DevOps-Driven Real-Time Health Analytics: A Scalable Framework for Wearable IoT Data

Author(s)	Jesu Marcus Immanuvel Arockias
Country	United States
Abstract	The rapid adoption of wearable health devices and IoT sensors has given us real-time health monitoring like never before, with opportunities for early disease detection, personalized treatment, and proactive healthcare interventions. However, scalability, latency, security, and regulatory compliance pose significant challenges. This white paper explores how DevOps methodologies – including continuous integration and continuous deployment (CI/CD) pipelines, machine learning operations (MLOps), cloud-native architectures, and security automation (DevSecOps) – enable real-time health data streaming and analytics. A cardiac monitoring case study included in this paper illustrates measurable benefits, including sub-second anomaly detection, 95% accuracy, and a fivefold improvement in data processing throughput. Furthermore, the scalable framework is adaptable to other medical conditions, such as EEG-based early stroke detection. By adopting DevOps best practices, healthcare providers can accelerate AI-driven insights, streamline compliance, and improve patient outcomes at scale.
Keywords	DevOps, DevSecOps, Healthcare Analytics, IoT, Wearables
Field	Computer > Data / Information
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-02-21
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.37358
Short DOI	https://doi.org/g85szr

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International Journal For Multidisciplinary Research