Abstract

The convergence of Internet of Things (IoT), wearable medical devices, and intelligent Clinical Decision Support Systems (CDSS) was transforming personalized patient care through real-time health monitoring and adaptive diagnostics. This book chapter explores the technological architecture, data processing mechanisms, and integration frameworks that enable continuous patient surveillance and timely clinical intervention. It emphasizes the role of edge computing, explainable artificial intelligence, and modular CDSS design in ensuring scalability, transparency, and adaptability of health informatics systems. Critical attention was given to secure API-driven interoperability, real-time data filtering, and the ethical implications of machine-guided decisionmaking in clinical workflows. The chapter also addresses cybersecurity threat models and regulatory compliance challenges associated with wearable-CDSS integration. Case studies and emerging applications are examined to illustrate the clinical relevance and future potential of these systems in remote care, post-operative rehabilitation, and chronic disease management. By presenting a unified view of technological enablers, risk mitigation strategies, and deployment models, this chapter contributes to advancing intelligent, secure, and patient-centric healthcare systems.

Introduction

The integration of Internet of Things (IoT) technology with wearable medical devices has catalyzed a new era in personalized healthcare, transforming the way clinical information was acquired, interpreted, and utilized [1]. Wearable devices equipped with advanced sensors can continuously capture a wide range of physiological data such as heart rate, respiration, blood glucose levels, and physical activity [2]. These data streams, when transmitted and processed in real time, offer clinicians a granular and dynamic view of a patient’s health trajectory [3]. Unlike traditional health monitoring approaches that rely on sporadic clinical visits or laboratory results, wearable-enabled systems provide a persistent flow of information, allowing for early detection of abnormalities and timely intervention [4]. This constant vigilance creates opportunities for managing chronic diseases more effectively and customizing treatment plans according to individual health profiles [5]. To translate raw sensor data into clinically meaningful insights, wearable devices must be integrated with robust Clinical Decision Support Systems (CDSS) [6]. These systems utilize algorithms, data analytics, and machine learning models to assist healthcare professionals in making accurate, timely, and patient-specific decisions [7]. The adoption of CDSS was not solely dependent on computational capacity but also on the system's ability to align with existing clinical workflows and information systems [8]. Real-time integration ensures that alerts, recommendations, and trends are presented within the clinical context, supporting both emergency responses and long-term care strategies, explainable artificial intelligence embedded within CDSS frameworks enhances clinician trust by making the rationale behind automated suggestions transparent and understandable [9]. This fusion of wearable technology and intelligent decisionmaking infrastructure lays the groundwork for next-generation healthcare delivery models [10].