Electronic Medical Record Use and Associated Factors in Diredawa, Eastern Ethiopia. A mixed Method Study
Bibliographic record
Abstract
Abstract Background: Despite rapid growth in the information technology (IT), adoption rate of electronic medical records (EMR) in health care setting is lagging behind in Ethiopia. EMRs have long been considered as essential elements in improving healthcare quality and safety, enhance service performance, reduce adverse events for patients, and support clinical decision support system. However, utilization of EMR among healthcare providers still remains low and there has been little progress toward harnessing the benefits of EMR particularly in developing countries. Objective: This study, therefore, is aimed at exploring EMR use and its determinants among health care providers. Methods: Quantitative cross-sectional study was conducted on 402 health professionals working at public health facilities supplemented with an exploratory qualitative study in Dire Dawa, Ethiopia. Descriptive statistics and logistic regression analysis were used to explore determinant factors of EMR use while qualitative data were thematically analyzed. Results: Overall, about a quarter (26.6%) of health professionals were using electronic medical record. Work experience of 6 years or less (AOR=2.23; 95% CI: [1.15-4.31]), discussion on EMR (AOR=14.47; 95% CI: [5.58-37.57]), presence of EMR manual (AOR=3.10 95%CI: [1.28-7.38]), and positive attitude towards EMR system (AOR=11.15; 95%CI: [4.90-25.36]) and service quality (AOR=8.02; 95% CI: [4.09-15.72]) were independent determinants of EMR use.Conclusion: The analysis carried out indicates that EMR use by health professionals in the study area is very low. Therefore, there is a need to leverage through continuous technical support and commitment to enhance EMR use which has the potential to improve health service performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".