Knowledge of telemedicine and its associated factors among health professional in Ethiopia: A systematic review and meta-analysis.

INTRODUCTION: Telemedicine is a useful tool for decreasing hospital stress, patient suffering, ambulance needs, hospital anxiety, and costs while improving the standard of care. Nonetheless, the lack of awareness regarding telemedicine poses a barrier to its application, presenting several difficulties in underdeveloped nations like Ethiopia. This review evaluates Ethiopian-specific telemedicine knowledge and associated factors. METHODS: This systematic review was conducted using a search of several online databases in addition to the main databases, like Medline, PubMed, Scopus, and Science Direct. The writers have looked for, reviewed, and summarized information about telemedicine knowledge in the healthcare system. This study contained seven studies that examined telemedicine knowledge in the Ethiopian healthcare sector. Studies that followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) were found using search engines. The investigation was carried out using STATA version 11. The indicator of heterogeneity (I2) was used to assess the level of heterogeneity among the included studies. The funnel plot was visually inspected, and Egger's regression test was run to check for publication bias. The pooled effect size of every study is estimated using a random-effect model meta-analysis. RESULTS: Examination of 2160 studies, seven studies involving 2775 health professionals, and seven out of the 2160 publications assessed satisfied the inclusion criteria and were added to the systematic review and meta-analysis. The pooled prevalence of Telemedicine knowledge was 45.20 (95% CI: 34.87-55.53). Whereas the pooled factor was computer training was 2.24 times (AOR = 2.24 (95%; CI: 1.64-3.08)), computer access was 2.07 times (AOR = 2.07 (95% CI: 1.50-2.87)), internet access was 3.09 times (AOR = 3.09 (95% CI: 1.34-7.13)), social media access were 3.09 times (AOR = 3.09(95%; CI: 1.34-7.13)), educational status degree and above were 2.73 times (AOR = 2.73; 95% CI: 0.85-8.82), Awareness were 3.18 times (AOR = 3.18 (95%; CI: 1.02-9.91)), Management support was 1.85 (AOR = 1.85 (95% CI: 01.25-2.75)), computer literacy were 2.90 times (AOR = 2.90 (95% CI: 1.81-4.64)), computer owner were 1.70 times (AOR = 1.70 (95% CI: 1.05-2.76)), male gender were 1.95 times (AOR = 1.95 (95% CI: 1.32-2.87)). CONCLUSION: The overall pooled prevalence of telemedicine knowledge was low. Gender, education, management support, computer access, social media access, internet access, telemedicine awareness, and telemedicine training associated with telemedicine knowledge.

Exploring the interplay between food security and antenatal care utilization among pregnant women in Southern Ethiopia: Insights from an institution-based cross-sectional study.

Background: Ensuring the satisfaction of pregnant women with antenatal care is crucial for positive pregnancy outcomes and their engagement with emerging technologies and alternative care models. Maintaining high satisfaction during the antenatal period significantly impacts the well-being of both the expectant mother and the unborn child. Despite the recognized importance of antenatal care satisfaction, comprehensive information on satisfaction levels and influencing factors in the specific study area is lacking. Therefore, this study aims to assess antenatal care service satisfaction and associated factors among pregnant women in Arba Minch town, southern Ethiopia. Methods: We conducted an institution-based cross-sectional study among 418 pregnant women from December 2022 to January 30, 2023, using a systematic sampling method. Data were collected using the Kobo Toolbox and analyzed with SPSS Version 26. The threshold for statistical significance was set at a p-value of less than 0.05. Results: Out of 418 participants, 54.3% (95% CI=49.4-60.4) expressed satisfaction with antenatal care services. Factors significantly associated with women's satisfaction included: being unable to read and write (AOR=2.37; 95% CI: 1.97-3.80), being aged 25-29 years (AOR=3.20; 95% CI: 1.65-6.22), receiving antenatal care at a hospital (AOR=1.81; 95% CI: 1.05-3.12), having a previous history of antenatal visits (AOR=2.59; 95% CI: 1.26-5.30), a monthly income of 2500-5000 ETB (AOR=1.44; 95% CI: 1.21-3.94), waiting times of less than 30 min (AOR=2.59; 95% CI: 1.52-4.41), maintaining a positive attitude towards antenatal care (AOR=2.50; 95% CI: 1.05-3.65), and having a secure food source (AOR=2.06; 95% CI: 1.13-3.78). Conclusion: Over 54% of participants were satisfied with antenatal care services. To improve satisfaction levels, recommended strategies include enhancing healthcare infrastructure, establishing maternity waiting areas, reducing waiting times, and expanding services to remote areas.

Information seeking behavior on hepatitis B virus, and its associated factors among pregnant women at teaching and specialized hospitals, Northwest Ethiopia: A cross-sectional study.

INTRODUCTION: Hepatitis B virus (HBV) infection continues to be a major public health issue worldwide. Health information-seeking behavior is critical to obtain information about health, diseases such as the Hepatitis B virus, health risks, and health promotion and it has become a major concern of health policymakers. However, there is little evidence of information-seeking behavior on the Hepatitis B virus in Ethiopia. So, this study aimed to assess Hepatitis B virus information-seeking behavior and its associated factors among pregnant women at teaching and Specialized Hospitals, in Northwest Ethiopia. METHODS: An institution-based cross-sectional study was conducted among pregnant women at teaching and specialized hospitals, in Northwest Ethiopia from May 01 to June 01, 2022. A total of 423 participants were selected using a systematic random sampling method. The data was collected through an interview-administered questionnaire by kobo-collect software. Then export into SPSS version 20 for analysis. Descriptive statistics, bi-variable, and multivariable logistic regression analyses were done to identify factors associated with Hepatitis B virus information-seeking behavior. RESULTS: The proportion of information-seeking behavior on the Hepatitis B virus among pregnant women was 40.5% (CI = 35.7, 45.6). Education(diploma and above) [AOR = 3.3, 95% CI (1.31, 8.16)], more than one ANC visit [AOR = 5.99, 95% CI (3.20, 12.31)], smart-phone ownership [AOR = 4.1, 95%CI (1.35, 12.31)], internet access [AOR = 5.1, 95%CI (1.35, 15.60)], perceived susceptibility [AOR = 2.7, 95%CI (1.38, 5.31)], perceived severity [AOR = 3.7, 95%CI (2.06, 6.55)], and self-efficacy [AOR = 1.9, 95%CI (1.03, 3.73)] were factors influencing information seeking on Hepatitis B virus. CONCLUSION: The overall proportion of information-seeking behavior on HBV among pregnant women was low. To improve information-seeking behavior on HBV among pregnant women we should connect the women to the internet and technology. Creating women's awareness about the Hepatitis B virus severity and their venerability and increasing their antenatal care (ANC) visits, self-efficacy, internet access, and women's education can improve information seeking about the Hepatitis B virus.

Intimate partner violence is independently associated with poor utilization of antenatal care in Arba Minch town, southern Ethiopia: A cross-sectional study.

To ensure the best possible health conditions for both mother and fetus throughout pregnancy, skilled healthcare professionals provide antenatal care (ANC) to expectant mothers. Even though the introduction of antenatal care has reduced maternal mortality by 34% since 2002, some atypical behaviors, such as intimate partner violence, have had a significant impact on how often women seek out expert medical treatment during pregnancy. Hence, early identification of such risk factors is very important to decrease maternal mortality from preventable causes. To assess the prevalence and factors of intimate partner violence and associated factors among pregnant women at Arba Minch town, southern Ethiopia. An institution-based cross-sectional study was conducted among 403 mothers who were enrolled from December 1, 2022, to January 30, 2023. The total sample size was allocated proportionately to the number of women attending antenatal care at each public health facility. Thus, systematic sampling was applied. Kobo Toolbox was used for data collection and cleaning, which was then analyzed using IBM SPSS Version 26. Statistical significance was determined at a p-value of less than 0.05. In this study area, the prevalence of intimate partner violence among pregnant women was 35% (95% CI: 30.5-39). The associated factors of intimate partner violence were late initiation of antenatal care (AOR = 3.81, 95% CI: 1.7, 6.04), non-autonomous women (AOR = 1.8, 95% CI: 1.18, 3.14), inadequate antenatal utilization (AOR = 3.41, 95% CI: 1.8, 6.2), and a husband with an extra wife (AOR = 6.0, 95% CI: 4.2, 10.57).This study showed that more than one-third of pregnant women in this study area were facing intimate partner violence. Having extra wife, lack of women's autonomy, less antenatal care utilization and late initiation of antenatal care were associated with Intimate Partner Violence (IPV). Therefore, it is essential to greatly empower women and provide them significant prestige in the home.

Machine learning to predict unintended pregnancy among reproductive-age women in Ethiopia: evidence from EDHS 2016.

BACKGROUND: An unintended pregnancy is a pregnancy that is either unwanted or mistimed, such as when it occurs earlier than desired. It is one of the most important issues the public health system is currently facing, and it comes at a significant cost to society both economically and socially. The burden of an undesired pregnancy still weighs heavily on Ethiopia. The purpose of this study was to assess the effectiveness of machine learning algorithms in predicting unintended pregnancy in Ethiopia and to identify the key predictors. METHOD: Machine learning techniques were used in the study to analyze secondary data from the 2016 Ethiopian Demographic and Health Survey. To predict and identify significant determinants of unintended pregnancy using Python software, six machine-learning algorithms were applied to a total sample of 7193 women. The top unplanned pregnancy predictors were chosen using the feature importance technique. The effectiveness of such models was evaluated using sensitivity, specificity, accuracy, and area under the curve. RESULT: The ExtraTrees classifier was chosen as the top machine learning model after various performance evaluations. The region, the ideal number of children, religion, wealth index, age at first sex, husband education, refusal sex, total births, age at first birth, and mother's educational status are identified as contributing factors in that predict unintended pregnancy. CONCLUSION: The ExtraTrees machine learning model has a better predictive performance for identifying predictors of unintended pregnancies among the chosen algorithms and could improve with better policy decision-making in this area. Using these important features to help direct appropriate policy can significantly increase the chances of mother survival.

Intention to use a mobile phone to receive mental health support and its predicting factors among women attending antenatal care at public health facilities in Ambo town, West Shoa zone, Ethiopia 2022.

BACKGROUND: Mental health problems are the most common morbidities of women during the prenatal period. In LMICs mobile phones have been identified as a good vehicle for monitoring individuals with a high risk of mental health conditions. However, evidence is scarce and the purpose of this study was to assess the intention to use a mobile phone to receive mental health support and its predicting factors among women attending antenatal care at public health facilities in Ambo town, Ethiopia 2022. METHODS AND MATERIALS: An institutional-based cross-sectional study design was conducted from May 20th to June 20th, 2022. A total of 715 prenatal women were included and a systematic random sampling technique was employed. An interviewer-administered structured questionnaire was used. Collected data was exported to SPSS version 25 for the descriptive part, and AMOS 26 structural equation modeling was also used to describe and assess the degree and significance of relationships between variables. RESULTS: A total of 699 (97.8% response rate) responded to complete all the questionnaires. About 530 (77.3%) 95% CI (74%-80.3%) of women intended to use a mobile phone to receive mental health support. The perceived usefulness has a positive effect on attitude (ß = 0.391, p < 0.001) and intention to use (ß = 0.253, p < 0.001). The perceived ease of use influences perceived usefulness (ß = 0.253, p < 0.001) and attitude ß = 0.579, p < 0.001). The intention to use is positively affected by attitude (ß = 0.662, p < 0.001).Trust has a positive effect on perceived usefulness (ß = 0.580, p < 0.001) and intention to use (ß = 0.113, p = 0.005). Subjective norm has a direct positive effect on perceived usefulness (ß = 0.248, p < 0.001). Attitude serves as a partial mediator between perceived usefulness and intention to use and a complete mediating role between perceived ease of use and intention to use. CONCLUSION: The level of intention to use a mobile phone among prenatal women is relatively high and attitude, perceived usefulness, and trust had direct positive effects on intention to use a mobile phone. Therefore, hospitals and healthcare providers should take proactive measures to implement the strategies and policies for providing mobile phone-based mental health support to prenatal women in remote areas.

Healthcare professionals' intention to adopt mobile phone-based SMS and its predictors for adherence support and care of TB patients in a resource-limited setting: a structural equation modelling analysis.

OBJECTIVE: To assess healthcare providers' intentions and the associated factors to use mobile phone-based short message service (SMS) to support adherence and care of tuberculosis (TB) patients in the Oromia region of southwest Ethiopia. STUDY DESIGN: An institutional-based cross-sectional study was conducted from October to November 2022. STUDY SETTING: The study was conducted in public hospitals which include Mettu Karl referral hospital, Dembi Hospital, Bedelle Hospital, Darimu Hospital and Chora Hospital in Ilu Aba Bor and Buno Bedelle zones. PARTICIPANTS: A total of 625 (54.9% male and 45.1% female) health professionals participated in the study. The study participants were selected using a simple random sampling technique. All health professionals permanently working in Ilu Aba Bor and Buno Bedelle zone hospitals were included in this study. However, health professionals with less than 6 months of experience and those who were not present during the data collection period were excluded from this study. OUTCOME MEASURE: The intention to use mobile phone-based SMS to support TB patients. RESULTS: Healthcare professionals' intention to use mobile SMS was 54.4%. Effort expectancy had a significant direct effect on attitude (ß=0.162, p<0.01) and intention towards using mobile phone SMS (ß=0.329, p<0.001). The intention to use mobile phone SMS was directly influenced by facilitating conditions (ß=0.104, p<0.01) and attitude (ß=0.26, p<0.001). The relationship between effort expectancy and intention to use SMS was mediated by attitude (ß=0.043, p<0.01). CONCLUSIONS: Overall, intention to use of mobile-based SMS was high. Effort expectancy, attitude and facilitating conditions were significant factors that determined healthcare professionals' behavioural intention to use mobile phone SMS. Effort expectancy had a more significant prediction power than others. As a result, system forms that are easily interactive and applicable should be implemented to improve capacity building and support the adherence and care of TB patients.

Evidence-Based Practice and Its Associated Factors among Health Professionals Working at Public Hospitals in Southwest Ethiopia.

Introduction: "Evidence-based practice" (EBP) is the process of incorporating clinical expertise and taking patient values and preferences into consideration when making clinical decisions. It is used to describe the provision of high-quality patient care. Objective: This study is aimed at assessing evidence-based practice and associated factors among health professionals working at public hospitals in Illu Aba Bora and Buno Bedele Zones, Oromia Region, Southwest Ethiopia, in 2022. Methods: An institution-based cross-sectional study design was conducted from May 8 to June 20 at public hospitals in Illu Aba Bora and Buno Bedele Zones, Oromia Region, Southwest Ethiopia. A total of 423 health professionals were included, using proportional allocation and simple random sampling. The data were collected using a self-administered questionnaire. Data was entered using EpiData version 4.6, and the collected data was cleared, arranged, coded, and then analyzed using Statistical Package for the Social Sciences version 26. Descriptive statistics and bivariable and multivariable analyses of logistic regression with AOR (95% CI) were performed at p < 0.05. Result: The study revealed that 36.2% of health professionals had good evidence-based practice. The factors found to be significantly associated with good EBP include having training in EBP (AOR = 5.43; 95% CI: 4.323, 9.532), good knowledge (AOR = 1.91; 95% CI: 1.065, 3.541), a favorable attitude (AOR = 1.91; 95% CI: 1.884, 2.342), and work experience greater than 5 years (AOR = 1.58; 95% CI: 1.482, 2.437). Conclusion: The evidence-based practice of health professionals was poor. Evidence-based practice should included in the curriculum, and also planned trainings need to be delivered to all health professionals, inorder to enhancing their knowledge as well as their attitudes by motivating them to increase evidence-based practice.

Post-natal care: a vital chance to save mothers and infants! Exploring barriers and factors associated with it: a mixed study.

Introduction: The most effective maternal health intervention for enhancing mother and baby survival is postnatal care, yet it is also the most neglected service in Ethiopia. Less is known about postnatal care despite earlier studies concentrating on pregnancy and delivery service utilization. Postnatal care is the subject of few national and local area studies. Therefore this research aims to evaluate postnatal care utilization and barriers and associated characteristics among women in Ilubabor Zone and Buno Bedele Zone. Methods: A mixed-methods study involving women who visited immunization clinics was conducted in Southwest Ethiopia. For the quantitative part, a cross-sectional survey was conducted between June 12 and July 12, 2022. The data collected through interviews was analyzed using SPSS version 26. An adjusted odds ratio (AOR) with a 95% confidence interval (CI) and p-value was constructed to evaluate the associations between postnatal care service utilization and explanatory variables. The usage of postnatal care services was determined to be significantly correlated with explanatory variables in multivariable logistic regression with a p-value less than 0.05. This qualitative study used two focused group discussions and two in-depth interviews to gather data from purposely selected mothers, and thematic analysis was used to analyze the data. Results and Discussion: A total of 422 participants with a 100% response rate were included in the analysis. 234 (55.5%) of these underwent postnatal checks. In the quantitative section, postnatal care counseling and appointment setting, counseling on danger signs, and prior postnatal care utilization all demonstrated a statistically significant association with the use of postnatal care services (AOR = 3.6, 95% CI (1.47-7.23)), [AOR = 2, 95% CI (1.05-3.64)], and [AOR = 3, 95% CI (1.36-58), respectively). At the qualitative level, it was determined that the themes of knowledge and access were obstacles to the use of postpartum care services. Generally this study revealed that the Ilubabor Zone and Buno Bedele Zone have a poor total PNC service utilization rate. Furthermore, ignorance, conventional wisdom, religious activity, distance from facilities, environmental exposure, and waiting time were identified as barriers to postnatal care service utilization. To optimize this service, all parties involved should address these factors.

Machine learning algorithms' application to predict childhood vaccination among children aged 12-23 months in Ethiopia: Evidence 2016 Ethiopian Demographic and Health Survey dataset.

INTRODUCTION: Childhood vaccination is a cost-effective public health intervention to reduce child mortality and morbidity. But, vaccination coverage remains low, and previous similar studies have not focused on machine learning algorithms to predict childhood vaccination. Therefore, knowledge extraction, association rule formulation, and discovering insights from hidden patterns in vaccination data are limited. Therefore, this study aimed to predict childhood vaccination among children aged 12-23 months using the best machine learning algorithm. METHODS: A cross-sectional study design with a two-stage sampling technique was used. A total of 1617 samples of living children aged 12-23 months were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 70% and 30% of the observations were used for training, and evaluating the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. All the included algorithms were evaluated using confusion matrix elements. The synthetic minority oversampling technique was used for imbalanced data management. Informational gain value was used to select important attributes to predict childhood vaccination. The If/ then logical association was used to generate rules based on relationships among attributes, and Weka version 3.8.6 software was used to perform all the prediction analyses. RESULTS: PART was the first best machine learning algorithm to predict childhood vaccination with 95.53% accuracy. J48, multilayer perceptron, and random forest models were the consecutively best machine learning algorithms to predict childhood vaccination with 89.24%, 87.20%, and 82.37% accuracy, respectively. ANC visits, institutional delivery, health facility visits, higher education, and being rich were the top five attributes to predict childhood vaccination. A total of seven rules were generated that could jointly determine the magnitude of childhood vaccination. Of these, if wealth status = 3 (Rich), adequate ANC visits = 1 (yes), and residency = 2 (Urban), then the probability of childhood vaccination would be 86.73%. CONCLUSIONS: The PART, J48, multilayer perceptron, and random forest algorithms were important algorithms for predicting childhood vaccination. The findings would provide insight into childhood vaccination and serve as a framework for further studies. Strengthening mothers' ANC visits, institutional delivery, improving maternal education, and creating income opportunities for mothers could be important interventions to enhance childhood vaccination.

Machine learning modeling for identifying predictors of unmet need for family planning among married/in-union women in Ethiopia: Evidence from performance monitoring and accountability (PMA) survey 2019 dataset.

Unmet need for contraceptives is a public health issue globally that affects maternal and child health. Reducing unmet need reduces the risk of abortion or childbearing by preventing unintended pregnancy. The unmet need for family planning is a frequently used indicator for monitoring family planning programs. This study aimed to identify predictors of unmet need for family planning using advanced machine learning modeling on recent PMA 2019 survey data. The study was conducted using secondary data from PMA Ethiopia 2019 cross-sectional household and female survey which was carried out from September 2019 to December 2019. Eight machine learning classifiers were employed on a total weighted sample of 5819 women and evaluated using performance metrics to predict and identify important predictors of unmet need of family planning with Python 3.10 version software. Data preparation techniques such as removing outliers, handling missing values, handling unbalanced categories, feature engineering, and data splitting were applied to smooth the data for further analysis. Finally, Shapley Additive exPlanations (SHAP) analysis was used to identify the top predictors of unmet need and explain the contribution of the predictors on the model's output. Random Forest was the best predictive model with a performance of 85% accuracy and 0.93 area under the curve on balanced training data through tenfold cross-validation. The SHAP analysis based on random forest model revealed that husband/partner disapproval to use family planning, number of household members, women education being primary, being from Amhara region, and previously delivered in health facility were the top important predictors of unmet need for family planning in Ethiopia. Findings from this study suggest various sociocultural and economic factors might be considered while implementing health policies intended to decrease unmet needs for family planning in Ethiopia. In particular, the husband's/partner's involvement in family planning sessions should be emphasized as it has a significant impact on women's demand for contraceptives.

Attitude of mental healthcare providers toward tele-psychiatry services and associated factors at public referral hospitals in Addis Ababa city, Ethiopia.

INTRODUCTION: Health systems around the world are struggling with the massive numbers of people with mental disorders who require professional care. The treatment gap for mental disorders is high all over the world, with between 76 and 85% of people in low- and middle-income countries with severe mental disorders receiving no treatment for their mental health conditions. Tele-psychiatry is used as an alternative solution to the problem of limited mental health services and effective Tele-psychiatry service use may be achievable if mental health providers have a good attitude towards it. OBJECTIVE: To assess the attitude of mental healthcare providers toward Tele-psychiatry services and associated factors at public referral hospitals in Addis Ababa city, Ethiopia, 2022. METHOD: A Multicenter institution-based cross-sectional study was conducted among 413 mental health professionals working in public referral hospitals in Addis Ababa city, from May 04 to June 10, 2022. Data were collected by using a structured and self-administered questionnaire prepared by reviewing previous related studies. Epi Data version 3.1 and Stata version 14 were used for data entry and analysis respectively. Bivariate and multivariable logistic regression analyses were used to identify factors associated with attitudes toward Tele-psychiatry services. A statistical significance was declared at p-value < 0.05. RESULT: A total of 413 Participants were enrolled with a response rate of 91.8%. The majority of respondents 230 (55.69%) were male and the mean age of participants was 29 years (SD + 5.02). In this study the majority (49%) of mental health care professionals had a poor attitude toward Tele-psychiatry. Having electronic health technology experience [AOR 16.79; 95% CI (4.26, 29.3)], lack of training in telemedicine applications [(AOR 0.1; 95% CI (0.01, 0.41)], a good computer uses for daily work activities [AOR 3.65; 95% CI (1.14, 11.60)], availability of e-Health technology awareness program [AOR 0.16; 95% CI (0.03, 0.90)], having a positive perception about the importance of e-Health technologies[AOR 0.041; 95% CI (0.01, 0.29)] and having good knowledge of Tele-psychiatry services [AOR 6.89; 95% CI (1.8, 12.0)] were significantly associated with attitude towards Tele-psychiatry services. CONCLUSION: This study found that mental healthcare providers at a public referral hospital in Addis Ababa city generally had poor attitudes regarding Tele-psychiatry services. Considering the significant factors will improve the attitude to use tele-psychiatry services in Ethiopia.

Exploring facilitators and barriers of the sustainable acceptance of e-health system solutions in Ethiopia: A systematic review.

BACKGROUND: eHealth is the use of information and communications technologies in support of health and health-related fields, including healthcare services, health surveillance, health literature, and health education knowledge and research, has the potential to improve the delivery and support of healthcare services by promoting information sharing and evidence-based health practice. Acceptance of e-health in Ethiopia using systematic review is uncertain. As a result, this study aimed to assess barriers and facilitators of the sustainable acceptance of e-health system adoption in Ethiopia through a systematic review of the literature. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist was used to conduct this study. Relevant articles have been searched in Google Scholar, Medline, PubMed, Embrace, Web of Science, Scopus, Cochrane Library, and empirical research done in Ethiopia is the main emphasis of the search strategy. The total number of studies that satisfied the criteria for inclusion was ten. In this research, empirical data related to e-health acceptance factors were retrieved, examined, and summarized by the authors. RESULTS: This systematic review identified a total of 25 predictors that have been found in the ten studies. The identified facilitators were effort expectancy, performance expectancy, facilitating conditions, social influences, attitude, computer literacy, participant age, perceived enjoyment, and educational status, duration of mobile device use, organizational culture, and habit. Moreover, technology anxiety was the most barrier to sustainable acceptance of e-health systems in Ethiopia. CONCLUSIONS: The most common facilitator identified from the predictors was effort expectancy, which played a major role in the adoption of the e-health system in Ethiopia. Therefore, eHealth implementers and managers in those settings should give users of the system priority in improving the technical infrastructure by regularly providing them with basic facilitating conditions. They should also pay attention to the system they want to implement because doing so will improve the users' perception of the system's value and attitude.

Healthcare professionals' satisfaction toward the use of district health information system and its associated factors in southwest Ethiopia: using the information system success model.

Background: Ethiopia has the potential to use the district health information system, which is a building block of the health system. Thus, it needs to assess the performance level of the system by identifying the satisfaction of end users. There is little evidence about users' satisfaction with using this system. As a result, this study was conducted to fill this gap by evaluating user satisfaction and associated factors of district health information system among healthcare providers in Ethiopia, using the information system success model. Methods: An institutional-based cross-sectional study was conducted from November to December 2022 in the Oromia region of southwest Ethiopia. A total of 391 health professionals participated in the study. The study participants were selected using a census. Using a self-administered questionnaire, data were collected. Measurement and structural equation modeling analyses were used to evaluate reliability, the validity of model fit, and to test the relationship between the constructs, respectively, using analysis of moment structure (AMOS) V 26. Results: System quality had a positive direct effect on the respondent's system use (ß = 0.18, P-value < 0.001), and satisfaction (ß = 0.44, P-value < 0.001). Service quality had also a direct effect on the respondent's system use (ß = 0.37, P-value < 0.01), and satisfaction with using the district health information system (ß = 0.36, P-value < 0.01). Similarly, system use had also a direct effect on the respondent's satisfaction (ß = 0.53, P-value < 0.05). Moreover, computer literacy had a direct effect on the respondent's system use (ß = 0.63, P-value < 0.05), and satisfaction (ß = 0.51, P-value < 0.01). Concussions: The overall user satisfaction with using the district health information system in Ethiopia was low. System quality, service quality, and computer literacy had a direct positive effect on system use and user satisfaction. In addition, system use and information quality had a direct positive effect on healthcare professionals' satisfaction with using the district health information system. The most important factor for enhancing system use and user satisfaction was computer literacy. Accordingly, for the specific user training required for the success of the district health information system in Ethiopia, the manager should offer additional basic computer courses for better use of the system.

Awareness and readiness of mental healthcare providers to implement telemental health services and associated factors at public referral hospitals in Addis Ababa City, Ethiopia.

OBJECTIVE: To assess the awareness and readiness of mental healthcare providers to implement telemental health services and the associated factors at public referral hospitals in Addis Ababa City, Ethiopia. STUDY DESIGN: A cross-sectional study was conducted among mental health professionals working at public referral hospitals in Addis Ababa City from 4 May to 10 June 2022. PARTICIPANTS: A total of 413 (55.7% male and 44.3% female) health professionals participated in the study. The study participants were selected using a simple random sampling technique. All mental healthcare providers working at public referral hospitals in Addis Ababa City were considered as the source population. Mental healthcare providers who meet the inclusion criteria were considered the study population. Having a minimum diploma qualification and 6 months or more of work experience and giving written consent were considered the inclusion criteria. OUTCOME MEASURE: The main outcome measure was awareness and readiness to implement telemental health services. RESULT: A total of 413 participants were enrolled, with a response rate of 90.6%. The prevalence of good awareness towards telemental health services was 41% (95% CI 37%, 47%). Being female (adjusted OR (AOR)=0.34; 95% CI 0.15, 0.76), having poor information technology support (AOR=0.32; 95% CI 0.11, 0.99) and having electronic health technology experience (AOR=1.21; 95% CI 1.1, 2.44) were significantly associated with awareness of telemental health services, whereas computer access at the workplace (AOR=0.04; 95% CI 0.003, 0.55) and awareness (AOR=4.34; 95% CI 1.02, 18.48) and attitude (AOR=1.01; 95% CI 1.03, 2.19) towards telemental health services were the variables that showed statistically significant association with readiness of mental healthcare providers for telemental health services. CONCLUSION: Although majority of healthcare providers in this survey were prepared to implement telemental health services, there was typically little awareness among mental healthcare providers.

Exploring spatial patterns, and identifying factors associated with insufficient cash or food received from a productive safety net program among eligible households in Ethiopia: a spatial and multilevel analysis as an input for international food aid programmers.

BACKGROUND: In low-income countries, households' food insecurity and the undernutrition of children are the main health problems. Ethiopia is vulnerable to food insecurity and undernutrition among children because its agricultural production system is traditional. Hence, the productive safety net program (PSNP) is implemented as a social protection system to combat food insecurity and enhance agricultural productivity by providing cash or food assistance to eligible households. So, this study aimed to explore spatial patterns of households' insufficient cash or food receiving from PSNP, and identify its associated factors in Ethiopia. METHODS: The 2019 Ethiopian Mini Demographic and Health Survey dataset was used. A total of 8595 households were included in this study. Data management and descriptive analysis were done using STATA version 15 software and Microsoft Office Excel. ArcMap version 10.7 software was used for spatial exploration and visualization. SaTScan version 9.5 software was used for spatial scan statistics reports. In the multilevel mixed effect logistic regression analysis, explanatory variables with a p-value of less than 0.05 were considered significant factors. RESULTS: Overall, 13.5% (95% CI: 12.81-14.27%) of the households' level beneficiaries received cash or food from PSNP. The spatial distribution of households' benficiaries received cash or food from PSNP was not random, and good access to cash or food from PSNP was detected in Addis Ababa, SNNPR, Amhara, and Oromia regions. Households' heads aged 25-34 (AOR:1.43, 95% CI: 1.02, 2.00), 35-44 (AOR: 2.41, 95% CI: 1.72, 3.37), and > 34 (AOR: 2.54, 95% CI: 1.83, 3.51) years, being female (AOR: 1.51, 95% CI: 1.27,1.79), poor households (AOR: 1.91, 95% CI:1.52, 2.39), Amhara (AOR:.14, 95% CI: .06, .39) and Oromia (AOR:.36, 95% CI:.12, 0.91) regions, being rural residents (AOR:2.18, 95% CI: 1.21,3.94), and enrollment in CBHS (AOR: 3.34, 95% CI:2.69,4.16) are statistically significant factors. CONCLUSIONS: Households have limited access to cash or food from the PSNP. Households in Addis Ababa, SNNPR, Amhara, and Oromia regions are more likely to receive benefits from PSNP. Encouraging poor and rural households to receive benefits from the PSNP and raise awareness among beneficiaries to use the benefits they received for productivity purposes. Stakeholders would ensure the eligibility criteria and pay close attention to the hotspot areas.

Willingness of diabetes mellitus patients to use mHealth applications and its associated factors for self-care management in a low-income country: an input for digital health implementation.

BACKGROUND: Although mHealth applications are becoming more widely available and used, there is no evidence about why people are willing to use them. Therefore, this study aimed to assess the willingness of patients with diabetes to use mHealth applications and associated factors for self-care management in Ethiopia. METHODS: An institutional cross-sectional study was conducted among 422 patients with diabetes. Data were collected using pretested interviewer-administered questionnaire. Epi Data V.4.6 for entering the data and STATA V.14 for analysing the data were used. A multivariable logistic regression analysis was carried out to identify factors associated with patient's willingness to use mobile health applications. RESULTS: A total of 398 study participants were included in the study. About 284 (71.4%) 95% CI (66.8% to 75.9%)). Of participants were willing to use mobile health applications. Patients below 30 years of age (adjusted OR, AOR 2.21; 95% CI (1.22 to 4.10)), urban residents (AOR 2.12; 95% CI (1.12 to 3.98)), internet access (AOR 3.91; 95% CI (1.31 to 11.5)), favourable attitude (AOR 5.20; 95% CI (2.60 to 10.40)), perceived ease of use (AOR 2.57; 95% CI (1.34 to 4.85)) and perceived usefulness (AOR 4.67; 95% CI (1.95 to 5.77)) were significantly associated with patients' willingness to use mobile health applications. CONCLUSIONS: Overall, diabetes patients' willingness to use mobile health applications was high. Patients' age, place of residence, internet access, attitude, perceived ease of use and perceived usefulness were significant factors concerning their willingness to use mobile health applications. Considering these factors could provide insight for developing and adopting diabetes management applications on mobile devices in Ethiopia.

User Satisfaction of Using Electronic Medical Record System and Its Associated Factors among Healthcare Professionals in Ethiopia: A Cross-Sectional Study.

Background: Electronic medical record (EMR) systems have become essential for the proper management of patients' information. Electronic medical record systems are on the rise in developing countries due to the need to ensure improved quality of healthcare. However, EMR systems can be ignored, if users are not satisfied with the implemented system. User dissatisfaction has been associated with the failure of EMR systems as a primary factor. There is also limited research done in the Ethiopian context on EMR user satisfaction at private hospitals. This study is aimed at assessing user satisfaction with electronic medical records and associated factors among health professionals working at private hospitals in Addis Ababa. Methods: Institution-based cross-sectional quantitative study was conducted among health professionals working at private hospitals in Addis Ababa, from March to April 2021. A self-administered questionnaire was used to collect the data. EpiData version 4.6 and Stata version 25 were used for data entry and analysis, respectively. Descriptive analyses were computed for the study variables. Bivariable and multivariable logistic regression analyses were carried out to assess the significance of independent variables on dependent variables. Results: A total of 403 (95.33% response rate) participants completed all the questionnaires. More than half of 214 (53.10%) of the participants were satisfied with the EMR system. Factors associated with user satisfaction with electronic medical records were good computer literacy (AOR = 2.92, 95% CI: [1.16-7.37]), perceived information quality (AOR = 3.54, 95% CI: [1.55-8.11]), perceived service quality (AOR = 3.15, 95% CI: [1.58-6.28]), perceived system quality (AOR = 3.05, 95% CI: [1.32-7.05]), EMR training (AOR = 4.00, 95% CI: [1.76-9.03]), computer access (AOR = 3.17, 95% CI: [1.19-8.46]), and HMIS training (AOR = 2.05, 95% CI: [1.22-6.71]). Conclusions: In this study, health professionals' EMR satisfaction was moderate. The result showed that EMR training, computer literacy, computer access, perceived system quality, information quality, service quality, and HMIS training were associated with user satisfaction. Improving computer-related training, system quality, information quality, and service quality is an important intervention to improve the healthcare professional's satisfaction towards using electronic health record systems in Ethiopia.

Women's health service access and associated factors in Ethiopia: application of geographical information system and multilevel analysis.

OBJECTIVES: Women's access to healthcare services is challenged by various factors. This study aimed to assess women's health service access and identify associated factors. METHODS: A cross-sectional study design with a two-stage stratified sampling technique, and 12 945 women from the 2016 Ethiopia Demographic and Health Survey dataset were used. The spatial hotspot analysis and purely Bernoulli-based model scan statistics were used to highlight hot and cold spot areas, and to detect significant local clusters of women's health service access. A multilevel logistic regression analysis was used to assess factors that affect women's access to health services. A variable with a p

Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, Ethiopia, 2022.

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.