BackgroundAntenatal care (ANC) is one of the components of care to be provided to pregnant women. In Ethiopia, characterizing the spatial distribution of antenatal care utilization is essential to prioritize risk areas where ANC is needed and facilitate interventions. Therefore, this spatial analysis was performed to assess the spatial distribution of ANC utilization between 2000 and 2011 and to identify factors associated with ANC utilization in Ethiopia.MethodsA total of 23,179 women who had a live birth in the five years preceding the surveys were included in the study. The spatial data were created in ArcGIS10.1 for each study clusters. The Bernoulli model was used by applying Kulldorff methods using the SaTScan™ software to analyze the purely spatial clusters of ANC utilization. Multiple logistic regression analysis was used to identify predictors affecting ANC utilization.ResultsANC utilization had spatial variations across the country. Spatial scan statistics identified 49 high performing clusters (LLR = 111.92, P < 0.001) in 2000, 51 (LLR = 114.49, P < 0.001) in 2005 and, 86 (LLR = 121.53, P < 0.001) in 2011. ANC utilization was higher among mothers; with richest wealth quintiles, lowest number of birth order, who are living in urban areas, younger and educated.ConclusionThese results provide further insight into differences in ANC utilization in the country and highlight high and modest performing clusters. This could enable efficient and timely spatial targeting to improve ANC service up take in Ethiopia.

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 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.

The use of health information technology significantly enhances patient outcomes. As a result, policymakers from developing countries have placed strong emphasis on formulating eHealth policies and initiatives. However, there have not been many successful deployments to show for. The role of individual factors in the successful implementation of these technologies is indispensable. Therefore, this study assesses healthcare professionals’ knowledge, attitudes, and practice of health information technology. An institution-based cross-sectional study was conducted at the University of Gondar Comprehensive Specialized Hospital from November 15 to December 29, 2020. A structured, self-administered questionnaire was used to collect data. Student’s t -test was used to learn if there were any significant differences in practice habits between participants with and without previous information technology-related training. In addition, first-order partial correlation was conducted to identify the relationship of knowledge and attitude with practice. A total of 347 health professionals responded to the questionnaire, yielding an 87.2% response rate. Most health professionals are not aware of how to use health information technologies. Notably, practice levels were low and needed prompt action from responsible authorities. Previous training did not work very well to improve the practice levels of health professionals. However, the positive attitude of these professionals encourages policymakers and implementers to engage closely.