Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2021 Mar 29;11(4):607.
doi: 10.3390/diagnostics11040607.

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Nora El-Rashidy  1 , Shaker El-Sappagh  2   3 , S M Riazul Islam  4 , Hazem M El-Bakry  5 , Samir Abdelrazek  5
Affiliations
Review

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Nora El-Rashidy et al. Diagnostics (Basel). .

Abstract

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.

Keywords: AI; clinical-decision support system; cloud computing; electronic health; electronic health record; internet of things; remote patient monitoring; wireless body area network.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Steps used to select articles.
Figure 2
Figure 2
(a) Number of articles per year; (b) Distribution of RPMs according to diseases.
Figure 3
Figure 3
The general architecture of RPMs.
Figure 4
Figure 4
Fog computing layers in RPMs.
Figure 5
Figure 5
Case study for monitoring system for chronic disease patients.
See this image and copyright information in PMC

References

    1. Raghupathi W., Raghupathi V. An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach to Public Health. Int. J. Environ. Res. Public Health. 2018;15:431. doi: 10.3390/ijerph15030431. - DOI - PMC - PubMed
    1. Waqialla M., Alshammari R., Razzak M.I. An ontology for remote monitoring of cardiac implantable electronic devices; Proceedings of the 2015 3rd International Conference on Computer, Communication, Control and Information Technology; Hooghly, India. 7–8 February 2015; pp. 520–523.
    1. Rahim A., Forkan M., Khalil I. A Probabilistic model for early prediction of abnormal clinical events using vital sign correlations in home-based monitoring; Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications; Sydney, NSW, Australia. 14–19 March 2016.
    1. Zhanwei D., Yongjian Y. Semi-automatic remote medicine monitoring system of mobile users. China Commun. 2015;12:134–142. doi: 10.1109/CC.2015.7366236. - DOI
    1. Jackson Healthcare . Physician Trends 2016 Report. Jackson Healthcare; Alpharetta, GA, USA: 2016. pp. 1–60.

LinkOut - more resources