Undispensed Prescriptions due to Drug Unavailability at a Teaching Hospital in Saudi Arabia

Purpose: To describe the extent of undispensed prescriptions as a result of drug unavailability in a tertiary health facility in Saudi Arabia Methods: This study was conducted prospectively in a large teaching hospital over a 6 months period from May to October of 2005 and tracked 14 commonly prescribed drugs. The bimonthly drug requisition forms and the amount supplied by medical supply department (MSD) were collected and reviewed. Results: Total number of prescriptions issued for the drugs under examination was 29113. 26551 prescriptions were dispensed while the remaining 2562 (9%) were undispensed due to drug unavailability. The difference between quantity of drugs requested by the pharmacy and quantity issued from MSD was 47-52%. Conclusion: Mismatch between drug demand and supply in the facility studied is the main cause of shortage.Keywords: Drug prescriptions, inventory management, hospital pharmac

Technology acceptance drivers for AR smart glasses in the middle east : a quantitative study

This study aims to establish Middle East users' perspectives on the major factors that impact their decision to adopt Augmented Reality AR smart glasses (ARSG). Thus, an online questionnaire was designed and sent directly to the respondents, and 584 valid data points were collected from individuals living in the Middle East. The data were analyzed using Pearson correlations and Exploratory Factor Analysis (EFA) techniques using SPSS. Eleven hypotheses were tested using Multiple Regression analysis, where seven independent variables out of eleven were confirmed to have a significant impact on the perceived adoption of ARSG. The results indicate that four of the independent variables including Pre-Market Knowledge, Image, Own privacy and Technology innovativeness show the significant impact on ARSG adoption at the 1% significant level. In addition, the results indicate that three of the social and technological factors include Perceived Ease of use, Perceived usefulness and Other's privacy show the significant effect on ARSG adoption at the 5% significant level. Among the 7 social and technological factors, the results suggest that technology innovation expresses the strongest effect on ARSG adoption with the highest coefficient value of 0.413 (b = 0.413, t = 12.881, ρ < 0.01). Moreover, user intention is significantly impacted by gender and place of living but not by education or age. The research also provides pre-market insights on users' personal types that represent who will most likely adopt the new smart glasses and that differentiate them based on their priorities. To the best of our knowledge, this is among the first works to investigate technology acceptance drivers of AR smart glasses in the Middle East

Acceptance of Google Meet during the spread of Coronavirus by Arab university students

The COVID-19 pandemic not only affected our health and medical systems but also has created large disruption of education systems at school and universities levels. According to the United Nation’s report, COVID-19 has influenced more than 1.6 billion learners from all over the world (190 countries or more). To tackle this problem, universities and colleges have implemented various technologically based platforms to replace the physical classrooms during the spread of Coronavirus. The effectiveness of these technologies and their educational impact on the educational sector has been the concern of researchers during the spread of the pandemic. Consequently, the current study is an attempt to explore the effect of Google Meet acceptance among Arab students during the pandemic in Oman, UAE, and Jordan. The perceived fear factor is integrated into a hybrid model that combines crucial factors in TAM (Technology acceptance Model) and VAM (Value-based Adoption Model). The integration embraces perceived fear factor with other important factors in TAM perceived ease of use (PEOU) and perceived usefulness (PU) on the one hand and technically influential factor of VAM, which are perceived technicality (PTE) and perceived enjoyment (PE) on the other hand. The data, collected from 475 participants (49% males and 51% females students), were analyzed using the partial least squares-structural equation modelling (PLS-SEM). The results have shown that TAM hypotheses of usefulness and easy to use have been supported. Similarly, the results have supported the hypotheses related to VAM factors of being technically useful and enjoying, which helps in reducing the atmosphere of fear that is created due to the spread of Coronavirus

Acceptance determinants of 5G services

5G is a revolutionary development in network technologies which is gradually becoming very common among people contributing significantly in different fields such as education, industry, agriculture, health, tourism and military. Currently, 5G is an outbreak change as opposed to the traditional service of the Internet since it offers better quality, ultra-fast connection, low-cost, reduced latency, energy saving, which makes its great impact even greater in people’s life. The present study examines various factors that have a significant impact on the Use of 5G in the Gulf area. The study extended the TAM (Technology Acceptance Model) to include factors such as Perceived Enjoyment, Perceived Resources and Perceived Skills Readiness. The present research has adopted a hybrid model that incorporates TAM determinants with other external factors which have a direct relation with 5G as internet service. Previous studies have focused on the importance of 5G in different environments and countries. However, this study focuses on the newly spread Use of 5G in the gulf area by adopting a hybrid conceptual model. The findings suggest that 5G may help in promoting the usage of internet service more effectively with its low-cost, faster data transfer and better quality. Moreover, the findings indicate a positive effect of the gender as a mediator between the variables: Perceived Skills Readiness, Perceived Ease of use, and Perceived Resources

Acceptance determinants of 5G services

5G is a revolutionary development in network technologies which is gradually becoming very common among people contributing significantly in different fields such as education, industry, agriculture, health, tourism and military. Currently, 5G is an outbreak change as opposed to the traditional service of the Internet since it offers better quality, ultra-fast connection, low-cost, reduced latency, energy saving, which makes its great impact even greater in people’s life. The present study examines various factors that have a significant impact on the Use of 5G in the Gulf area. The study extended the TAM (Technology Acceptance Model) to include factors such as Perceived Enjoyment, Perceived Resources and Perceived Skills Readiness. The present research has adopted a hybrid model that incorporates TAM determinants with other external factors which have a direct relation with 5G as internet service. Previous studies have focused on the importance of 5G in different environments and countries. However, this study focuses on the newly spread Use of 5G in the gulf area by adopting a hybrid conceptual model. The findings suggest that 5G may help in promoting the usage of internet service more effectively with its low-cost, faster data transfer and better quality. Moreover, the findings indicate a positive effect of the gender as a mediator between the variables: Perceived Skills Readiness, Perceived Ease of use, and Perceived Resources

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children &lt;18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p&lt;0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p&lt;0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p&lt;0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

Business excellence in enhancing global competitive advantage in healthcare sector of UAE

The purpose of the study is to explore the role of business excellence in improving global competitive advantage in the UAE’s healthcare sector. The study explores various business excellence models and recognizes widely used models and the best model applicable to the UAE context, and evaluates factors that influence business excellence such as leadership, HRM, Quality management, customer satisfaction, information, and analysis on the quality results competitive advantage. The paper used a quantitative approach to gather primary data on the selected sector besides statistical analysis to evaluate the influence of business excellence factors on improving competitive advantage. The findings show a high maturity of organizational functioning in the healthcare industry to implement business excellence to improve their competitive advantage

Classifying audio music genres using CNN and RNN

This paper discusses applying different types of neural networks to classify a dataset of type audio. We used a GTZAN dataset that includes various audio music records representing different conventional categories of music genres. Each shares a set of common traditions; these traditions we call features. We build our proposed Python models using the Anaconda toolkit with TensorFlow (TF) an open-source deep-learning library. In our previous research, we build a multilayer sequential model to classify the dataset and then solve the overfitting issue in that model. In this paper, we build a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) with Long Short Time Memory (LSTM). Finally, we compared the results to know the capabilities and limitations of Deep Learning (DL). CNN outperformed the other models in terms of training and test accuracy, having 83.74% and 74%, respectively