TL;DR
The "Sensing-Integration-Decision" framework is adopted to explore how sensing technologies expand the clinical observation capabilities of nurses and how clinical decision support systems facilitate the conversion of fragmented data into actionable care insights.
This paper synthesizes the latest literature on the intersection of mobile health, machine learning, and physiological tracking, with a primary focus on heart rate variability (HRV) and associated biochemical markers, such as cortisol, salivary alpha-amylase, and interleukins.
Healthcare is generating more continuous, real-time data than ever through sensors, wearable devices, remote patient monitoring and connected care platforms. The opportunity is significant, but collecting data alone does not improve outcomes. Information must be translated into timely, clinically meaningful action with...
The internet of medical things (IoMT) is a structured integrative review of the internet of things (IoT) that focuses on the medical field, specifically on the integration of physiological sensors, medical devices, communication networks, and the clinical information systems to monitor and track patients' health and co...
Examination of key areas where IT impacts healthcare, including electronic health records, telemedicine, clinical decision support, wearable and remote monitoring devices, big data analytics, and artificial intelligence, concludes that while IT has led to measurable improvements in health outcomes and system efficiency...
Early recognition of patient deterioration remains a cornerstone of high-quality nursing care and is essential for preventing avoidable morbidity, mortality, and unplanned intensive care admissions. Despite advances in physiological monitoring, delays in recognizing subtle clinical changes continue to contribute to adv...
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