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

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

Level of satisfaction among primary health care workers in Kosovo

Aim: The objective of this study was to assess the extent and selected corelates of work satisfaction among primary healthcare professionals in Kosovo.Methods: A cross-sectional study was conducted in selected regions of Kosovo during the period May-June 2022 including a representative sample of 500 primary healthcare workers (209 men and 291 women; overall mean age: 42.0±12.3 years). A structured 9-item questionnaire was administered to all participants aiming at assessing the level of satisfaction among primary healthcare workers (each item ranging from 1 [high] to 5 [low]). A summary score was calculated for all 9 items related to satisfaction level ranging from 9 (the highest satisfaction level) to 45 (the lowest satisfaction level). Binary logistic regression was used to assess the association of satisfaction level (dichotomized into “satisfied” vs. “unsatisfied”, based on median value of the summary score) with selected demographic factors and work characteristics of primary healthcare workers.Results: Mean summary score of the 9 items related to the satisfaction level of primary healthcare workers was about 23±5; median score was 23 (interquartile range: 20-26). In multivariable-adjusted logistic regression models, the level of satisfaction was not significantly related to any demographic factor, but positively associated with the years of working experience of primary healthcare workers [OR(for 1 year increment in the work experience)=1.03, 95%CI=1.00-1.05] Conclusion: The evidence from this study conducted in Kosovo indicates no significant relationships of the level of satisfaction with demographic factors of primary healthcare workers, but a strong association with their working experience. Policymakers in Kosovo and in other countries should be aware of the importance of working conditions and working environment in order to gradually increase the level of satisfaction of the staff, which is a basic prerequisite for quality improvement of service delivery at primary healthcare level

Bills Of Health: Cholera And The Politics Of Health In The Colonial Port Of Calcutta (1866-1876)

Introduction: This paper seeks to unravel some of the complex political ramifications of the Bill of Health in the context of colonial India. As the threat of Cholera loomed larger than ever during the second half of 19th century, pilgrimage and pestilence assumed a political character with the major European powers that held the Indian pilgrims to the Hedjaz responsible for the contagion. With the re-enforcement of the Bill of Health, the British government in India was expected to acquiesce to the international demands. But for the British authorities in India, pilgrimage had political implications that could not be ignored. Bill of Health, thus, perturbed the British authorities in India in more ways than one. What was to be the format of the bill? Who was to be appointed the authority to grant such a certificate? What was its relevance for Britain in the global political scenario? These are some of the problems that the paper attempts to address through a diligent archival study.  The port of Calcutta has been taken as a case study not only because it was one of the most important ports of colonial India, but also because it serves the purpose of highlighting the wide gulf between the core and the periphery of the British administration with regard to the execution of adopted policies Objectives: The objective of this research is to understand how colonialism as a system was affected by repeated outbreaks of cholera epidemics. Focusing on one of the major aspects of public health regulations during an epidemic, this article makes an attempt to study how the colonial state of British India responded to the dangers of epidemic and impending sanctions from the international community at large. Methods: The research is mostly based on archival sources, especially official proceedings regarding the Haj pilgrimage from the Port of Calcutta between 1866-1876

Epidemiological features of New-Onset Type 1 Diabetes Mellitus in children and adolescent during 2010-2014 in Albania - a unique experience.

Aim: Diabetes mellitus is a major public health problem worldwide. Type 1 diabetes mellitus (T1DM) is the most common metabolic chronic disease in genetically susceptible children and adolescents, due to an autoimmune process characterized by a selective destruction of insulin producing β-cells. The aim is to assess the epidemiological features of new-onset T1DM in children and adolescent at the national level during the period 2010-2014 in Department of Pediatrics, Endocrine Unit, University Hospital Center \u27Mother Teresa\u27, Tirana, as the unique center for pediatric endocrinology and diabetology in Albania.Methods: The clinical and laboratory characteristics of 152 patients aged <15 years newly diagnosed with T1D from 1 January 2010 to 31 December 2014 were studied. T1D was diagnosed according to WHO 2006 criteria and DKA was diagnosed based on ISPAD 2014 criteria. Patients were classified into 3 sub-groups (I: 0-4 years, II: 5-9 years, and III; 10-14 years). Statistical analysis was performed using SPSS 26. Results: The incidence of new-onset of T1DM was 5.012/100.000/year. The mean age of children at diagnosis was 8.3 ± 3.6 years. The patients were mostly diagnosed at ages 5-9 years (40.1%), and 10-14 years (39.5%), followed by the 0-4 years age group (20.4%). Mean duration of symptoms was 23.35 ± 17.16 days; longer in the subgroup 5-9 years (P= 0. 0.013). Three quarters (75%) of children with T1DM live in urban areas. Viral infections or other circumstance triggers were in 41.9% of children aged 0-4 years compared to other subgroups (P=0.002). Most of the children were born in the spring−summer months (53.23%) compared to the autumn−winter months (46.77%). Approximately 1/4 of the children were born and diagnosed with type 1 diabetes in each of the seasons of the year and 52.63% of the patients studied were first born. Family history for DMT1 and DMT2 is observed in 15.8% and 17.8% of the children, respectively. Polyuria (99.3%), polydipsia (99.6%) and weight loss (98.1%) were the most common symptoms and 67.8% of patients presented with diabetic ketoacidosis (DKA). Misdiagnosis was in 21 (13.8%) patients. Mean glycosylated hemoglobin A1c (HbA1c) was 11.63%; 11.9 ± 2.0 in DKA positive children and 11.1 ± 2.4 in DKA negative children (p= 0.195).At diagnosis and during follow up of T1DM 25% (38/152) developed associated autoimmune diseases; 68.42% at diagnosis of T1DM and 65.79% (25/38) of patients were female. During follow up children with T1DM developed associated CD and SAT, 2.54, and 2.19 years, respectively.Conclusion: Diabetes mellitus is one of the major public health problems worldwide. Albania is a country with middle incidence of T1DM and the age at onset is decreasing. The symptoms lasted significantly longer and mean HbA1c levels were significantly higher in older children. The incidence of DKA in children with newly diagnosed T1DM is high

Personalized Patient Care Through AI-Driven Segmentation Predictive Modelling Thyroid Disease Detection Using Machine Learning

The thyroid gland is the crucial organ in the human body, secreting two hormones that help to regulate the human body’s metabolism. Thyroid disease is a severe medical complaint that could be developed by high Thyroid Stimulating Hormone (TSH) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two critical conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Deep learning models can be used to precisely process the data generated from different medical sectors and to build a model to predict several dis- eases. In this paper, we use different machine-learning algorithms to predict hypothyroidism and hyperthyroidism. Moreover, we identified the most significant features, which can be used to detect thyroid diseases more precisely. After completing the pre-processing and feature selection steps, we applied our modified and original data to several classification models to predict thyroids. We found Random Forest (RF) is giving the maximum evaluation score in all sectors in our dataset, and Naive Bayes is performing very poorly. Moreover, selecting the feature by using the feature importance method RF provides the best accuracy of 91.42%, precision of 92%, recall of 92% and F1-score of 92%. Further, by analyzing the characteristics and behavior of the dataset, we identified the most important features (TSH, T3, TT4, and FTI) of the dataset. In terms of accuracy and other performance evaluation criteria, this study could advocate the use of effective classifiers and features backed by machine learning algorithms to detect and diagnose thyroid disease. Finally, we did some explain ability analysis of our best classifier to understand the internal black-box of our machine learning model and datasets. This study could further pave the way for the researcher as well as healthcare professionals to analyze thyroid disease in real time applications

Clinical Profile Of Congenital Eye Abnormalities In Children Aged 0-5 Years And Their Correlation With Demographic Factors

Background: Congenital eye abnormalities encompass a wide range of structural and functional defects present at birth, contributing significantly to childhood visual impairment and blindness. Understanding their clinical profile and demographic associations is essential for early detection and prevention. Aim of the study: To evaluate the clinical spectrum of congenital eye abnormalities in children aged 0-5 years and analyze their correlation with demographic factors. Methods: This hospital-based observational study was conducted over one year in Bangladesh. Eighty-two pediatric patients presenting with congenital ocular anomalies were included. Data on clinical features, antenatal history, and demographic variables were collected using a structured questionnaire. Comprehensive ophthalmologic examinations were performed, and findings were analyzed using SPSS v26.0. Result: Children aged 4-5 years constituted the largest group (56.10%), with a male predominance (64.63%). The most common presenting symptom was decreased ocular vision (18.29%), followed by watering of eyes (13.41%). Congenital cataract was the most prevalent anomaly (46.74%), followed by coloboma of the iris and choroid (33.62%), and anophthalmos (19.68%). Bilateral involvement was more frequent than unilateral presentation. Conclusion: Congenital cataract, coloboma, and whole globe anomalies are the predominant ocular abnormalities in early childhood. Timely screening and intervention strategies are essential to minimize long-term visual disability, especially in resource-limited settings

Effects Of Back School On Chronic Nonspecific Low Back Pain In Adolescents

Background: Low back pain (LBP) is a common condition among adolescents, with significant implications for their health and quality of life. This study aimed to assess the prevalence of LBP and examine its associations with age, Body Mass Index (BMI), backpack weight, and posture in adolescent students. Methods: A cross-sectional study was conducted with 60 adolescent students aged 14-17 years from local schools in Dhaka, Bangladesh. Data were collected through self-reported questionnaires and physical assessments to determine the prevalence of LBP and identify risk factors. Participants were categorized based on age, BMI, backpack weight, and posture type. Descriptive statistics and Chi-square tests were used for analysis, with a p-value of <0.05 considered statistically significant. Results: The prevalence of LBP was found to be 78.3%, with the highest incidence observed in 15-year-olds (50%) and underweight adolescents (50%). A significant association was found between LBP and heavier backpack weight, with 33.3% of students in the 2.001–3.00 kg category reporting pain. Posture type was also a significant factor, with sitting posture (41.7%) being most strongly associated with LBP. Statistical analysis revealed significant associations between age, BMI, backpack weight, and posture type with the prevalence of LBP (p<0.05). Conclusions: The study highlights the high prevalence of LBP among adolescents, particularly in relation to age, BMI, backpack weight, and posture. These findings suggest that interventions such as Back School programs, aimed at promoting proper posture and reducing backpack weight, could help prevent and manage LBP in this age group. Further longitudinal studies are recommended to explore the long-term effects of these factors on adolescent back health

Harnessing Clinical Data To Improve Healthcare Efficiency

In recent years, healthcare systems worldwide have faced immense pressure to enhance efficiency and provide better patient outcomes while reducing costs. One of the key solutions lies in the ability to harness clinical data for improved decision-making, resource allocation, and treatment outcomes. Clinical data, such as patient records, diagnostic results, and treatment histories, offers valuable insights into the effectiveness of healthcare interventions. This research explores how clinical data can be leveraged through advanced analytics, machine learning algorithms, and data integration techniques to optimize healthcare delivery. By using real-world data, the study aims to enhance decision-making processes and provide a foundation for predictive modeling in clinical settings. The paper presents various tools, methodologies, and challenges in utilizing clinical data, as well as their potential to improve healthcare efficiency. The research further discusses the importance of data-driven approaches in enhancing operational efficiency and patient outcomes. Results suggest that integrating clinical data across platforms and applying analytics leads to better resource allocation, reduces patient wait times, and enables more personalized care pathways. The study’s findings provide valuable insights for policymakers, healthcare practitioners, and technology developers interested in optimizing healthcare systems through data-driven approaches