Determinants predicting the electronic medical record adoption in healthcare: A SEM-Artificial Neural Network approach

An Electronic Medical Record (EMR) has the capability of promoting knowledge and awareness regarding healthcare in both healthcare providers and patients to enhance interconnectivity within various government bodies, and quality healthcare services. This study aims at investigating aspects that predict and explain an EMR system adoption in the healthcare system in the UAE through an integrated approach of the Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology Acceptance Model (TAM) using various external factors. The collection of data was through a cross-section design and survey questionnaires as the tool for data collection among 259 participants from 15 healthcare facilities in Dubai. The study further utilised the Artificial Neural Networks (ANN) algorithm and the Partial Least Squares Structural Equation Modeling (PLS-SEM) in the analysis of the data collected. The study's data proved that the intention of using an EMR system was the most influential and predictor of the actual use of the system. It was also found that TAM construct was directly influenced by anxiety, innovativeness, self-efficacy, and trust. The behavioural intention of an individual regarding EMR was also proved to positively influence the use of an EMR system. This study proves to be useful practically by providing healthcare decision-makers with a guide on factors to consider and what to avoid when implementing strategies and policies

Determinants of intention to use medical smartwatch-based dual-stage SEM-ANN analysis

The current study is based on an integrated research model developed by combining constructs from the Technology Acceptance Model (TAM) and other features affecting smartwatch effectiveness, such as content richness and user satisfaction (SAT). TAM is used to locate factors influencing the adoption of the smartwatch (ASW). Most importantly, the current study focuses on factors influencing smartwatch acceptance and use in the medical area, facilitating and enhancing the effective role of doctors and patients. The present study's conceptual framework examines the close association between two-term TAM variables of perceived ease of use (PEU) and perceived usefulness (PU) and the constructs of user satisfaction and content richness. It also incorporates the flow theory (EXP) to measure the effectiveness of the smartwatch. The study also uses the flow theory to assess involvement and control over ASW. The study used a sample of 489 respondents from the medical field, including doctors, nurses, and patients. The study employed a hybrid analysis method combining Structural Equation Modeling (SEM) and an Artificial Neural Network (ANN) based on deep learning. The study also used Importance-Performance Map Analysis (IPMA) to determine the relevance and performance of the variables influencing ASW. Based on the ANN and IPMA analyses, user satisfaction is the most crucial predictor of intention to use a medical smartwatch. Applying the structural equation model to the sample shows that SAT, PU, PEU, and EXP significantly influence intention to use a medical smartwatch. The study also revealed that content richness is an important factor that enhances users' PU. The current study could enable healthcare provider practitioners and decision-makers to identify factors for prioritisation and to strategise their policies accordingly. Methodologically, this study indicates that a “deep ANN architecture” can determine the non-linear associations between variables in the theoretical model. Overall, the study finds that smartwatches are in high demand in the medical field and are useful in information transmission between doctors and their patients

Prediction of User’s Intention to Use Metaverse System in Medical Education: A Hybrid SEM-ML Learning Approach

Metaverse (MS) is a digital universe accessible through a virtual environment. It is established through the merging of virtually improved physical and digital reality. Metaverse (MS) offers enhanced immersive experiences and a more interactive learning experience for students in learning and educational settings. It is an expanded and synchronous communication setting that allows different users to share their experiences. The present study aims to evaluate students’ perception of the application of MS in the United Arab Emirates (UAE) for medical-educational purposes. In this study, 1858 university students were surveyed to examine this model. The study’s conceptual framework consisted of adoption constructs including Technology Acceptance Model (TAM), Personal innovativeness (PI), Perceived Compatibility (PCO), User Satisfaction (US), Perceived Triability (PTR), and Perceived Observability (POB). The study was unique because the model correlated technology-based features and individual-based features. The study also used hybrid analyses such as Machine Learning (ML) algorithms and Structural Equation Modelling (SEM). The present study also employs the Importance Performance Map Analysis (IPMA) to assess the importance and performance factors. The study finds US as an essential determinant of users’ intention to use the metaverse (UMS). The present study’s finding is useful for stakeholders in the educational sector in understanding the importance of each factor and in making plans based on the order of significance of each factor. The study also methodologically contributes to Information Systems (IS) literature because it is one of the few studies that have used a complementary multi-analytical approach such as ML algorithms to investigate the UMS metaverse systems

A controlled trial of screening, brief intervention and referral for treatment (SBIRT) implementation in primary care in the United Arab Emirates

The authors would like to acknowledge all the Primary Care Physicians and patients who participated in this project.Peer reviewedPostprin