration challenges posed by consumer-grade wearable devices in clinical settings—stemming from device heterogeneity, proprietary data formats, and regulatory compliance requirements—by proposing an event-driven, cloud-native high-throughput system. The system leverages a cross-platform mobile application to collect high-frequency physiological data and employs a microservices architecture coupled with a stream processing engine to enable FHIR-compliant data standardization, real-time analytics, and end-to-end machine learning support. It introduces a novel dependency-aware FHIR minimization strategy that significantly reduces storage overhead while preserving lossless data reconstruction, thereby establishing a vendor-agnostic, scalable clinical integration framework. Evaluated performance demonstrates support for up to 50 ingestion requests per second with a median response latency under 8 milliseconds, satisfying stringent low-latency monitoring demands while adhering to healthcare regulatory standards.

Abstract

Continuous physiological monitoring using consumer-grade wearables offers a transformative opportunity for clinical care and research, yet integration remains hindered by device heterogeneity, proprietary data formats, and strict regulatory requirements. We present an event-driven, cloud-native system designed to ingest, normalize, and analyze high-frequency vital signs from wearables at scale and without vendor lock-in. The system design proposes a multi-layered microservice architecture using cluster orchestration. Data acquisition is handled via a cross-platform mobile application that leverages native health frameworks, ensuring compatibility across fragmented device ecosystems. To address interoperability, we implement an event-driven transformation pipeline using stream processing engines and specialized services to map raw measurements to the FHIR standard for medical interoperability. Our novel dependency-aware FHIR minimization scheme reduces storage overhead while maintaining lossless resource reconstruction. Furthermore, the platform integrates a modular data analytics and machine learning layer based on a medallion lakehouse architecture, supporting the full machine learning lifecycle from real-time stream processing to model serving. Performance evaluation demonstrates that the ingestion pipeline sustains 50 full ingestion requests per second with median response times under 8 ms, satisfying the low-latency requirements for real-time patient monitoring. Our open-source implementation adheres to regulatory compliance standards through role-based access control and secure service-to-service communication, providing a robust foundation for deploying wearable-based monitoring in institutional healthcare settings for clinical decision support and research workloads.

Problem

• wearable integration
• clinical interoperability
• real-time monitoring
• device heterogeneity
• regulatory compliance

Innovation

• event-driven architecture
• cloud-native
• FHIR interoperability
• wearable analytics
• medallion lakehouse