Understanding mindsets, skills, current practices, and barriers of adoption of digital accessibility in Kuwait's software development landscape.

The responsibility for creating accessible software within the development of digital services is important for multiple reasons, mainly equity and inclusion. However, adopting and sustaining the development of accessible digital solutions has always been challenging, more so in countries that are relatively new to the concept of universal design, and physical and digital accessibility, and where legal sanctions are not yet established. This work investigates the technology scene in the State of Kuwait and analyses the responses of computing professionals with regard to their skills, best practices and procurement of accessible tech and to their level of awareness toward people with disabilities. The findings reveal a low level of awareness among tech professionals with regard to disabilities and digital accessibility-related standards. The findings also highlight a lack of available guidance for developing inclusive design and accessibility. Additionally, time constraints, lack of training, legal enforcement and fundamentals concepts during undergraduate and higher education contributed to observed weaknesses. Participants were keen to learn more and benefited from flyers and free professional development courses offered as incentives for survey completion.

Evaluating breast cancer websites targeting Arabic speakers: empirical investigation of popularity, availability, accessibility, readability, and quality.

BACKGROUND: Nowadays, patients have access to all types of health information on the internet, influencing their decision-making process. The Middle East and North Africa (MENA) region consists of 22 countries with an estimated population of around 600 million. Breast cancer is the highest diagnosed cancer in this region. Websites are commonly the go-to cancer information sources. A large population of the MENA region is only fluent in the Arabic language, thus access to Arabic websites is in more demand. However, little is known about breast cancer websites that cater to an Arabic-speaking audience. This study aims at evaluating Arabic breast cancer websites and offering recommendations to improve engagement and access to health information. METHODS: This study employed a cross-sectional analysis approach. Google trends was used to reveal the top searched topics across the MENA region, which in turn were used as search terms to identify the websites. To be included, a website had to be active, available in Arabic, and contain breast cancer information. The evaluation was based on a combination of automated and expert-based evaluation methods through five dimensions: Availability, Accessibility, Readability, Quality, and Popularity. RESULTS: Overall most of the websites performed poorly in the five dimensions and require careful reassessment concerning design, content, and readability levels; Only one website performed well in all dimensions, except for readability. Generally, the readability scores indicated that the websites were above the recommended level of reading. None of the websites passed the automated accessibility tests. The expert evaluation using the "Health on the Net" checklist showed good results for most websites. CONCLUSIONS: Breast cancer rates are rising in the MENA region, therefore having comprehensive, accurate, trustworthy, and easy-to-understand health information in their native language is a must. The results from this study show a need for improving the accessibility to breast cancer information websites available to Arabic speakers. The search was limited to three search engines yielding 10 websites and only one tool was used per dimension. Future work is needed to overcome these limitations. Collaboration between multiple stakeholders is necessary to develop websites that contain easy-to-read and understand high-quality information.

Towards value-based healthcare: Establishing baseline pharmacy care costs for diabetes management.

The prevalence of diabetes has increased by three folds over the last 20 years, and the global cost of diabetes mellitus surpassed one trillion US Dollars (USD) or 1.8% of the global GDP in 2015. Generally, prescription medication to treat complications of diabetes makes up nearly 30% of diabetes medical expenditures. To facilitate value-based decision-making at national and organizational levels, we analyzed the cost drivers of pharmacy services in a diabetes care institute by developing a flexible costing model that accounts for pharmaceuticals and labour costs of pharmacy processes. We calculated the direct pharmaceutical costs and the indirect labour costs at the activity level from electronic health records and observational data. On average, the cost of pharmacy services over 1 year was equivalent to 1246 USD per diabetes patient. Approximately 98% of the pharmacy costs were pharmaceutical costs, while 2% were attributable to labour. The flexible costing model and cost estimates are essential for value-based comparisons of interventions and care redesign. The outlined costing framework and findings carry implications nationally and organizationally to accelerate the path towards value-based healthcare delivery and provider reimbursement schemes through agile cost estimation, efficiency improvements, and higher value of care.

The effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in different socioeconomic populations in Kuwait: a modeling study.

BACKGROUND: Aggressive non-pharmaceutical interventions (NPIs) may reduce transmission of SARS-CoV-2. The extent to which these interventions are successful in stopping the spread have not been characterized in countries with distinct socioeconomic groups. We compared the effects of a partial lockdown on disease transmission among Kuwaitis (P1) and non-Kuwaitis (P2) living in Kuwait. METHODS: We fit a modified metapopulation SEIR transmission model to reported cases stratified by two groups to estimate the impact of a partial lockdown on the effective reproduction number ([Formula: see text]). We estimated the basic reproduction number ([Formula: see text]) for the transmission in each group and simulated the potential trajectories of an outbreak from the first recorded case of community transmission until 12 days after the partial lockdown. We estimated [Formula: see text] values of both groups before and after the partial curfew, simulated the effect of these values on the epidemic curves and explored a range of cross-transmission scenarios. RESULTS: We estimate [Formula: see text] at 1·08 (95% CI: 1·00-1·26) for P1 and 2·36 (2·03-2·71) for P2. On March 22nd, [Formula: see text] for P1 and P2 are estimated at 1·19 (1·04-1·34) and 1·75 (1·26-2·11) respectively. After the partial curfew had taken effect, [Formula: see text] for P1 dropped modestly to 1·05 (0·82-1·26) but almost doubled for P2 to 2·89 (2·30-3·70). Our simulated epidemic trajectories show that the partial curfew measure greatly reduced and delayed the height of the peak in P1, yet significantly elevated and hastened the peak in P2. Modest cross-transmission between P1 and P2 greatly elevated the height of the peak in P1 and brought it forward in time closer to the peak of P2. CONCLUSION: Our results indicate and quantify how the same lockdown intervention can accentuate disease transmission in some subpopulations while potentially controlling it in others. Any such control may further become compromised in the presence of cross-transmission between subpopulations. Future interventions and policies need to be sensitive to socioeconomic and health disparities.

Overview of Technologies Implemented During the First Wave of the COVID-19 Pandemic: Scoping Review.

BACKGROUND: Technologies have been extensively implemented to provide health care services for all types of clinical conditions during the COVID-19 pandemic. While several reviews have been conducted regarding technologies used during the COVID-19 pandemic, they were limited by focusing either on a specific technology (or features) or proposed rather than implemented technologies. OBJECTIVE: This review aims to provide an overview of technologies, as reported in the literature, implemented during the first wave of the COVID-19 pandemic. METHODS: We conducted a scoping review using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) Extension for Scoping Reviews. Studies were retrieved by searching 8 electronic databases, checking the reference lists of included studies and relevant reviews (backward reference list checking), and checking studies that cited included studies (forward reference list checking). The search terms were chosen based on the target intervention (ie, technologies) and the target disease (ie, COVID-19). We included English publications that focused on technologies or digital tools implemented during the COVID-19 pandemic to provide health-related services regardless of target health condition, user, or setting. Two reviewers independently assessed the eligibility of studies and extracted data from eligible papers. We used a narrative approach to synthesize extracted data. RESULTS: Of 7374 retrieved papers, 126 were deemed eligible. Telemedicine was the most common type of technology (107/126, 84.9%) implemented in the first wave of the COVID-19 pandemic, and the most common mode of telemedicine was synchronous (100/108, 92.6%). The most common purpose of the technologies was providing consultation (75/126, 59.5%), followed by following up with patients (45/126, 35.7%), and monitoring their health status (22/126, 17.4%). Zoom (22/126, 17.5%) and WhatsApp (12/126, 9.5%) were the most commonly used videoconferencing and social media platforms, respectively. Both health care professionals and health consumers were the most common target users (103/126, 81.7%). The health condition most frequently targeted was COVID-19 (38/126, 30.2%), followed by any physical health conditions (21/126, 16.7%), and mental health conditions (13/126, 10.3%). Technologies were web-based in 84.1% of the studies (106/126). Technologies could be used through 11 modes, and the most common were mobile apps (86/126, 68.3%), desktop apps (73/126, 57.9%), telephone calls (49/126, 38.9%), and websites (45/126, 35.7%). CONCLUSIONS: Technologies played a crucial role in mitigating the challenges faced during the COVID-19 pandemic. We did not find papers describing the implementation of other technologies (eg, contact-tracing apps, drones, blockchain) during the first wave. Furthermore, technologies in this review were used for other purposes (eg, drugs and vaccines discovery, social distancing, and immunity passport). Future research on studies on these technologies and purposes is recommended, and further reviews are required to investigate technologies implemented in subsequent waves of the pandemic.

Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study.

BACKGROUND: The recent coronavirus disease (COVID-19) pandemic is taking a toll on the world's health care infrastructure as well as the social, economic, and psychological well-being of humanity. Individuals, organizations, and governments are using social media to communicate with each other on a number of issues relating to the COVID-19 pandemic. Not much is known about the topics being shared on social media platforms relating to COVID-19. Analyzing such information can help policy makers and health care organizations assess the needs of their stakeholders and address them appropriately. OBJECTIVE: This study aims to identify the main topics posted by Twitter users related to the COVID-19 pandemic. METHODS: Leveraging a set of tools (Twitter's search application programming interface (API), Tweepy Python library, and PostgreSQL database) and using a set of predefined search terms ("corona," "2019-nCov," and "COVID-19"), we extracted the text and metadata (number of likes and retweets, and user profile information including the number of followers) of public English language tweets from February 2, 2020, to March 15, 2020. We analyzed the collected tweets using word frequencies of single (unigrams) and double words (bigrams). We leveraged latent Dirichlet allocation for topic modeling to identify topics discussed in the tweets. We also performed sentiment analysis and extracted the mean number of retweets, likes, and followers for each topic and calculated the interaction rate per topic. RESULTS: Out of approximately 2.8 million tweets included, 167,073 unique tweets from 160,829 unique users met the inclusion criteria. Our analysis identified 12 topics, which were grouped into four main themes: origin of the virus; its sources; its impact on people, countries, and the economy; and ways of mitigating the risk of infection. The mean sentiment was positive for 10 topics and negative for 2 topics (deaths caused by COVID-19 and increased racism). The mean for tweet topics of account followers ranged from 2722 (increased racism) to 13,413 (economic losses). The highest mean of likes for the tweets was 15.4 (economic loss), while the lowest was 3.94 (travel bans and warnings). CONCLUSIONS: Public health crisis response activities on the ground and online are becoming increasingly simultaneous and intertwined. Social media provides an opportunity to directly communicate health information to the public. Health systems should work on building national and international disease detection and surveillance systems through monitoring social media. There is also a need for a more proactive and agile public health presence on social media to combat the spread of fake news.

Artificial Intelligence in the Fight Against COVID-19: Scoping Review.

BACKGROUND: In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. OBJECTIVE: This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation. METHODS: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data. RESULTS: We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome-related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine. CONCLUSIONS: The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

Assessing Electronic Health Literacy in the State of Kuwait: Survey of Internet Users From an Arab State.

BACKGROUND: The internet and social media have become an important source for health information. In 2017, the State of Kuwait ranked first in mobile subscription penetration in the Arab world; nearly 90% of its population uses the internet. Electronic health (eHealth) literacy is important in populations that have easy and affordable access to internet resources to more effectively manage health conditions as well as improve general population health. OBJECTIVE: The aim of this study was to assess eHealth literacy levels across internet users in Kuwait and identify demographic characteristics that influence eHealth literacy. Furthermore, the study aimed to identify the reasons and type of information that people seek online. Finally, this study examined the utilization of various social media channels for accessing online health information. The social media platforms considered were as follows: WhatsApp, Twitter, Instagram, YouTube, Facebook, and Snapchat. METHODS: A cross-sectional anonymous Web-based survey was used to collect data about eHealth literacy and related information. The eHealth literacy scale (eHEALS), originally developed by Norman and Skinner, is measured using 8 Likert-type scales. A linear regression model estimates the effect of demographic variables such as age, gender, and education on eHealth literacy while controlling for participants' perceived usefulness and importance of the internet. Participants were also surveyed about their frequency in using social media platforms for seeking health information. RESULTS: Kuwait's composite eHEALS, based on a sample of 386 participants, was 28.63, which is very similar to eHEALS observed among adult populations in other developed countries. Females in Kuwait demonstrated a higher average eHEALS compared with males. Among the social media platforms, the survey results indicated that YouTube is the most frequently used to seek health information, with Facebook being the least frequently used. CONCLUSIONS: Internet users in Kuwait appear confident in their ability to search for health-related information online compared with other populations, as indicated by aggregate eHEALS scores. Considering this finding, government and health care organizations should shift more efforts from traditional media toward online health information, focusing on the social media outlets that people in Kuwait find more useful for seeking health information.

Evaluating hospital websites in Kuwait to improve consumer engagement and access to health information: a cross-sectional analytical study.

BACKGROUND: Current advances in information and communication technology have made accessing and obtaining health-related information easier than ever before. Today, many hospital websites use a patient-centric approach to promote engagement and encourage learning for better health-related decision making. However, little is known about the current state of hospital websites in the State of Kuwait. This study aims to evaluate hospital websites in Kuwait and offer recommendations to improve patient engagement and access to health information. METHODS: This study employs a cross-sectional analytical approach to evaluate hospital websites in Kuwait in 2017. The websites of hospitals that provide in-patient services were identified through a structured search. Only active websites that were available in either English or Arabic were considered. The evaluation of the websites involved a combination of automated and expert- based evaluation methods and was performed across four dimensions: Accessibility, Usability, Presence, and Content. RESULTS: Nine hospitals met the inclusion criteria. Most of the websites fell short in all four dimensions. None of the websites passed the accessibility guidelines. The usability of websites varied between hospitals. Overall, the majority of hospitals in Kuwait have rudimentary online presence and their websites require careful reassessment with respect to design, content, and user experience. The websites focus primarily on promoting services provided by the hospital rather than engaging and communicating with patients or providing evidence-based information. CONCLUSIONS: Healthcare organization and website developers should follow best-practices to improve their websites taking into consideration the quality, readability, objectivity, coverage and currency of the information as well as the design of their websites. Hospitals should leverage social media to gain outreach and better engagement with consumers. The websites should be offered in additional languages commonly spoken by people living in Kuwait. Efforts should be made to ensure that health information on hospital websites are evidence-based and checked by healthcare professionals.

Technology Innovations for Better Fall Risk Management in Home Care.

Achieving better fall risk management is an integral component of quality home care. The current qualitative study uncovers the challenges and opportunities of home health agencies (HHAs) in achieving better fall risk management. A secondary document analysis was adopted to learn from rich contextual information in fall incident reports recorded in a HHA. Poor engagement of patients and caregivers was a contributing factor in many fall incidents. Patients often fell as a result of not understanding or accepting their physical limitations. For better fall risk management, many incidents highlighted a need for providing complete and thorough care, better coordination of care, higher levels of sociocultural awareness, patient engagement, and caregiver involvement. The results provide evidence regarding the challenges and opportunities for improving fall risk management in home care along with insight about how information technology solutions can support improvement initiatives. [Journal of Gerontological Nursing, 44(7), 15-20.].

Supporting the information domains of fall-risk management in home care via health information technology.

In the United States, home care clinicians often start the episode of care devoid of relevant fall-risk information. By collecting and analyzing qualitative data from 30 clinicians in one home health agency, this case study aimed to understand how the currently adopted information technology solutions supported the clinicians' fall-risk management (FRM) information domains, and explored opportunities to adopt other solutions to better support FRM. The currently adopted electronic health record system and fall-reporting application served only some information domains with a limited capacity. Substantial improvement in addressing the FRM information domains is possible by effectively modifying the existing solutions and purposefully adopting new solutions.

Assessing the Quality, Privacy, and Security of Breast Cancer Apps for Arabic Speakers: Systematic Search and Review of Smartphone Apps.

BACKGROUND: Breast cancer is a widespread disease, and its incidence is rapidly increasing in the Middle East and North Africa region. With the increasing availability of smartphone apps for various health purposes, breast cancer apps have emerged as tools for raising awareness, providing support, and empowering women affected by this disease. These apps offer many features, including information on breast cancer risk factors, self-examination guides, appointment reminders, and community support groups or hotlines. Using apps raises the risk of privacy and security issues, and we hope that examining these features of the apps will contribute to the understanding of how technology can be used to improve these apps and provide insights for future development and improvement of breast cancer apps. OBJECTIVE: This study aims to critically review the quality, privacy, and security of breast cancer apps available to Arabic speakers. METHODS: Similar to several recent studies, we used a systematic search for apps available in Google Play and Apple App stores using both the web interface and the built-in native stores installed on smartphones. The search was conducted in mid-December 2022 in Arabic using the following keywords: سرطان الثدي - فحص سرطان الثدي - علاج سرطان الثدي - مرض سرطان الثدي - أعراض سرطان الثدي - فحص الثدي (breast cancer, breast cancer treatment, breast cancer disease, breast cancer symptoms, breast cancer screening, and breast test). These preidentified search terms are based on earlier work concerning the top searched breast cancer topics by Arabic speakers through Google's search engine. We excluded apps that did not have an Arabic interface, were developed for non-Arabic speakers, were paid, needed a subscription, or were directed toward health care workers. The Mobile App Rating Scale was used to evaluate the quality of the apps concerning their engagement, functionality, aesthetics, and information. A risk score was calculated for the apps to determine their security risk factors. RESULTS: Only 9 apps were included, with most (6/9, 67%) being supported by advertisements and categorized as informational. Overall, the apps had low numbers of downloads (>10 to >1000). The majority of the included apps (8/9, 89%) requested dangerous access permissions, including access to storage, media files, and the camera. The average security score of the included apps was 3.22, while only 2 apps provided information about data security and privacy. The included apps achieved an overall average quality score of 3.27, with individual dimension scores of 4.75 for functionality, 3.04 for information, 3.00 for aesthetics, and 2.32 for engagement. CONCLUSIONS: The limited availability of breast cancer apps available to Arabic speakers should be a call to action and prompt health care organizations and developers to join forces and collaboratively develop information-rich, usable, functional, engaging, and secure apps.

Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study.

BACKGROUND: Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest. OBJECTIVE: In this paper, we propose a machine learning-based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study. METHODS: We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance). RESULTS: After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: "virus of COVID-19," "risk factors of COVID-19," "prevention of COVID-19," "treatment of COVID-19," "health care delivery during COVID-19," "and impact of COVID-19." The most prominent topic, observed in over half of the analyzed studies, was "the impact of COVID-19." CONCLUSIONS: The proposed machine learning-based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.

Technological Challenges and Solutions in Emergency Remote Teaching for Nursing: An International Cross-Sectional Survey.

OBJECTIVES: With the sudden global shift to online learning modalities, this study aimed to understand the unique challenges and experiences of emergency remote teaching (ERT) in nursing education. METHODS: We conducted a comprehensive online international cross-sectional survey to capture the current state and firsthand experiences of ERT in the nursing discipline. Our analytical methods included a combination of traditional statistical analysis, advanced natural language processing techniques, latent Dirichlet allocation using Python, and a thorough qualitative assessment of feedback from open-ended questions. RESULTS: We received responses from 328 nursing educators from 18 different countries. The data revealed generally positive satisfaction levels, strong technological self-efficacy, and significant support from their institutions. Notably, the characteristics of professors, such as age (p = 0.02) and position (p = 0.03), influenced satisfaction levels. The ERT experience varied significantly by country, as evidenced by satisfaction (p = 0.05), delivery (p = 0.001), teacher-student interaction (p = 0.04), and willingness to use ERT in the future (p = 0.04). However, concerns were raised about the depth of content, the transition to online delivery, teacher-student interaction, and the technology gap. CONCLUSIONS: Our findings can help advance nursing education. Nevertheless, collaborative efforts from all stakeholders are essential to address current challenges, achieve digital equity, and develop a standardized curriculum for nursing education.

Exploring the Challenges and Opportunities of Adopting and Using Telemedicine for Diabetes Care and Management: Qualitative Semistructured Interview Study Among Health Care Providers and Patients With Diabetes.

BACKGROUND: Around the world, over half of the global population experiences noncommunicable diseases, resulting in premature death. Health care providers (HCPs) can deliver medical treatment from a distance by using digital advancements such as telemedicine. However, there is a limited understanding of the difficulties and opportunities of implementing telemedicine solutions in different socioeconomic and cultural environments, including Kuwait. OBJECTIVE: The purpose of this study is to (1) examine the obstacles and benefits of telemedicine in the context of diabetes treatment and management, as perceived by both HCPs and patients with diabetes; (2) investigate the nonfunctional requirements for telemedicine applications used in diabetes care and management; and (3) provide suggestions to enhance the integration and adoption of telemedicine in Kuwait's health care system for diabetes care and management. METHODS: The research used a qualitative and exploratory design, with semistructured interviews as the main data collection method. Participants were recruited on the internet through social media platforms due to the COVID-19 pandemic. The results were analyzed using thematic analysis and the Framework Method. The "diffusion of innovation" model was used as a perspective to interpret the findings. RESULTS: A total of 20 participants were included in this study-10 HCPs and 10 patients with diabetes-all of whom supported telemedicine. The HCPs reported that many diabetes cases could be managed through telemedicine, with only a few requiring in-person visits. Patients with diabetes noted the convenience and time-saving aspect of telemedicine. Both groups recommended the creation of a secure and user-friendly telemedicine system similar to popular social media platforms. Additionally, participants emphasized the importance of telemedicine during the pandemic as a way to prioritize patient safety. CONCLUSIONS: The results of this study provide valuable insights into the needs and preferences of both HCPs and patients with diabetes in a resource-rich country like Kuwait to embrace telemedicine. The COVID-19 pandemic has changed the way medical care is provided and has pushed both groups to consider digital solutions for ongoing diabetes management and treatment.

Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions.

The integration of large language models (LLMs), such as those in the Generative Pre-trained Transformers (GPT) series, into medical education has the potential to transform learning experiences for students and elevate their knowledge, skills, and competence. Drawing on a wealth of professional and academic experience, we propose that LLMs hold promise for revolutionizing medical curriculum development, teaching methodologies, personalized study plans and learning materials, student assessments, and more. However, we also critically examine the challenges that such integration might pose by addressing issues of algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, and copyright concerns in medical education. As we navigate the shift from an information-driven educational paradigm to an artificial intelligence (AI)-driven educational paradigm, we argue that it is paramount to understand both the potential and the pitfalls of LLMs in medical education. This paper thus offers our perspective on the opportunities and challenges of using LLMs in this context. We believe that the insights gleaned from this analysis will serve as a foundation for future recommendations and best practices in the field, fostering the responsible and effective use of AI technologies in medical education.

Exploring the Use and Adoption of Wearable Physical Activity Trackers in Oil-Rich Nations: A Qualitative Study of Youth Perspectives from Kuwait.

This study aimed to uncover participants' perspectives about the use of self-tracking wearables and describe the barriers and motivators for adopting them. Qualitative data were collected through semi-structured interviews. The analysis of the transcribed interviewed followed a thematic analysis approach and utilized the Framework method. The findings highlight the importance of wearables to increase physical activity health interventions targeting sedentary lifestyles.

Effectiveness of Serious Games for Improving Executive Functions Among Older Adults With Cognitive Impairment: Systematic Review and Meta-analysis.

BACKGROUND: Executive functions are one of the known cognitive abilities that decline with age. They are the high-order cognitive processes that enable an individual to concentrate, plan, and take action. Serious games, which are games developed for specific purposes other than entertainment, could play a positive role in improving executive functions. Several systematic reviews have pooled the evidence about the effectiveness of serious games in improving executive functions; however, they are limited by some weaknesses. OBJECTIVE: This study aims to investigate the effectiveness of serious games for improving executive functions among older adults with cognitive impairment. METHODS: A systematic review of randomized controlled trials (RCTs) was conducted. To retrieve relevant studies, 8 electronic databases were searched. Further, reference lists of the included studies and relevant reviews were screened, and we checked studies that cited our included studies. Two reviewers independently checked the eligibility of the studies, extracted data from the included studies, assessed the risk of bias, and appraised the quality of the evidence. We used a narrative and statistical approach, as appropriate, to synthesize results of the included studies. RESULTS: Of 548 publications identified, 16 RCTs were eventually included in this review. Of the 16 studies, 14 studies were included in 6 meta-analyses. Our meta-analyses showed that serious games are as effective as no or passive interventions at improving executive functions (P=.29). Surprisingly, conventional exercises were more effective than serious games at improving executive functions (P=.03). Our subgroup analysis showed that both types of serious games (cognitive training games, P=.08; exergames, P=.16) are as effective as conventional exercises at improving executive functions. No difference was found between adaptive serious games and nonadaptive serious games for improving executive functions (P=.59). CONCLUSIONS: Serious games are not superior to no or passive interventions and conventional exercises at improving executive functions among older adults with cognitive impairment. However, our findings remain inconclusive due to the low quality of the evidence, the small sample size in most included studies, and the paucity of studies included in the meta-analyses. Accordingly, until more robust evidence is available, serious games should not be offered by health care providers nor used by patients for improving executive functions among older adults with cognitive impairment. Further reviews are needed to assess the long-term effect of serious games on specific executive functions or other cognitive abilities among people from different age groups with or without cognitive impairment. TRIAL REGISTRATION: PROSPERO CRD42021272757; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=272757.

A procurement-based classification of pharmaceutical supplies for diabetes disease management.

Background and Aims: Diabetes is among the most prevalent noncommunicable chronic diseases globally and carries a substantial expense in worldwide health care. Pharmaceutical supplies related to diabetes management account for 20%-40% of the disease's management cos, and this percentage continues to increase. This study examines the pharmaceutical expenses associated with one of the most common chronic diseases: diabetes. Specifically, we measure the extent to which patient health and demographic factors drive the annual cost of pharmaceutical supplies for diabetes management. Second, the study applied a procurement-centric classification scheme to pharmaceutical items involved in diabetes treatment. Methods: Data on 98,648 pharmaceutical-dispensing transactions (related to 2828 patients) over 1 year were collected from a specialized diabetes health center. Pharmaceutical prices from the sample were compared internationally to ensure that the findings apply to other countries. The association between the item cost and the number of unique patients prescribed pharmaceutical products was estimated at the category and subcategory levels. Results: Approximately 80% of total pharmaceutical expenditures were attributed to 20% of patients. Two of 20 pharmaceutical categories-anti-diabetes drugs and insulin-accounted for 34% of products dispensed and 57% of total pharmaceutical expenditures. Age, body mass index, and diabetes type were essential factors in predicting supply cost per patient. Conclusion: Applying the portfolio purchasing model also suggested that some clinically similar items, like insulin types, are best procured through divergent procurement strategies or vendors for optimal cost efficiency. A better understanding of the diverse array of diabetes supplies can reveal opportunities for better strategic supply management. This supply classification approach can also be applied in other supply-intensive specialties, such as orthopedics.

Diabetes Self-management Apps: Systematic Review of Adoption Determinants and Future Research Agenda.

BACKGROUND: Most diabetes management involves self-management. Effective self-management of the condition improves diabetes control, reduces the risk of complications, and improves patient outcomes. Mobile apps for diabetes self-management (DSM) can enhance patients' self-management activities. However, they are only effective if clinicians recommend them, and patients use them. OBJECTIVE: This study aimed to explore the determinants of DSM apps' use by patients and their recommendations by health care professionals (HCPs). It also outlines the future research agenda for using DSM apps in diabetes care. METHODS: We systematically reviewed the factors affecting the adoption of DSM apps by both patients and HCPs. Searches were performed using PubMed, Scopus, CINAHL, Cochrane Central, ACM, and Xplore digital libraries for articles published from 2008 to 2020. The search terms were diabetes, mobile apps, and self-management. Relevant data were extracted from the included studies and analyzed using a thematic synthesis approach. RESULTS: A total of 28 studies met the inclusion criteria. We identified a range of determinants related to patients' and HCPs' characteristics, experiences, and preferences. Young female patients were more likely to adopt DSM apps. Patients' perceptions of the benefits of apps, ease of use, and recommendations by patients and other HCPs strongly affect their intention to use DSM apps. HCPs are less likely to recommend these apps if they do not perceive their benefits and may not recommend their use if they are unaware of their existence or credibility. Young and technology-savvy HCPs were more likely to recommend DSM apps. CONCLUSIONS: Despite the potential of DSM apps to improve patients' self-care activities and diabetes outcomes, HCPs and patients remain hesitant to use them. However, the COVID-19 pandemic may hasten the integration of technology into diabetes care. The use of DSM apps may become a part of the new normal.