Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/11/4/607 |
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| author | Nora El-Rashidy Shaker El-Sappagh S. M. Riazul Islam Hazem M. El-Bakry Samir Abdelrazek |
| author_facet | Nora El-Rashidy Shaker El-Sappagh S. M. Riazul Islam Hazem M. El-Bakry Samir Abdelrazek |
| author_sort | Nora El-Rashidy |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:48:07Z |
| format | Article |
| id | doaj.art-22d2d07e6685476ba32de558ab51f460 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T12:48:07Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-22d2d07e6685476ba32de558ab51f4602023-11-21T13:17:09ZengMDPI AGDiagnostics2075-44182021-03-0111460710.3390/diagnostics11040607Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and ChallengesNora El-Rashidy0Shaker El-Sappagh1S. M. Riazul Islam2Hazem M. El-Bakry3Samir Abdelrazek4Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, EgyptCentro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, SpainDepartment of Computer Science and Engineering, Sejong University, Seoul 05006, KoreaInformation Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura 13518, EgyptInformation Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura 13518, EgyptChronic 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.https://www.mdpi.com/2075-4418/11/4/607electronic healthelectronic health recordclinical-decision support systemAIremote patient monitoringcloud computing |
| spellingShingle | Nora El-Rashidy Shaker El-Sappagh S. M. Riazul Islam Hazem M. El-Bakry Samir Abdelrazek Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges Diagnostics electronic health electronic health record clinical-decision support system AI remote patient monitoring cloud computing |
| title | Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges |
| title_full | Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges |
| title_fullStr | Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges |
| title_full_unstemmed | Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges |
| title_short | Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges |
| title_sort | mobile health in remote patient monitoring for chronic diseases principles trends and challenges |
| topic | electronic health electronic health record clinical-decision support system AI remote patient monitoring cloud computing |
| url | https://www.mdpi.com/2075-4418/11/4/607 |
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