Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction

Objective: The rapid advancements in the Internet of Things (IoT) have allowed end users to enjoy restriction-free access to information. One of the notable developments in IoT is the introduction of wearable technologies, such as smartwatches. The growing popularity of wearable technology has made it possible for users to receive health and fitness data regardless of time or place. This study aims to examine the mediation role of artificial intelligence in physician experience toward using the medical smartwatch, particularly examining the effect of the medical smartwatch on physician satisfaction.Methods: This study utilized a deductive research approach employing a cross-sectional design. Data was collected through online questionnaires from healthcare providers, particularly physicians in the United Arab Emirates (UAE). The Structural Equation Modelling analysis (SEM) was employed to evaluate the theoretical and final path models. This study further assessed the theoretical model using the Partial Least Squares (PLS) as it offers concurrent analysis for evaluating the structural model and enhancing result accuracy.Results: Artificial Intelligence (AI) experience significantly influenced physicians’ satisfaction. Additionally, the study provided supporting, satisfying evidence for the mediating effects of AI experience.Conclusion: The study provided supporting evidence for the mediating effects of AI experience on physicians’ satisfaction. This study bridges the gap in the literature regarding the absence of studies examining physicians’ perceptions of medical smartwatch usage in the medical domain by providing a profound understanding of physicians’ satisfaction and perceptions regarding smartwatch usage in the UAE.This study bridges the gap in the literature regarding the absence of studies examining physicians’ perceptions of medical smartwatch usage by providing a profound understanding of physicians’ satisfaction and perceptions regarding smartwatch usage in the UAE

Factors Affecting Medical Students’ Acceptance of the Metaverse System in Medical Training in the United Arab Emirates

Aim: Medical training activities have been disrupted in many regions following the outbreak and rapid spread of the coronavirus disease 2019 (COVID-19) across the globe. The most affected areas include organizations’ process of leveraging high-tech medical equipment from abroad to facilitate a practical approach to learning. Also, as countries implemented COVID-19 safety regulations, it became difficult for organizations to conduct face-to-face training. Consequently, non-face-to-face learning methods have been introduced in the medical field to enable instructors to remotely engage with learners. The current research investigated the students' perceptions of the use of metaverse systems in medical training within the medical community of the United Arab Emirates (UAE). Methods: A conceptual model comprising the adoption properties of personal innovativeness, perceived enjoyment, and Technology Acceptance Model concepts was utilised. The current research targeted students in UAE medical universities. Data was obtained by conducting online surveys that were implemented in the winter semester of 2021/2022 between 15th February and 15th May 2022. 500 questionnaires were issued to students following their voluntary participation and 435 questionnaire responses were obtained i.e. an 87% response rate. The research team tested the measurement model employing Structural Equation Modeling using Smart Partial Least Squares Version (3.2.7). Results: Statistically significant associations were confirmed to exist between Personal Innovativeness (PI) influenced by both the Perceived Ease of Use (PEOU), and Perceived Usefulness (PU) (β= 0.456) and (β= 0.563) at P<0.001. The statistically significant associations involving Perceived Enjoyment (EJ) and PEOU and PU (β= 0.554, P<0.05), (β= 0.571, P<0.05) were further confirmed. Additionally, PEOU had a relationship with PU (β= 0.863, P<0.001). Eventually, PEOU and PU significantly influenced the participants’ inclination to use the metaverse technology with (β= 0.745, P<0.001) and (β= 0.416, P<0.001), respectively

The Impact of Hospital Demographic Factors on Total Quality Management Implementation: A Case Study of UAE Hospitals

Aim:  Maintaining service quality and value using quality and management tools is crucial in any organization. In essence, improving service quality boosts both efficiency of organizations and consumer pleasure. The deployment of quality development programs such as Total Quality Management (TQM) is one technique that businesses may employ to deliver exceptional customer service. The health sector, in particular, is one of the industries that require TQM adoption due to its complexity and the need for constant service improvement. TQM helps to improve service quality in health facilities through advanced clinical and administrative procedures. This research comprehensively assesses TQM levels and the impact of hospital demographics on its implementation process in hospitals in the United Arab Emirates (UAE).   Methods:  The study used a quantitative research strategy based on a survey study design. Questionnaires were used to gather primary data from respondents deployed a self-administered technique. 1850 questionnaires were delivered to the hospital's senior staff based on their number in each hospital. Of the 1850 questionnaires distributed, 1238 usable questionnaires were analyzed, yielding a response rate of 66.9%. The study used a binary logistic regression model to determine if hospital demographics affected TQM implementation. The study data were examined and analysed using version 25.0 of the SPSS software.   Results: The results show that most of the health facilities with an overall TQM between 4.12 and 4.82 were utilized, governmental, accredited and utilized and large hospitals, while the hospitals with a mean between 2.91 and 3.45 were small, unaccredited private, and non-specialised. Thus, large hospitals have a higher TQM utilization rate than small hospitals. In addition, the findings of the t-test revealed that a high TQM is represented by means of 4.68, 4.67, 4.43, and 4.12 for accredited, utilized, governmental and large hospitals. The binary regression analysis also reveals similar results: large, governmental, utilized and accredited hospitals have greater chances of TQM adoption than other categories of hospitals (Exp (B): 1.2; 95%CI: 1.001 – 1.421, P< .05); (Exp (B): 1.3; 95%CI: 1.012 – 1.721, P< .05); (Exp (B): 1.5; 95%CI: 1.127 – 2.051, P< .01); and (Exp   (B): 1.5; 95%CI: 1.102 – 2.012, P< .05); correspondingly. Another observation from the results is that hospitals that implemented technological tools had a greater chance of successfully executing the TQM program than hospitals that did not utilize advanced technologies due to the limited availability of resources (Exp (B): 1.7; 95%CI: 1.332 – 2.187, P< .01). Conclusion: Even though health facilities need to adopt TQM, its implementation depends on the hospital size and demographics that significantly influence the adoption of TQM programs. However, this study will help bridge the current gap on the usage of TQM in the health context by examine the influence of demographic factors on adopting TQM in hospitals. Hence, provide adequate information to help the UAE hospital administrators appropriately execute the TQM program in the hospitals and enhance the efficacy of their operations. &nbsp

The Impact of Hospital Demographic Factors on Total Quality Management Implementation: A Case Study of UAE Hospitals

Aim:  Maintaining service quality and value using quality and management tools is crucial in any organization. In essence, improving service quality boosts both efficiency of organizations and consumer pleasure. The deployment of quality development programs such as Total Quality Management (TQM) is one technique that businesses may employ to deliver exceptional customer service. The health sector, in particular, is one of the industries that require TQM adoption due to its complexity and the need for constant service improvement. TQM helps to improve service quality in health facilities through advanced clinical and administrative procedures. This research comprehensively assesses TQM levels and the impact of hospital demographics on its implementation process in hospitals in the United Arab Emirates (UAE).   Methods:  The study used a quantitative research strategy based on a survey study design. Questionnaires were used to gather primary data from respondents deployed a self-administered technique. 1850 questionnaires were delivered to the hospital's senior staff based on their number in each hospital. Of the 1850 questionnaires distributed, 1238 usable questionnaires were analyzed, yielding a response rate of 66.9%. The study used a binary logistic regression model to determine if hospital demographics affected TQM implementation. The study data were examined and analysed using version 25.0 of the SPSS software.   Results: The results show that most of the health facilities with an overall TQM between 4.12 and 4.82 were utilized, governmental, accredited and utilized and large hospitals, while the hospitals with a mean between 2.91 and 3.45 were small, unaccredited private, and non-specialised. Thus, large hospitals have a higher TQM utilization rate than small hospitals. In addition, the findings of the t-test revealed that a high TQM is represented by means of 4.68, 4.67, 4.43, and 4.12 for accredited, utilized, governmental and large hospitals. The binary regression analysis also reveals similar results: large, governmental, utilized and accredited hospitals have greater chances of TQM adoption than other categories of hospitals (Exp (B): 1.2; 95%CI: 1.001 – 1.421, P< .05); (Exp (B): 1.3; 95%CI: 1.012 – 1.721, P< .05); (Exp (B): 1.5; 95%CI: 1.127 – 2.051, P< .01); and (Exp   (B): 1.5; 95%CI: 1.102 – 2.012, P< .05); correspondingly. Another observation from the results is that hospitals that implemented technological tools had a greater chance of successfully executing the TQM program than hospitals that did not utilize advanced technologies due to the limited availability of resources (Exp (B): 1.7; 95%CI: 1.332 – 2.187, P< .01). Conclusion: Even though health facilities need to adopt TQM, its implementation depends on the hospital size and demographics that significantly influence the adoption of TQM programs. However, this study will help bridge the current gap on the usage of TQM in the health context by examine the influence of demographic factors on adopting TQM in hospitals. Hence, provide adequate information to help the UAE hospital administrators appropriately execute the TQM program in the hospitals and enhance the efficacy of their operations.     Conflict of interest: None declare

Factors Affecting Medical Students’ Acceptance of the Metaverse System in Medical Training in the United Arab Emirates

Aim: Medical training activities have been disrupted in many regions following the outbreak and rapid spread of the coronavirus disease 2019 (COVID-19) across the globe. The most affected areas include organizations’ process of leveraging high-tech medical equipment from abroad to facilitate a practical approach to learning. Also, as countries implemented COVID-19 safety regulations, it became difficult for organizations to conduct face-to-face training. Consequently, non-face-to-face learning methods have been introduced in the medical field to enable instructors to remotely engage with learners. The current research investigated the students' perceptions of the use of metaverse systems in medical training within the medical community of the United Arab Emirates (UAE).   Methods: A conceptual model comprising the adoption properties of personal innovativeness, perceived enjoyment, and Technology Acceptance Model concepts was utilised. The current research targeted students in UAE medical universities. Data was obtained by conducting online surveys that were implemented in the winter semester of 2021/2022 between 15th February and 15th May 2022. 500 questionnaires were issued to students following their voluntary participation and 435 questionnaire responses were obtained i.e. an 87% response rate. The research team tested the measurement model employing Structural Equation Modeling using Smart Partial Least Squares Version (3.2.7).   Results: Statistically significant associations were confirmed to exist between Personal Innovativeness (PI) influenced by both the Perceived Ease of Use (PEOU), and Perceived Usefulness (PU) (β= 0.456) and (β= 0.563) at P<0.001. The statistically significant associations involving Perceived Enjoyment (EJ) and PEOU and PU (β= 0.554, P<0.05), (β= 0.571, P<0.05) were further confirmed. Additionally, PEOU had a relationship with PU (β= 0.863, P<0.001). Eventually, PEOU and PU significantly influenced the participants’ inclination to use the metaverse technology with (β= 0.745, P<0.001) and (β= 0.416, P<0.001), respectively.     Conclusion: Conclusions made during the research add to the existing literature regarding technology adoption by demonstrating how adoption properties, perceived enjoyment, and personal innovativeness influence students’ perceptions concerning innovational technologies used in education.   Conflicts of interest: None declared

Predicting Diabetes in United Arab Emirates Healthcare: Artificial Intelligence and Data Mining Case Study

Aim: The primary aim of this article is to address the scarcity of tools available to examine the relationships between different attributes in medical datasets within the healthcare industry. Specifically, the focus is on developing a predictive model for diabetes using Artificial Intelligence and Data Mining techniques in the United Arab Emirates healthcare sector.Methods: The paper follows a comprehensive approach, employing the four data mining steps: data preprocessing, data exploration, model building, and model evaluation. To build the predictive model, the decision tree algorithm is utilized. Data from 2856 patients, collected from prime hospitals in Dubai, United Arab Emirates, are analyzed and used as the basis for model development.Results: The research findings indicate that several factors significantly influence the likelihood of developing diabetes. Specifically, age, gender, and genetics emerge as critical determinants in predicting the onset of diabetes. The developed predictive model demonstrates the potential to provide accurate and easy-to-understand results regarding the likelihood of diabetes in the future.Conclusion: This study highlights the importance of Artificial Intelligence and Data Mining techniques in predicting diabetes within the United Arab Emirates healthcare sector. The findings emphasize the significance of age, gender, and genetics in diabetes prediction. This research addresses the current data scarcity and offers valuable insights for healthcare professionals. Furthermore, the study recommends further research to enhance diabetes prediction models and their application in clinical settings

The Impact of COVID-19 Lockdowns on Air Quality: A Systematic Review Study

Background: The purpose of this article was to review the published literature and evaluate the association between air quality/air pollution and the lockdown/stay-at-home orders during COVID-19 pandemic. Our goal is to identify the various environmental factors, such as urban and rural air quality, which were affected by the lockdown during the coronavirus disease (COVID-19) pandemic. Methods: We searched PubMed (2000–2021) for eligible articles using the following: (1) Aerosol[Title/Abstract], AND (2) air quality[Title/Abstract] OR air pollution[Title/Abstract] AND (3) COVID-19[Title/Abstract]. A total of 39 articles were identified through the search conducted in PubMed. We first screened the title and the abstract of those 39 articles for eligibility. A total of 24 articles did not meet the eligibility criteria and were excluded based on the title and the abstract review. The 15 remaining articles were assessed in full text for eligibility and data extraction. After a full-text review, 3 articles were excluded. Finally, a total of 12 selected articles were confirmed for data extraction. Results: Among the 12 studies, 5 articles focused on the effect of the air pollution, fine particulate matter, and air pollutants of COVID-19 pandemic’s lockdown, while 1 article targeted the relationship between the weather/air quality and COVID-19 death rate during lockdown. In addition, 5 papers focused on the association between the environmental factors, air pollution and air quality and COVID-19 mortality rate. Finally, 1 research study paper aimed to study the COVID-19 positivity rate and the effect of air quality during the stay-at-home order or the lockdown which was occurred in March 2020. It is important to note that it has been found that an increase in the average PM2.5 concentration was correlated with a relative increase in the COVID-19 test positivity rate. This explains the increase in the number of COVID cases during the period of the wildfire smoke from August to October 2020 (1). Conclusion: The findings indicate that the COVID-19 lockdown has significant impact on the air quality across the world. The lockdown significantly reduces the air pollutants such as NO2, CO, O3 and Particulate Matter PM2.5 and PM10. This reduction led to a much healthier and safer outdoor air and hence improved the air quality during the lockdown/stay-at-home orders. More research is needed to validate that the air pollutants (NO2, CO, O3, PM2.5 and PM10) have a significant impact on the COVID-19 mortality and fatality rates

The Impact of COVID-19 Lockdowns on Air Quality: A Systematic Review Study

Background: The purpose of this article was to review the published literature and evaluate the association between air quality/air pollution and the lockdown/stay-at-home orders during COVID-19 pandemic. Our goal is to identify the various environmental factors, such as urban and rural air quality, which were affected by the lockdown during the coronavirus disease (COVID-19) pandemic. Methods: We searched PubMed (2000–2021) for eligible articles using the following: (1) Aerosol[Title/Abstract], AND (2) air quality[Title/Abstract] OR air pollution[Title/Abstract] AND (3) COVID-19[Title/Abstract]. A total of 39 articles were identified through the search conducted in PubMed. We first screened the title and the abstract of those 39 articles for eligibility. A total of 24 articles did not meet the eligibility criteria and were excluded based on the title and the abstract review. The 15 remaining articles were assessed in full text for eligibility and data extraction. After a full-text review, 3 articles were excluded. Finally, a total of 12 selected articles were confirmed for data extraction. Results: Among the 12 studies, 5 articles focused on the effect of the air pollution, fine particulate matter, and air pollutants of COVID-19 pandemic’s lockdown, while 1 article targeted the relationship between the weather/air quality and COVID-19 death rate during lockdown. In addition, 5 papers focused on the association between the environmental factors, air pollution and air quality and COVID-19 mortality rate. Finally, 1 research study paper aimed to study the COVID-19 positivity rate and the effect of air quality during the stay-at-home order or the lockdown which was occurred in March 2020. It is important to note that it has been found that an increase in the average PM2.5 concentration was correlated with a relative increase in the COVID-19 test positivity rate. This explains the increase in the number of COVID cases during the period of the wildfire smoke from August to October 2020 (1). Conclusion: The findings indicate that the COVID-19 lockdown has significant impact on the air quality across the world. The lockdown significantly reduces the air pollutants such as NO2, CO, O3 and Particulate Matter PM2.5 and PM10. This reduction led to a much healthier and safer outdoor air and hence improved the air quality during the lockdown/stay-at-home orders. More research is needed to validate that the air pollutants (NO2, CO, O3, PM2.5 and PM10) have a significant impact on the COVID-19 mortality and fatality rates