review · Diagnostics
Chronic diseases are increasingly prevalent, placing significant logistical and financial strain on patients and medical facilities through frequent hospital visits. Advances in wearable sensors and communication protocols have driven the development of remote patient monitoring systems. These platforms gather vital signs using invasive and non-invasive methods and transmit the data to physicians in real time, supporting timely clinical decisions. A systematic review of fifty-six publications across five academic databases assessed the integration of artificial intelligence, the internet of things, cloud computing, and wireless body area networks in these architectures. The gathered evidence confirms that remote patient monitoring enhances healthcare delivery, accelerates diagnosis speed, and lowers associated medical costs. Additionally, an examination of a chronic disease monitoring setup illustrates enhanced solutions for implementing continuous remote healthcare delivery.
Managing chronic illnesses traditionally demands recurrent hospital appointments, overwhelming clinics and inconveniencing patients. Remote patient monitoring systems leverage wearable devices and artificial intelligence to track health data continuously from home. This shift offers a reliable way to accelerate medical diagnoses, reduce healthcare expenses, and support clinicians in making timely interventions, ultimately easing the operational burdens on healthcare institutions.
Remote patient monitoring technologies target healthcare providers and clinicians managing long-term chronic conditions through connected wearable sensors and digital decision-support tools. Because this review synthesises existing literature across fifty-six studies and explores a chronic disease monitoring case study rather than validating a specific commercial device, the underlying technology spans early-stage conceptual architectures to applied systems requiring further integration before full clinical deployment.
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Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.
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DOI: 10.3390/diagnostics11040607
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