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Abstract Background Treatment with effective antiretroviral therapy (ART) reduces viral load as well as HIV-related morbidity and mortality in HIV-positive patients. Despite the expanded availability of antiretroviral therapy around the world, virological failure remains a serious problem for HIV-positive patients. Thus, Machine learning predictive algorithms have the potential to improve the quality of care and predict the needs of HIV patients by analyzing huge amounts of data, and enhancing prediction capabilities. This study used different machine learning classification algorithms to predict the features that cause virological failure in HIV-positive patients. Method An institution-based secondary data was used to conduct patients who were on antiretroviral therapy at the University of Gondar Comprehensive and Specialized Hospital from January 2020 to May 2022. Patients’ data were extracted from the electronic database using a structured checklist and imported into Python version three software for data pre-processing and analysis. Then, seven supervised classification machine-learning algorithms for model development were trained. The performances of the predictive models were evaluated using accuracy, sensitivity, specificity, precision, f1-score, and AUC. Association rule mining was used to generate the best rule for the association between independent features and the target feature. Result Out of 5264 study participants, 1893 (35.06%) males and 3371 (64.04%) females were included. The random forest classifier (sensitivity = 1.00, precision = 0.987, f1-score = 0.993, AUC = 0.9989) outperformed in predicting virological failure among all selected classifiers. Random forest feature importance and association rules identified the top eight predictors (Male, younger age, longer duration on ART, not taking CPT, not taking TPT, secondary educational status, TDF-3TC-EFV, and low CD4 counts) of virological failure based on the importance ranking, and the CD-4 count was recognized as the most important predictor feature. Conclusion The random forest classifier outperformed in predicting and identifying the relevant predictors of virological failure. The results of this study could be very helpful to health professionals in determining the optimal virological outcome.

Abstract Background Globally, 38% of contraceptive users discontinue the use of a method within the first twelve months. In Ethiopia, about 35% of contraceptive users also discontinue within twelve months. Discontinuation reduces contraceptive coverage, family planning program effectiveness and contributes to undesired fertility. Hence understanding potential predictors of contraceptive discontinuation is crucial to reducing its undesired outcomes. Predicting the risk of discontinuing contraceptives is also used as an early-warning system to notify family planning programs. Thus, this study could enable to predict and determine the predictors for contraceptive discontinuation in Ethiopia. Methodology Secondary data analysis was done on the 2016 Ethiopian Demographic and Health Survey. Eight machine learning algorithms were employed on a total sample of 5885 women and evaluated using performance metrics to predict and identify important predictors of discontinuation through python software. Feature importance method was used to select top predictors of contraceptive discontinuation. Finally, association rule mining was applied to discover the relationship between contraceptive discontinuation and its top predictors by using R statistical software. Result Random forest was the best predictive model with 68% accuracy which identified the top predictors of contraceptive discontinuation. Association rule mining identified women's age, women’s education level, family size, husband’s desire for children, husband’s education level, and women’s fertility preference as predictors most frequently associated with contraceptive discontinuation. Conclusion Results have shown that machine learning algorithms can accurately predict the discontinuation status of contraceptives, making them potentially valuable as decision-support tools for the relevant stakeholders. Through association rule mining analysis of a large dataset, our findings also revealed previously unknown patterns and relationships between contraceptive discontinuation and numerous predictors.

Objectives Personal health record systems allow users to manage their health information in a confidential manner. However, there is little evidence about healthcare providers’ intentions to use such technologies in resource-limited settings. Therefore, this study aimed to assess predicting healthcare providers’ acceptance of electronic personal health record systems.Methods An institutional-based cross-sectional study was conducted from 19 July to 23 August 2022 at teaching hospitals in the Amhara regional state of Ethiopia. A total of 638 health professionals participated in the study. Simple random sampling techniques were used to select the study participants. Structural equation modelling analysis was employed using AMOS V.26 software.Result Perceived ease of use had a significant effect on the intention to use electronic personal health records (β=0. 377, p

BackgroundThe strategic plan of the Ethiopian Ministry of Health recommends an electronic medical record (EMR) system to enhance health care delivery and streamline data systems. However, only a few exhaustive systematic reviews and meta-analyses have been conducted on the degree of EMR use in Ethiopia and the factors influencing success. This will emphasize the factors that make EMR effective and increase awareness of its widespread use among future implementers in Ethiopia. ObjectiveThis study aims to determine the pooled estimate of EMR use and success determinants among health professionals in Ethiopia. MethodsWe developed a protocol and searched PubMed, Web of Sciences, African Journals OnLine, Embase, MEDLINE, and Scopus to identify relevant studies. To assess the quality of each included study, we used the Joanna Briggs Institute quality assessment tool using 9 criteria. The applicable data were extracted using Microsoft Excel 2019, and the data were then analyzed using Stata software (version 11; StataCorp). The presence of total heterogeneity across included studies was calculated using the index of heterogeneity I2 statistics. The pooled size of EMR use was estimated using a random effect model with a 95% CI. ResultsAfter reviewing 11,026 research papers, 5 papers with a combined total of 2439 health workers were included in the evaluation and meta-analysis. The pooled estimate of EMR usage in Ethiopia was 51.85% (95% CI 37.14%-66.55%). The subgroup study found that the northern Ethiopian region had the greatest EMR utilization rate (58.75%) and that higher (54.99%) utilization was also seen in publications published after 2016. Age groups

Introduction: Health information utilization is a global approach to improving the healthcare service quality. However, health information utilization has been challenging in developing countries due to health professionals having limited access to appropriate data. Objective: Therefore this study aimed to assess health information utilization and associated factors among health professionals in North Gondar Zone Hospitals. Methods: An institution-based cross-sectional survey study was conducted among 335 health professionals from June 1 to July 3, 2022. The data were analyzed using SPSS version 26 software. Data was collected using pretested interviewer-administered questionnaires. Data were entered with epi data 4.6 versions and cleaned and analyzed using SPSS version 26. Bi-variable and multivariable logistic regression was carried out to see the effect of explanatory variables on the outcome variable. The strength of the association between the dependent and independent variables was assessed using the odds ratio, and statistical significance was determined using a p-value of less than 0.05. Results: A total of 327 study subjects participated in this study with a 97.6% response rate. About 64%(95%; CI = 58.4, 68.8) of the participants had good health information utilization. Discuss feedback at a meeting (AOR = 2.65, 95% CI = 1.31–5.39), collect and store data using HMIS tools was (AOR = 2.13, 95% CI = 1.10–4.10), and respondents who had check data quality (AOR = 4.54, 95% CI = 2.31, 8.92) were found to be the significant factors associated with health information utilization. Conclusion: Overall, this study finding showed that the level of health information utilization was moderate. The following points are forwarded to improve health information utilization such as; discuss feedback, check data quality, and use HMIS tools.

Background: Nursing care practice in most developing countries is challenged due to a lack of effective methods, physical limitations, and a shortage of nurses’ professionals to provide nursing care for patients. But there is no evidence about awareness and knowledge of telenursing care. So, this study aimed to fill the evidence gap regarding telenursing awareness or knowledge among nurses in resource limited settings. Method: An institution-based cross-sectional study was conducted among 422 nurses at a specialized teaching referral hospital from May 16 to July 18, 2022. A simple random sampling technique was applied to select study participants. Descriptive statistics were done through a table and a bar graph. A binary logistic regression analysis was done, and 95% confidence intervals with a P-value of less than 0.05 were employed to identify factors associated with telenursing awareness and knowledge. Result: Four hundred fifteen (415) nurses were included for analysis, with a response rate of 98.5%. Among study participants, 49.4% and 45.8% of them had good awareness and knowledge toward telenursing care, respectively. Educational level, job experience, source of information, digital training, internet access, and computer training were factors associated with a nurse's awareness of telenursing care. Moreover, nurses' awareness, social media use, experience contacting clients or patients online, source of information, research, digital training, and computer training were factors associated with nurses' knowledge of telenursing care. Conclusion: and recommendation: Overall, nurses' awareness and knowledge about telenursing care were low. Considering factors and the user's preference could inform the implementation strategy for improving nurses' awareness and knowledge regarding telenursing care for future acceptance, use, and implantation.

Introduction: The burden of diabetes mellitus is increasing in Africa. Wearables have a proven track record of combating chronic diseases. However, little is known about patients’ intentions to use such technologies in resource-limited settings. Therefore, this study aimed to assess DM patients' intentions to use wearable health devices and its predictors in Ethiopia. Methods: An institutional-based cross-sectional study was conducted among 924 diabetes mellitus patients from May 19 to June 23, 2022, at referral hospitals in the Amhara regional state of Ethiopia. An interviewer-administered questionnaire was used to collect the data using the Kobo Collect app. A systematic random sampling technique was used to select the study participants. Descriptive statistics were done using SPSS version 25 software and presented using tables and pie charts. Structural equation modeling analysis with SPSS AMOS version 26 software was employed to identify predictors associated with the intention to use wearable health devices in Ethiopia. Results: A total of 883 diabetes mellitus patients, with a 95.56% response rate, participated in the study. The proportion of intention to use wearable health devices was 47.1%, 95% CI (43.7–50.5). Effort expectancy (β = 0.543, P

Background: Among currently available technologies, only the Internet has the potential to deliver universal access to up-to-date health care information. This study aimed to assess the status of Internet utilization to access health information and its associated factors among university students in Ethiopia. Methods: A facility-based cross-sectional study was conducted among 845 undergraduate students selected by Stratified multi-stage sampling from 19 randomly chosen departments of Addis Ababa University. A pretested structured interviewer-administered questionnaire was used to collect the data. The data were entered into EPI Info version 7 and exported to the SPSS version 23 for analysis. A binary logistic regression analysis was conducted to assess the association between the study and outcome variables. Result: Almost all 761 (98.8%) university students have used the Internet. Among these, the overall proportion of Internet use for health information was 59.1% (95% CI: 55.5–62.7). Respondents with good eHealth literacy (AOR = 1.656, 95% CI: (1.143, 2.397), student's field of study (AOR = 0.025, 95% CI: (0.007, 0.087), year of study (AOR = 1.609, 95% CI: (1.130, 2.290), and owning computer device (AOR = 2.314, 95% CI: (1.392, 3.844) were found to be significantly associated with utilization of Internet for health information. Conclusion: This study confirms that nearly two-thirds of undergraduate university students use the Internet to access health information. eHealth literacy, the field of study, year of study, and ownership of electronic devices were significant predictors for Internet use for accessing health information. As a result, it is recommended that university students in Ethiopia have access to electronic devices and that eHealth literacy be promoted to use the Internet as a source of health information effectively.

Background: Universal health coverage demands universal access to health information. However, in developing nations, a lack of health information continues to be a significant obstacle to evidence-based health care. As a result, the objective of this study was to determine the pooled level of health information seekers in Ethiopia and its associated factors. Methods: Studies were found using search engines in Medline, PubMed, Scopus, EMBASE, and African journal online (AJOL), HINARI, Science direct, and web-science, all following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA). The study was performed using STATA version 11 software. The index of heterogeneity (I2) was used to assess heterogeneity amongst the included papers. A visual inspection of the funnel plot and Egger's regression test were used to search for potential publication bias. The pooled effect size of each study is estimated using a random-effect model meta-analysis with a 95% confidence interval. Results: Eight out of the 197 assessed studies met the inclusion criteria and were included in the systematic review and meta-analysis. The estimated prevalence of health information seekers in Ethiopia was 50.45% (95% CI: 39.68–61.23). According to the subgroup analysis, health information seekers accounted for 52.48% in the Amhara area, 57.45% among health professionals, and 53.77% in studies conducted after 2015. Furthermore, having computer and internet access and being computer literate were significant associations with health information seeking in Ethiopia. Conclusion: The overall pooled prevalence of health information seekers in Ethiopia was relatively low. Having computer and internet access and being computer literate was a positive association with health information seeking. As a result, offering computer and internet access and a training package on basic computer skills could be recommended to raise the overall number of health information seekers in Ethiopia.