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Background Patient portal technology is increasingly utilized in the health care system for diabetes management as a means of communication and information-sharing tools, and it has the potential to improve access, quality, and outcomes for patients with diabetes. However, its adaptation is very low, and patients’ intention toward the patient portal is unknown. This study aims to fill this gap by determining the intention to use the patient portal and its predictors among patients with diabetes in Ethiopia. Method An institution-based cross-sectional study was conducted on patients with diabetes from April 3 to May 8 in eight referral hospitals in the Amhara region, Ethiopia 2023. Samples were proportionally allocated for each hospital, and participants were selected by using a systematic random sampling method. The data were collected by using an interviewer-administered questionnaire using the Kobo collection mobile app. Descriptive statistics were performed using SPSS version 26. The degree of association between exogenous and endogenous variables was assessed and validated using structural equation modeling using AMOS version 21. Result A total of 1037 (96.2% response rate) patients with diabetes participated in the study. Of them, 407 (39.25%), 95% CI: [36.4–42.2] were found to have an intention to use the patient portal. Digital literacy (β = 0.312, 95% CI: [0.154–0.465], p < .01), performance expectancy (β = 0.303, 95% CI: [0.185–0.420], p < .01), effort expectancy (β = 0.25, 95% CI: [0.131–0.392], p < 0.01) facilitating condition (β = 0.22, 95% CI: [0.081–0.36], p < .01) and habit (β = 0.111, 95% CI: [−0.009 to 0.227], p < .05) were significantly associated with the intention to use patient portals. Effort expectancy and facilitating conditions were positively moderated by gender. Conclusion This study found that patient with diabetes’ intention to use patient portals was low. To increase patients with diabetes’ intention to use the patient portal, interventions in digital literacy, performance expectations, effort expectations, facilitating conditions, and habits are required.

IntroductionGynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications.MethodsThe study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model.ResultsThe review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias.ConclusionThis review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.

Abstract Background Healthcare providers across all clinical practice settings are progressively relying and adapting information communication technologies to perform their professional activities. In this era of technology, healthcare providers especially in lower income countries should have at least basic digital competency if a successful application of technology is to be achieved. The aim of this study was to assess digital competency of healthcare providers among seven public health centers in North-West Ethiopia. Methods A cross-sectional study design was applied to assess the basic digital competency of healthcare providers working in seven public health centers in North-west Amhara regional state, Ethiopia. Self-administered questionnaire adopted from the European commission’s digital competency framework for assessing digital competency were used. A multivariable logistic regression was performed to identify factors associated with basic digital competency with p-value 0 .7. Healthcare providers in those public health centers reported that problem solving, safety and communication are the most common challenges encountered. The multivariable logistic regression model indicated that factors such as sex, educational status, profession type, monthly income and years of experience are statistically significant predictors. Conclusion Basic digital competency level of healthcare providers working in public health centers in this setting is relatively low. The results highlight the need to improve digital competency among healthcare providers focusing on the identified skill gaps.

Abstract Background Chronic patients persistently seek for health information on the internet for medication information seeking, nutrition, disease management, information regarding disease preventive actions and so on. Consumers ability to search, find, appraise and use health information from the internet is known as eHealth literacy skill. eHealth literacy is a congregate set of six basic skills (traditional literacy, health literacy, information literacy, scientific literacy, media literacy and computer literacy). The aim of this study was to assess eHealth literacy level and associated factors among internet user chronic patients in North-west Ethiopia. Methods Institutional based cross-sectional study design was conducted. Stratified sampling technique was used to select 423 study participants among chronic patients. The eHealth literacy scale (eHEALS) was used for data collection. The eHEALS is a validated eight-item Likert scaled questionnaire used to asses self-reported capability of eHealth consumers to find, appraise, and use health related information from the internet to solve health problems. Statistical Package for Social science version 20 was used for data entry and further analysis. Multivariable logistic regression was used to examine the association between the eHealth literacy skill and associated factors. Significance was obtained at 95% CI and p

Abstract Background The World Health Organization estimates that 29% of under-five mortality could be prevented with existing vaccines. However, non-consistent attendance for immunization appointments remains a global challenge to healthcare providers. Thus, innovative strategies are required to reach the last mile where technology could be effectively utilized to achieve better compliance with children immunization schedules. Therefore, the aim of the review was to systematically collect and summarize the available evidence on the effectiveness of text message reminders on childhood vaccination. Methods This review was conducted according to a priori published protocol on PROSPERO. A systematic literature search of databases (PubMed/MEDLINE, EMBASE, Cochrane/Wiley library, and Science direct) was conducted. Eligibility and risk of bias assessments were performed independently by two reviewers. PRISMA flow diagrams were used to summarize the study selection process. Taking into account the level of heterogeneity, a random effects model was used and risk ratios with their 95% CI were used to present the pooled estimates. To investigate the sources of heterogeneity, subgroup analysis and meta-regression analysis were also considered. In this review, publication bias was assessed statistically using Harbord test. Results A total of 1771 articles were searched. Out of those 1771 articles, 558 duplicated articles were removed. About 1213 articles were further screened, and finally, ten articles met the inclusion criteria. The meta-analysis showed that there is a significant effect of text message reminders on childhood vaccination coverage (RR = 1.11; 95% CI 1.05–1.17) with a moderate level of heterogeneity (I 2 = 64.3%, P = 0.003). The results from the Harbord test suggested that there is no evidence for publication bias (P = 0.340). Conclusion This review highlights the potential benefits of incorporating mobile text message reminders into the standard management of childhood immunizations, especially in low- and middle-income countries. The frequency and timing of the text message reminders are also crucial in determining the effectiveness of text message reminders. Hence, mHealth interventions deserve more attention as a potential innovation to improve healthcare programs. Systematic review registration PROSPERO CRD42017074230