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Healthcare
Special Issues
Telehealth and Remote Patient Monitoring
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Telehealth and Remote Patient Monitoring

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "TeleHealth and Digital Healthcare".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 7230

Special Issue Editor

Dr. Ricky C. Leung

E-Mail Website
Guest Editor
1. School of Public Health, University at Albany, Albany, NY 12222, USA
2. NUS Business School, National University of Singapore, Singapore 119245, Singapore
Interests: artificial intelligence (AI); machine learning (ML); deep learning; AI/ML applications; health information technology; health analytics; global health; organization and management theory; technological innovation; Internet of Things; health security; big data; electronic health records; social media and mobile health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is increasingly being integrated into telehealth and remote patient monitoring, and the potential benefits are substantial. This Special Issue of the journal Healthcare focuses on the application of AI in telehealth and remote patient monitoring, exploring the latest research, developments, and challenges in this rapidly evolving field. 

One of the key advantages of AI in telehealth and remote patient monitoring is the ability to collect and analyze vast amounts of patient data in real time. AI algorithms can quickly identify patterns and anomalies in patient data, enabling healthcare providers to make more informed decisions

and intervene early when necessary. This can lead to improved patient outcomes, reduced hospital readmissions, and better overall quality of care. 

Relevant topics for this Special Issue include but are not limited to: 

  1. The use of AI in telehealth and remote patient monitoring for chronic disease management;
  2. AI-assisted diagnosis can help physicians make more accurate diagnoses by analyzing medical images, interpreting lab results, and identifying patterns in patient data. What are potential challenges?
  3. AI-enabled tools that can help patients manage their conditions more effectively. How can providers address such challenges as data interoperability, patient engagement, and reimbursement?
  4. What are unique ethical and privacy considerations surrounding the use of AI in telehealth and remote patient monitoring?
  5. Other relevant topics. 

In short, this Special Issue of Healthcare will provide a comprehensive overview of the latest developments in AI-enabled telehealth and remote patient monitoring. Articles should highlight the potential benefits of AI in healthcare whilst also addressing the challenges and ethical considerations that must be taken into account. As the use of AI in healthcare continues to evolve, this Special Issue provides a valuable resource for researchers, clinicians, and policymakers.

Dr. Ricky C. Leung
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • computational methodology
  • big data
  • neural networks
  • deep learning

Published Papers (5 papers)

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Research

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10 pages, 226 KiB  
Article
A Delphi Study on Identifying Competencies in Virtual Healthcare for Healthcare Professionals
by Ibrahim Mubarak Al Baalharith and Ahmad Eissa Aboshaiqah
Healthcare 2024, 12(7), 739; https://doi.org/10.3390/healthcare12070739 - 29 Mar 2024
Viewed by 686
Abstract
Background: Virtual care adoption accelerated during the COVID-19 pandemic, highlighting the need for healthcare professionals to develop relevant competencies. However, limited evidence exists on the core competencies required for quality virtual care delivery. Objective: This study aimed to identify the critical competencies physicians, [...] Read more.
Background: Virtual care adoption accelerated during the COVID-19 pandemic, highlighting the need for healthcare professionals to develop relevant competencies. However, limited evidence exists on the core competencies required for quality virtual care delivery. Objective: This study aimed to identify the critical competencies physicians, nurses, and other health professionals need for adequate virtual care provision in Saudi Arabia using a Delphi method. Methods: A 3-round Delphi technique was applied with a panel of 42 experts, including policymakers, healthcare professionals, academicians, and telehealth specialists. In Round 1, an open-ended questionnaire elicited competencies needed for virtual care. The competencies were distilled and rated for importance in Rounds 2 and 3 until consensus was achieved. Results: Consensus emerged on 151 competencies across 33 domains. The most prominent domains were communication (15 competencies), professionalism (13), leadership (12), health informatics (5), digital literacy (5), and clinical expertise (11). Full article
(This article belongs to the Special Issue Telehealth and Remote Patient Monitoring)
11 pages, 1348 KiB  
Article
Assessing Tele-Oral Medicine in Saudi Arabia: A Cross-Sectional Study on Specialists’ Experiences and Effectiveness in Oral Healthcare
by Sara Akeel, Soulafa Almazrooa, Sarah Alfarabi Ali, Nada A. Alhindi, Sana Alhamed, Osama M. Felemban, Ghada Mansour, Dania Sabbahi, Nada Binmadi and Hani Mawardi
Healthcare 2023, 11(23), 3089; https://doi.org/10.3390/healthcare11233089 - 2 Dec 2023
Cited by 1 | Viewed by 993
Abstract
Introduction: Teledentistry is an emerging tool to exchange medical information and clinical images to facilitate the diagnosis, prevention, and treatment of oral diseases and patient assurance and education. Considering the shortage of oral medicine specialists in Saudi Arabia, this study aims to assess [...] Read more.
Introduction: Teledentistry is an emerging tool to exchange medical information and clinical images to facilitate the diagnosis, prevention, and treatment of oral diseases and patient assurance and education. Considering the shortage of oral medicine specialists in Saudi Arabia, this study aims to assess the experiences of dental specialists with tele-oral medicine and its potential applicability in addressing this shortage. Materials and methods: This was a pilot, cross-sectional study conducted among specialists in the field of oral medicine from January 2020 to March 2020. A total of 16 preselected cases with oral lesions, including clinical history and images, were developed, validated, and shared via email with study participants. Each case included questions on differential diagnosis, provisional diagnosis, and management. The responses were recorded, analyzed, and presented as means and percentages. Results: A total of 49 subjects participated in this study and more than half were under 40 years of age and two-thirds were women. A total of 23 participants had prior experience with tele-oral medicine, mainly via WhatsApp (95.7%), and these cases were received from patients, their families, friends, or other dentists. For all study cases, the correct diagnosis score ranged between 73.50 and 100%, and correct management ranged between 51 and 98%. Conclusions: Tele-oral medicine is an effective tool that may play an important role in patient management in rural regions with a shortage of oral medicine services. Further studies with larger sample sizes and in collaboration with international centers are warranted to confirm these findings. Full article
(This article belongs to the Special Issue Telehealth and Remote Patient Monitoring)
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21 pages, 1127 KiB  
Article
DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy
by Jesús Jaime Moreno Escobar, Oswaldo Morales Matamoros, Erika Yolanda Aguilar del Villar, Hugo Quintana Espinosa and Liliana Chanona Hernández
Healthcare 2023, 11(16), 2295; https://doi.org/10.3390/healthcare11162295 - 14 Aug 2023
Viewed by 1241
Abstract
In Mexico, according to data from the General Directorate of Health Information (2018), there is an annual incidence of 689 newborns with Trisomy 21, well-known as Down Syndrome. Worldwide, this incidence is estimated between 1 in every 1000 newborns, approximately. That is why [...] Read more.
In Mexico, according to data from the General Directorate of Health Information (2018), there is an annual incidence of 689 newborns with Trisomy 21, well-known as Down Syndrome. Worldwide, this incidence is estimated between 1 in every 1000 newborns, approximately. That is why this work focuses on the detection and analysis of facial emotions in children with Down Syndrome in order to predict their emotions throughout a dolphin-assisted therapy. In this work, two databases are used: Exploratory Data Analysis, with a total of 20,214 images, and the Down’s Syndrome Dataset database, with 1445 images for training, validation, and testing of the neural network models. The construction of two architectures based on a Deep Convolutional Neural Network manages an efficiency of 79%, when these architectures are tested with a large reference image database. Then, the architecture that achieves better results is trained, validated, and tested in a small-image database with the facial emotions of children with Down Syndrome, obtaining an efficiency of 72%. However, this increases by 9% when the brain activity of the child is included in the training, resulting in an average precision of 81%. Using electroencephalogram (EEG) signals in a Convolutional Neural Network (CNN) along with the Down’s Syndrome Dataset (DSDS) has promising advantages in the field of brain–computer interfaces. EEG provides direct access to the electrical activity of the brain, allowing for real-time monitoring and analysis of cognitive states. Integrating EEG signals into a CNN architecture can enhance learning and decision-making capabilities. It is important to note that this work has the primary objective of addressing a doubly vulnerable population, as these children also have a disability. Full article
(This article belongs to the Special Issue Telehealth and Remote Patient Monitoring)
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15 pages, 1505 KiB  
Article
Exploring Older Adults’ Willingness to Install Home Surveil-Lance Systems in Taiwan: Factors and Privacy Concerns
by Chang-Yueh Wang and Fang-Suey Lin
Healthcare 2023, 11(11), 1616; https://doi.org/10.3390/healthcare11111616 - 1 Jun 2023
Cited by 2 | Viewed by 1062
Abstract
Taiwan has a rapidly increasing aging population with a considerably high life expectancy rate, which poses challenges for healthcare and medical systems. This study examines three key factors: safety concerns, family expectations, and privacy concerns, and their influence on surveillance system installation decisions. [...] Read more.
Taiwan has a rapidly increasing aging population with a considerably high life expectancy rate, which poses challenges for healthcare and medical systems. This study examines three key factors: safety concerns, family expectations, and privacy concerns, and their influence on surveillance system installation decisions. A cross-sectional study was conducted involving a group of physically active older adults in Taiwan, using a questionnaire to collect data on the reasons for in-stalling a surveillance system and preferences for three image privacy protection techniques: blurring the face and transformation to a 2D or 3D character. The study concluded that while safety concerns and family expectations facilitate the adoption of surveillance systems, privacy concerns serve as a significant barrier. Furthermore, older adults showed a clear preference for avatar-based privacy protection methods over simpler techniques, such as blurring. The outcomes of this research will be instrumental in shaping the development of privacy-conscious home surveillance technologies, adeptly balancing safety and privacy. This understanding can pave the way for technology design that skillfully balances privacy concerns with remote monitoring quality, thereby enhancing the well-being and safety of this demographic. These results could possibly be extended to other demographics as well. Full article
(This article belongs to the Special Issue Telehealth and Remote Patient Monitoring)
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11 pages, 487 KiB  
Concept Paper
Using AI–ML to Augment the Capabilities of Social Media for Telehealth and Remote Patient Monitoring
by Ricky Leung
Healthcare 2023, 11(12), 1704; https://doi.org/10.3390/healthcare11121704 - 10 Jun 2023
Cited by 4 | Viewed by 2573
Abstract
Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve telehealth, remote [...] Read more.
Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve telehealth, remote patient monitoring, and the well-being of individuals and communities. Previous research has revealed several trends in AI–ML adoption: First, AI can be used to enhance social media marketing. Drawing on sentiment analysis and related tools, social media is an effective way to increase brand awareness and customer engagement. Second, social media can become a very useful data collection tool when integrated with new AI–ML technologies. Using this function well requires researchers and practitioners to protect users’ privacy carefully, such as through the deployment of privacy-enhancing technologies (PETs). Third, AI–ML enables organizations to maintain a long-term relationship with stakeholders. Chatbots and related tools can increase users’ ability to receive personalized content. The review in this paper identifies research gaps in the literature. In view of these gaps, the paper proposes a conceptual framework that highlights essential components for better utilizing AI and ML. Additionally, it enables researchers and practitioners to better design social media platforms that minimize the spread of misinformation and address ethical concerns more readily. It also provides insights into the adoption of AI and ML in the context of remote patient monitoring and telehealth within social media platforms. Full article
(This article belongs to the Special Issue Telehealth and Remote Patient Monitoring)
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