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Review
. 2022 Oct 5;12(10):1656.
doi: 10.3390/jpm12101656.

A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment

Ștefan Sebastian Busnatu  1 , Adelina-Gabriela Niculescu  2 , Alexandra Bolocan  1 , Octavian Andronic  1 , Anca Mihaela Pantea Stoian  1 , Alexandru Scafa-Udriște  1 , Ana Maria Alexandra Stănescu  1 , Dan Nicolae Păduraru  1 , Mihnea Ioan Nicolescu  1 , Alexandru Mihai Grumezescu  2   3   4 , Viorel Jinga  1
Affiliations
Review

A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment

Ștefan Sebastian Busnatu et al. J Pers Med. .

Abstract

With the prevalence of digitalization in all aspects of modern society, health assessment is becoming digital too. Taking advantage of the most recent technological advances and approaching medicine from an interdisciplinary perspective has allowed for important progress in healthcare services. Digital health technologies and biotelemetry devices have been more extensively employed for preventing, detecting, diagnosing, monitoring, and predicting the evolution of various diseases, without requiring wires, invasive procedures, or face-to-face interaction with medical personnel. This paper aims to review the concepts correlated to digital health, classify and describe biotelemetry devices, and present the potential of digitalization for remote health assessment, the transition to personalized medicine, and the streamlining of clinical trials.

Keywords: Internet of Things; artificial intelligence; biotelemetry devices; digital health; medical biotechnology; mobile health; personalized medicine; precision medicine; remote patient monitoring; telemedicine.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Relationship between artificial intelligence, machine learning, and deep learning. Adapted from an open-access source [33].
Figure 2
Figure 2
Block diagram of a biotelemetry system. Created based on information from [18,37].
Figure 3
Figure 3
Classification of biotelemetry devices. Created based on information from literature references [36,38].
Figure 4
Figure 4
Schematic summary of the roles of wearable sensor technology and predictive analytics for monitoring COVID-19. (a) Physiological metrics currently capable of being measured from commercial wearable sensors. (b) Changes in physiological metrics that can be inputted into an early detection algorithm for COVID-19 monitoring to ensure that the true positive rate is robust to support the use of the analytics for real-time clinical decision making. (c) Uses of integrated analytics in monitoring COVID-19 for individual or population health. Abbreviations: HRV—heart rate variability; Resp Rate—respiration rate; SpO2—blood oxygen saturation; Temp—temperature; TP—true positive; FP—false positive; TN—true negative; FN—false negative. Adapted from an open-access source [116].
See this image and copyright information in PMC

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