Background. A lot of effort is being done in the electronic medical record (EMR) system. However, it has not been implemented and used at the expected scale for maximal effectiveness. There is limited evidence on the factors affecting the utilization of EMR in this particular context, which are critical for targeted strategies. Objective. To assess the magnitude and factors affecting the utilization of EMR among health professionals in eastern Ethiopia. Methods. An institutional-based cross-sectional study was conducted among randomly selected 412 health professionals from Harari and Dire Dawa, eastern Ethiopia, using a pretested self-administered questionnaire. The tool was developed from previous literature, and a pilot survey was done before the actual study. Bivariable and multivariable binary logistic regression were done to assess the relationship between an independent variable with EMR use. Crude and an adjusted odds ratio with a 95% confidence interval were reported. A P value of less than 0.05 was used to declare a statistically significant association. Results. A total of 412 health professionals with a mean age of 29 years (±6.4 years) were included. A total of 229 (55.6%) and 300 (72.8%) of them had good knowledge and attitude towards the EMR, while 279 (67.7%) used the service (54% used it on a daily basis). About 272 (66%) of the respondents reported that they prefer EMRs to paper-based systems. Health professionals with more than five years of experience had two times higher odds of using the service (AOR=2.22; 95% CI; 1.12-4.42) than early-career workers. Health professionals trained in EMR would use the service more (AOR=5.88; 95% CI; 2.93-11.88) compared to those who did not take the training. In addition, having good knowledge (AOR=1.52; 95% CI; 0.92-1.5) and a good attitude towards the EMR system (AOR=2.4; 95% CI; 1.35-4.31) showed to use EMR as compared to counterparts. Conclusions. The utilization of EMR was found to be optimal. Age, work experience, knowledge, attitude, and training of professionals were positively associated with the use of the service in their facility.
This study was conducted in Eastern Ethiopia (Dire Dawa, Eastern Harerghe, Harar, and Ethiopian Somali). Thus, out of these study sites, three areas reported to have an established EMR system in their health care system, namely, the Dire Dawa Administration, the Harari Regional State, and Ethiopian Somali. Harar is located 526 km east of Addis Ababa, the capital city of Ethiopia. The two regions and the administrative town together comprise more than 4.5 million population. All are located in the eastern part of the country. There are about 6755 health care workers working in these regions, including the Harari region [38]. This is an institutional-based quantitative cross-sectional study that was conducted to assess EMR utilization and its associated factors among health professionals in eastern Ethiopia. Voluntary health professionals who are working in health facilities in Eastern Ethiopia where there is a functional EMR system within the facility. All randomly selected health professionals from all categories working in the selected health facilities where there is a functional EMR system within the facilities were included in the study. While those who were on annual or maternal leave were not included in this study. The sample size was determined using a single population proportion formula at 95% confidence level, 5% significance level, EMR utilization among health care workers (p) of 70.8% [30], a desired degree of precision (d) of 5%, and a design effect of 1.5. The sample size for factors associated with EMR utilization was calculated using sample size calculation for double proportion (under Epi info version 7.0 software for sample size and power calculation) by taking power (80%), 95% confidence level, and utilization estimates from previous studies. The final sample size became 525 with the inclusion of a 5% nonresponse rate. However, because the total number of eligible health professionals working in a facility where EMR service was not available was greater than the number of eligible study participants, all eligible study participants were included. A stratified sampling technique with proportionate allocation to each region and health facility (sample size proportional to size) was employed. First, the total sample size was stratified into two regions where the service is functional. Then, further stratification by type of health facility was done to hospitals and health centers where the service is functional. Thus, health professionals working in facilities without functional EMR were not considered. The study samples were proportionally allocated to each health facility depending on the number of health professionals within that facility. To develop a sampling frame, the list of health facilities and health care workers was obtained from the health bureau of the respective regions and city administration. However, as the EMR system is available in two sites (namely, Harar and Dire Dawa one health facility only), the sampling population became smaller and all available health professionals working on all facilities with established EMR systems were included (Figure 1). Diagrammatic summary of the sample size (sampling procedure) for each region and city administration based on the stratification and proportion of their health care work force. A self-administered structured questionnaire was used to collect data on sociodemographic, organizational, and technology-related factors, as well as knowledge, attitude, and use of electronic medical records. The questionnaire was adopted from previous studies [17, 37, 39–41]. The questionnaire was prepared in the English language. Regarding data collection, diploma health informatics students and technicians were involved in administering the questionnaire after they took two days of training. Bachelor degree (BSC) holders from any health science field worked closely with investigators to oversee the data collection process. Structured self-administered questionnaires were adopted from previous studies and checked for consistency. The data collection information sheet was developed by the investigator on the objective of the study, how to collect data (technique of data collection), ethical issues, and a description of inclusion and exclusion criteria, and training was provided for the data collectors. All filled-out questionnaires were reviewed by the data collectors for clarity, completeness, and relevance. Close supervision was done accordingly. The collected data was entered in a prespecified format into Epi Data version 3.01, for consistency, double data entry, restricting entry through legal values, and skipping patterns. The dependent variable of this study was the utilization of EMR (utilized or not utilized), while sociodemographic variables (age, sex, income, educational level, and professional category), years of service, technology-related variables, access to computer, knowledge, attitudes, and training on EMR were independent variables considered. Data were entered into Epi Data version 3.01 and cleaned and analyzed using SPSS version 20 statistical software. Descriptive analyses such as frequency, percentages, graphic presentations, and summary tables were conducted for categorical variables. Bivariate logistic regression was performed for each independent variable against the outcome variable (EMR utilization) to estimate the crude odds ratio. The main purposes of EMR utilization for data recording, storing, retrieving, reporting, and other eight core functions of EMR in a daily task were considered in assessing the EMR’s utilization by health professionals. Thus, those with reported use of EMR for the stated purposes were categorized as EMR system users, whereas those who did not use the EMR for the abovementioned (twelve core functions) tasks were considered as nonusers of the EMR system. Health professionals’ knowledge of the EMR system was assessed using a set of questions adapted from previous literature, and the sum score was calculated. Based on the median of the sum knowledge score (skewed distribution), those who scored greater than or equal to the median score were categorized as having good knowledge of EMR. Similarly, an attitude score was generated, and the median attitude sum score was used to classify individuals as having a good or poor attitude towards the EMR system, respectively. A stepwise backward binary logistic regression was used to identify factors associated with the utilization of EMR. Both bivariate and multivariate binary logistic regressions were used. Predictor variables associated with outcome at a P value below 0.2 and important predictor variables identified in previous literature were considered for the multivariable analysis. The multivariable binary logistic regression method was used to assess the factors associated with the utilization of EMR with each identified predictor variable. An adjusted odds ratio (AOR) with a P value and a 95% confidence interval was reported. Associations with a P value below 0.05% in multivariate analysis were declared as statistically significant predictors of EMR utilization among health professionals. The goodness of fit of the model was assessed using Hosmer-Lemeshow’s statistical test with a P value above 0.5 as a fitted logistic regression model. In addition, a significant omnibus test and improved classification precision were also assessed for model specification. Ethical approval was obtained from the research and technology interchange (RTI) of Dire Dawa University (DDU), and a support letter was taken to each region and facility for official communications. Verbal informed consent was obtained from each health professional after a detailed explanation of the purpose, confidentiality, benefits, risks, and procedures during data collection. Privacy and confidentiality were maintained by not asking for personal identifiers like names and addresses. The respondent’s anonymity to withdraw from the study during the course of data collection was maintained. Personal identity identifiers were not collected.
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