This study explored issues related to the adoption and implementation of electronic health record (EHR) systems including building the awareness, knowledge, and experience of physicians toward cyber hygiene. This study used a qualitative research method to assess (a) the barriers to EHR systems adoption and implementation in Kuwait and (b) the level of awareness, knowledge and experiences related to cyber hygiene practices in Kuwait. The findings of the study supported the conceptual framework used to guide the research of the factors impacting the adoption and implementation of EHR systems in Kuwait as well as explore the level of awareness, knowledge, and experience of physicians about both EHR systems and cyber hygiene. The results from the systematic literature review analysis identified seven major barriers. These are financial barriers, time, difficulty of using technology, lack of support, negative attitude, legal and ethical (policies), and cultural barriers. The findings from the semistructured interviews supported the literature findings and provided more in-depth insights into the structural and social issues affecting the adoption and implementation of EHR systems. Given that Kuwait is a member of the Gulf Cooperation Countries (GCC), the results from the literature analysis showed that the problems in Kuwait are similar to …

In this study we explore the factors impacting physician patient interaction time and how these factors can be used to improve health knowledge transfer from physicians to patients. We also investigate how technology tools can be used to improve this interaction time. Physician patient interaction time is important because this is the time when both sides engage with each other, exchange information so that physicians understand their patients' health issues. Given the increasing health care costs and demand for physician time, over the years, this interaction time has been distracted due to many factors. It is important to explore these factors and provide some possible solutions, such as advanced knowledge management systems, such as knowledge portals. To identify the factors impacting this interaction time and the role of technology, we first identified the variables and then developed hypotheses. We used data from the surveys administered by the AHRQ and NCHS to test these hypotheses. We conducted correlation analyses to determine the factors that can be used to improve health knowledge transfer from physicians to patients and how technology tools can be used to improve physician patient interaction time. Our analyses indicate that the factors we identified to improve health knowledge …

The purpose of this study was to assess visual analytics technology acceptance and user experience among in vitro fertilization (IVF) consumers. The research aimed to show how visual analytics tools and technologies can be applied in the consumer space to enhance how users interpret healthcare success rate data. This exploratory user evaluation study utilized a quantitative dominant, mixed-methods approach with a convergent parallel design based on the data-validation variant. Survey data were collected from consumers who were currently seeking information about IVF treatment in the United States. The study findings indicated that the extended unified theory of acceptance and use of technology (UTAUT2) constructs of performance expectancy and hedonic motivation influenced consumer behavioral intention to use visual analytics technology, while effort expectancy did not. Further, the findings from the user experience and qualitative analyses indicated that there is strong support for consumer adoption of visual analytics technology for personal healthcare decision-making. These findings may help in the design and development of modern, interactive visualization tools that could be used to visualize public or private healthcare data for analysis by consumers. Stakeholders, including the US Centers for Disease Control and Prevention, the World Health Organization, and medical practitioners, may use the …

This study aims to apply user reviews and numerical ratings toward items to create an aspect-aware high-order representation for a recommendation system. We propose a novel aspect-aware knowledge graph recommendation model (AKGR) with the deep learning method to predict users' ratings on non-interacted items, from which more personalized recommendations can be made. First, we create a sequence-to-sequence encoder and decoder model by exploiting contextual and syntactic information in user reviews to extract aspects critical to items. Then we utilize the principal component analysis (PCA) and the K-means clustering to analyze the extracted aspects for category classification. Based on the aspects, we construct a graph structure to connect users and items which share the same aspect-based opinions for mining user preferences and item attributes. Finally, we combine the user and item latent features from the reviews and the user-item rating matrix to complete the rating prediction task by applying the factorization machine model. We conducted experiments on three aspect extraction datasets and five rating prediction datasets. To verify the effectiveness of the proposed aspect extraction model and rating prediction model, comparison experiments were made with some state-of-the-art baseline models, such as double embeddings convolutional neural network (DE-CNN) and dual graph convolutional …