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Sustainable Digital Transformation in Health in Times of the COVID-19 Pandemic: Technology-Driven Research, Innovation, and Training

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Health, Well-Being and Sustainability".

Deadline for manuscript submissions: closed (26 March 2023) | Viewed by 11228

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Special Issue Editors

Prof. Dr. Miltiadis D. Lytras

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Guest Editor

1. School of Business, Deree—The American College of Greece, 6 Gravias Street, GR-153 42 Aghia Paraskevi Athens, Greece
2. College of Engineering, Effat University, Jeddah, Saudi Arabia
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management
Special Issues, Collections and Topics in MDPI journals

Dr. Abdulrahman Housawi

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Guest Editor

Planning and Organizational Excellence Administration, Saudi Commission for Health Specialties, Riyadh, Saudi Arabia
Interests: medical education; quality in training; innovation; saudi commission for health specialties; smart healthcare
Special Issues, Collections and Topics in MDPI journals

Dr. Basim Alsaywid

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Guest Editor

1. Planning and Organizational Excellence Administration, Saudi Commission for Health Specialties, Riyadh, Saudi Arabia
2. College of Medicine, King Saud Bin-Abdul-Aziz University for Health Sciences, Jeddah, Saudi Arabia
3. Urology Section, Department of Surgery, King Abdulaziz Medical City, Ministry of National Guard, Jeddah, Saudi Arabia
Interests: medical education; quality in training; innovation; saudi commission for health specialties; smart healthcare
Special Issues, Collections and Topics in MDPI journals

Dr. Wesam Abuznadah

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Guest Editor

Executive Presidency of Academic Affairs, Saudi Commission for Health Specialties, Riyadh, Saudi Arabia.
Interests: medical education; quality in training; innovation; smart healthcare

Special Issue Information

Dear Colleagues,

Recent developments in emerging technologies and information system research provide a wide range of value-adding components for embedded systems in health and life sciences. From a sustainability point of view, the design, implementation, and performance of innovative services for the provision of efficient health services is a key direction toward personalized medicine. In the times of the COVID-19 pandemic, the agenda of our Special Issue is quite timely. The following technological enablers are in the focus of our Special Issue:

Artificial Intelligence and machine learning;
Internet of Things;
Cloud computing;
Bioinformatics;
Advanced data mining and sentiment analysis;
Image processing;
Social networking;
Big data analytics;
Robotics;
Nanotechnology;
3D bio-printing;
Medical imaging;
Wearables;
Cybersecurity for medical records.

These technologies are some of the key enablers of a new era of Innovations in health and life sciences.

In this Special Issue, we are interested in promoting progressive, novel technology-driven research, innovation, and training in health and life sciences from a sustainability point of view. We are looking for innovative research works that uncover the potential social impact and added value of the emerging technologies in the health and life sciences toward sustainable digital transformation in health. With a special emphasis on medical informatics and innovation, we intend to contribute to the following directions:

To promote recent sound research on the impact of emerging technologies in health and life specialties and disciplines;
To contribute to the body of knowledge by promoting sound methodological approaches for technology-driven research, innovation, and training in health and life sciences;
To communicate best practices and key lessons learnt from the implementation of innovative systems, services, and applications in real world contexts, with special emphasis on medical innovation;
To invite to the relevant scientific debate diverse communities from different domains of human activity, including bioinformatics, health sciences, medical training, information systems and computer sciences education, etc.;
To contribute to the discipline by synthesizing complementary approaches, limitations, and key findings.

Topics include but are not limited to the following:

Medical and health-related innovation and research as it is applied by the following technologies:
- Artificial Intelligence and machine learning;
- Internet of Things;
- Cloud computing;
- Bioinformatics;
- Advanced data mining and sentiment analysis;
- Image processing;
- Social networking;
- Big data analytics;
- Robotics;
- Nanotechnology;
- 3D bio-printing;
- Medical imaging;
- Wearables;
Design of progressive, efficient systems for quality in health and life sciences training and education;
Technology-enhanced training for medical education;
Quality initiatives in health and life sciences domains;
Medical analytics and enhanced decision making;
User satisfaction in health training programs;
Special focus on:
- Augmented and virtual reality in healthcare;
- Decrease on hype and focus on real world trusted, reliable. and efficient artificial intelligence products and services;
- Progressive medical imaging research;
- Integrating wearable devices in to patient care.

Prof. Dr. Miltiadis D. Lytras
Dr. Abdulrahman Housawi
Dr. Basim Alsaywid
Dr. Wesam Abuznadah
Guest Editors

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. Sustainability 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 2400 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

medical innovation
medical informatics
AI in healthcare
personalized medicine
wearables for health
IoT
Artificial Intelligence and machine learning
Internet of Things
cloud computing
bioinformatics
advanced data mining and sentiment analysis
image processing
social networking
big data analytics
robotics
nanotechnology
3D bio-printing
medical imaging

Published Papers (4 papers)

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Research

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16 pages, 3927 KiB

Open AccessArticle

Digital Transformation in Epilepsy Diagnosis Using Raw Images and Transfer Learning in Electroencephalograms

by Marlen Sofía Muñoz, Camilo Ernesto Sarmiento Torres, Ricardo Salazar-Cabrera, Diego M. López and Rubiel Vargas-Cañas

Sustainability 2022, 14(18), 11420; https://doi.org/10.3390/su141811420 - 12 Sep 2022

Cited by 2 | Viewed by 1435

Abstract

Epilepsy diagnosis is a medical care process that requires considerable transformation, mainly in developed countries, to provide efficient and effective care services taking into consideration the low number of available neurologists, especially in rural areas. EEG remains the most common test used to diagnose epilepsy. In recent years, there has been an increase in deep learning techniques to analyze electroencephalograms (EEG) to detect epileptiform events. These types of techniques support the epilepsy diagnostic processes performed by neurologists. There have been several approaches such as biomedical signal processing, analysis of characteristics extracted from the signals, and image analysis to detect epileptiform events. Most of the works reported in the literature, which use images, transformed the signals into a two-dimensional space interpreted as an image. However, only a few of them use the raw EEG image. This paper presents a computational model for detecting epileptiform events from raw EEG images, using convolutional neural networks and a transfer learning approach. To perform this work, 100 pediatric EEGs were collected, noting six characteristics of epileptiform events in each exam: spikes, poly-spikes, spike-and-wave, sharp waves, periodic, and a combination of them. Then, pre-trained convolutional neural networks were used, which, through transfer learning techniques, were retrained to classify possible events. The model’s performance was evaluated in terms of precision, accuracy, and Mathews’ correlation coefficient. The model offered a performance above 95% accuracy for binary classification and above 87% for multi-class classification. These results demonstrated that identifying epileptiform events from raw EEG images combined with deep learning techniques such as transfer learning is feasible. Significance: The proposed method for the evaluation of EEG tests, as a support tool for the diagnosis of epilepsy, can help to reduce the time of reading EEGs, which is very important, especially in developing countries with a limitation of a specialist in neurology. Full article

(This article belongs to the Special Issue Sustainable Digital Transformation in Health in Times of the COVID-19 Pandemic: Technology-Driven Research, Innovation, and Training)

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14 pages, 1141 KiB

Open AccessArticle

Sustainable Transformation of Consumer Behavior—Vector Modeling in Determining the Decision to Choose a Medical Service in the Context of COVID-19

by Raluca-Giorgiana (Popa) Chivu, Ionuț-Claudiu Popa, Adrian Mociu, Petre-Sorin Savin, Robert-Ionuț Popa and Anca-Olguța Orzan

Sustainability 2021, 13(23), 13025; https://doi.org/10.3390/su132313025 - 24 Nov 2021

Cited by 1 | Viewed by 1565

Abstract

Consumer behavior has been a topic of interest since ancient times, from the point of view of both the socio-human sciences (psychology, sociology) and the economy; the consumer is seen as a producer of income. With the emergence and development of services in the economic sphere, the consumer has become the beneficiary of those services, with the same role of generating revenue and profits for suppliers. In the field of healthcare provision, the analysis of consumer behavior is a delicate subject because there are no standard behavioral models (not only due to the confidentiality of information that does not allow data to be obtained by researchers but also due to individual particularities regarding the need for health services). Moreover, in the context of COVID-19, the attitude of their beneficiaries toward health services has changed compared to what experts have recorded in the past, as pandemic restrictions and fear of the new virus have led to changes in behavior and people’s decisions regarding health services. For this study, quantitative research was conducted, complemented by a conceptual, behavioral model, on the satisfaction levels of health service beneficiaries in the context of COVID-19. In this research, numerous variables were analyzed regarding the protection measures implemented by medical institutions whose services have benefited from the perceived urgency. The research was conducted in Romania on a sample of 100 people from the southeast region, in order to observe to what extent the analyzed factors inform their decisions. According to government statistics, only 80% of Romania’s population uses medical services. Given the sample size, the PLS-SEM method of analysis was used, which, according to the recommendations identified in the literature, is the most appropriate technique for small samples due to the individual method of analyzing the links between variables, leading to significant results. Technological evolution and the digitization of some procedures within the medical services (such as making online appointments and online or telephone consultations) represents only one factor analyzed in the process of determining the satisfaction levels of the beneficiaries of health services in the context of the COVID-19 pandemic. Full article

(This article belongs to the Special Issue Sustainable Digital Transformation in Health in Times of the COVID-19 Pandemic: Technology-Driven Research, Innovation, and Training)

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Review

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17 pages, 1291 KiB

Open AccessReview

Virtual Reality in the Treatment of Patients with Overweight and Obesity: A Systematic Review

by Amal Al-Rasheed, Eatedal Alabdulkreem, Mai Alduailij, Mona Alduailij, Wadee Alhalabi, Seham Alharbi and Miltiadis D. Lytras

Sustainability 2022, 14(6), 3324; https://doi.org/10.3390/su14063324 - 11 Mar 2022

Cited by 9 | Viewed by 4459

Abstract

Obesity is one of the world’s most serious health issues. Therefore, therapists have looked for methods to fight obesity. Currently, technology-based intervention options in medical settings are very common. One such technology is virtual reality (VR) which has been used in the treatment of obesity since the late 1990s. The main objective of this study is to review the literature on the use of VR in the treatment of obesity and overweight to better understand the role of VR-based interventions in this field. To this end, four databases (PubMed, Medline, Scopus, and Web of Science) were searched for related publications from 2000 to 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). From the 645 articles identified, 24 were selected. The main strength of this study is that it is the first systematic review to focus completely on the use of VR in the treatment of obesity. It includes most research in which VR was utilized to carry out the intervention. Although several limitations were detected in the reviewed studies, the findings of this review suggest that employing VR for self-monitoring of diet, physical activity, and/or weight is effective in supporting weight loss as well as improving satisfaction of body image and promoting health self-efficacy in overweight or obese persons. Full article

(This article belongs to the Special Issue Sustainable Digital Transformation in Health in Times of the COVID-19 Pandemic: Technology-Driven Research, Innovation, and Training)

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Other

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30 pages, 4356 KiB

Open AccessHypothesis

Extending the Unified Theory of Acceptance and Use of Technology for COVID-19 Contact Tracing Application by Malaysian Users

by Mahmood Alshami, Rawad Abdulghafor and Abdulaziz Aborujilah

Sustainability 2022, 14(11), 6811; https://doi.org/10.3390/su14116811 - 02 Jun 2022

Cited by 5 | Viewed by 2240

Abstract

The Malaysian government has mobilized its strength to confront the current COVID-19 pandemic and has sought to develop and implement a digital contact tracking application, making it an integral part of the exit strategy from the lockdown. These applications record which users have been near one another. When a user is confirmed with COVID-19, app users who have recently been near this person are notified. The effectiveness of these applications is determined by the users’ willingness to install and use them. Therefore, this research aims at identifying the factors that would stimulate or slow down the adoption of a contact-tracing app. It proposes solutions to mitigate the impact of the factors affecting the user’s acceptance of COVID-19 Digital Contact Tracing Apps. A quantitative approach was followed in this research, where an electronic survey was spread in Malaysia, for the objective of data collection, considering the previous discussion of the results. Then, using PLS-SEM, the collected data were analyzed statistically. The findings of this study indicate that the unified theory of acceptance and use of technology (UTAUT) factors (Performance Expectancy, Effort Expectancy, Social Influence, Facilities Condition) were significant predictors of MySejahtera application adoption among citizens in Malaysia. On the other hand, the factors of app-related privacy concern were found to be insignificant for MySejahtera application adoption. Full article

(This article belongs to the Special Issue Sustainable Digital Transformation in Health in Times of the COVID-19 Pandemic: Technology-Driven Research, Innovation, and Training)

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Sustainable Digital Transformation in Health in Times of the COVID-19 Pandemic: Technology-Driven Research, Innovation, and Training

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