Background: The implementation of eHealth systems with a trial-and-error approach is very expensive and unsuccessful. So, this study aims to examine the constructs and relationships of the modified technology acceptance model (TAM) to determine whether it can be applied to assess health professional’s behavioral intention to adopt eHealth systems in resource-limited settings or not. Methods: The institutional-based cross-sectional study design was conducted among a total of 384 healthcare professionals in referral hospitals of Amhara regional state, Ethiopia. Self-administered questionnaire was used to collect the data, and the data were entered using Epi-info version 7 and the descriptive data were analyzed using SPSS version 25. Structural equation modeling, using AMOS 22, was also applied to describe and validate the degree of relationships between variables. Results: The findings of the structural equation modeling (SEM) indicate that perceived usefulness has a significant influence on attitude (β =0.298, P< 0.01) and intention to use eHealth (β =0.387, P< 0.01). Perceived ease of use has significant influence on perceived usefulness (β=0.385, P< 0.05) and attitude (β=0.347, P< 0.05) and intention to use eHealth (β=0.339, P< 0.01). Technical infrastructure has significant influence on attitude (β =0.412, P< 0.01) and intention to use eHealth (β =0.355, P< 0.01). The staffs IT experience has a significant influence on perceived usefulness (β =0.595, P< 0.01) and attitude (β =0.267, P< 0.05), but the effect of IT experience on the intention to use eHealth was not significant. Among all the constructs, healthcare professionals attitude towards eHealth showed the strongest effect on the intention to use eHealth systems (β = 0.52, P< 0.01). Conclusion: Overall, this model describes 56.2% of the variance in behavioral intention to use eHealth systems. Therefore, the implementers should give priority in enhancing the organizations technical infrastructure, staff’s IT skill, and their attitude towards eHealth by giving continuous support.

Background. Electronic medical record (EMR) systems offer the potential to improve health care quality by allowing physicians real‐time access to patient healthcare information. The endorsement and usage of EMRs by physicians have a significant influence on other user groups in the healthcare system. As a result, the purpose of this study was to examine physicians’ attitudes regarding EMRs and identify the elements that may influence their attitudes. Method. An institutional‐based cross‐sectional study design supplemented with a qualitative study was conducted from March 1 to April 30, 2018, among a total of 403 physicians. A self‐administered questionnaire was used to collect quantitative data. The validity of the prediction bounds for the dependent variable and the validity of the confidence intervals and P values for the parameters were measured with a value of less than 0.05 and 95 percent of confidence interval. For the supplementary qualitative study, data were collected using semistructured in‐depth interviews from 11 key informants, and the data were analyzed using thematic analysis. Result. Physicians’ computer literacy (CI: 0.264, 0.713; P: 0001) and computer access at work (CI: 0.141, 0.533, P: 0.001) were shown to be favorable predictors of their attitude towards EMR system adoption. Another conclusion from this study was the inverse relationship between physicians’ prior EMR experience and their attitude about the system (CI: ‐0.517, ‐0.121; P: 0.002). Conclusion. According to the findings of this study, physicians’ attitudes regarding EMR were found moderate in the studied region. There was a favorable relationship between computer ownership, computer literacy, lack of EMR experience, participation in EMR training, and attitude towards EMR. Improving the aforementioned elements is critical to improving physicians’ attitudes regarding EMR.

Background: During the pandemic, information is inevitable and important, and having knowledge on pertinent information is very crucial for health-care professionals in order to deliver sustainable and quality health services to their clients, and to combat the virus. Therefore, the aim of this study was to assess the health-care providers’ information seeking behavior on COVID-19 pandemic. Methods: A descriptive cross-sectional study had conducted among 291 respondents. The participant’s characteristics were presented using frequency and percentage. The association between the independent and dependent variables was explained using chi-squared test. Results: Out of the 291 respondents, 207 (71.1%) of them had sought information on COVID-19 pandemic. The association between the gender and educational status with the outcome variable was found insignificant. Age (χ²=10.6, P=0.011), respondent’s working unit (χ²=33.7, P< 0.001), profession (χ²=12.7, P=0.013), taking computer training (χ²=17.44, P=0.000), computer access at work place (χ²=7.28, P=0.007), internet access at work place (χ²=22.2, P< 0.001), and frequency of internet use (χ²=17.63, P< 0.001) were found significant with COVID-19 information seeking. Conclusion: Information seeking behavior was positively associated with computer access, computer training, internet access and frequency of use. Therefore, comprehensive package is required to build the capacity of care providers on computer-related skills. Besides, institutions need to have internet and computer access.

Background: The 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. Objective: This study aims to determine the pooled estimate of EMR use and success determinants among health professionals in Ethiopia. Methods: We 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. Results: After 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 <30 years, access to an EMR manual, EMR-related training, and managerial support were identified factors associated with EMR use among health workers. Conclusions: The use of EMR systems in Ethiopia is relatively low. Belonging to a young age group, accessing an EMR manual, receiving EMR-related training, and managerial support were identified as factors associated with EMR use among health workers. As a result, to increase the use of EMRs by health care providers, it is essential to provide management support and an EMR training program and make the EMR manual accessible to health professionals.

Background: A dearth of studies showed that infectious diseases cause the majority of deaths among under-five children. Worldwide, Acute Respiratory Infection (ARI) continues to be the second most frequent cause of illness and mortality among children under the age of five. The paramount disease burden in developing nations, including Ethiopia, is still ARI. Objective: This study aims to determine the magnitude and predictors of ARI among under-five children in Ethiopia using used state of the art machine learning algorithms. Methods: Data for this study were derived from the 2016 Ethiopian Demographic and Health Survey. To predict the determinants of acute respiratory infections, we performed several experiments on ten machine learning algorithms (random forests, decision trees, support vector machines, Naïve Bayes, and K-nearest neighbors, Lasso regression, GBoost, XGboost), including one classic logistic regression model and an ensemble of the best performing models. The prediction ability of each machine-learning model was assessed using receiver operating characteristic curves, precision-recall curves, and classification metrics. Results: The total ARI prevalence rate among 9501 under-five children in Ethiopia was 7.2%, according to the findings of the study. The overall performance of the ensemble model of SVM, GBoost, and XGBoost showed an improved performance in classifying ARI cases with an accuracy of 86%, a sensitivity of 84.6%, and an AUC-ROC of 0.87. The highest performing predictive model (the ensemble model) showed that the child’s age, history of diarrhea, wealth index, type of toilet, mother’s educational level, number of living children, mother’s occupation, and type of fuel they used were an important predicting factor for acute respiratory infection among under-five children. Conclusion: The intricate web of factors contributing to ARI among under-five children was identified using an advanced machine learning algorithm. The child’s age, history of diarrhea, wealth index, and type of toilet were among the top factors identified using the ensemble model that registered a performance of 86% accuracy. This study stands as a testament to the potential of advanced data-driven methodologies in unraveling the complexities of ARI in low-income settings.

Background: To prevent the spread of COVID-19 and carry out a successful vaccination program especially in low-income countries, people must have faith on scientists and health experts. The most significant challenge to vaccination programs’ efficacy is now regarded to be a lack of information and trust in immunization due to myths and misinformation spread in the community. Therefore, this study aimed to identify the myth and misconceptions that are propagated about the COVID-19 vaccine, the refusal rate of the vaccine and determine the factors associated with COVID-19 vaccine refusal. Methods: A community-based cross-sectional study was conducted from December 7 to January 25, 2022. Face-to-face interviews with a standardized questionnaire were used to collect data on the variables. Data were entered into the statistical tool Epi data version 3.1 and then exported to SPSS version 25 for analysis. Binary logistic regression, both bivariable and multivariable, was conducted. In the multivariable binary logistic regression model, the adjusted odds ratio with 95% confidence interval was used to declare statistically significant factors based on a p value less than 0.05. Results: Out of the total 574 respondents, 60.3% [95% CI (55.5, 64.2)] of them refused to take COVID-19 vaccine. In this study, respondent’s age [AOR = 2.1 at 95% CI: (1.8, 4.9)], perception on COVID-19 vaccine [AOR = 3.0 at 95 CI: (1.9, 4.6)], eHealth literacy [AOR = 2.7 at 95% CI: (1.7, 4.1)], source of information about the vaccine [AOR = 2.9 at 95% CI: (1.9, 4.4)], computer literacy [AOR = 2.8 at 95 CI: (1.8, 4.2)] and frequency of internet use [AOR = 2.2 at 95 CI: (1.8, 5.3)] were identified as determinant factors for COVID-19 vaccine acceptance. Conclusion: Factors like eHealth literacy, source of information about the vaccine, frequency of internet use, respondent’s perception about the vaccine and computer literacy were found to be determinant factors for COVID-19 vaccine acceptance.