Acceptance of Electronic Medical Records and Associated Factors Among Health Care Workers in Northwest Ethiopia: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Although electronic medical records (EMRs) play a vital role in strengthening the health care system by improving efficiency, data management, and patient care, their development in Ethiopia is still in its early stages. Hence, most public health care facilities manage their patient information using paper-based recording, which results in errors, delays, and reduced service quality. OBJECTIVE: This study aims to determine the level of acceptance of the EMR system and describe contributing factors. METHODS: A cross-sectional study was conducted at health care facilities in Bahir City, Northwestern Ethiopia. A total of 322 health workers participated in the study, drawn from 5 health facilities that have implemented the EMR system. Descriptive statistics and bivariate and multivariate binary logistic regression were done to determine factors associated with EMR acceptance computed from mediating factors (perceived ease of use and perceived usefulness), and which is more appropriate in early-stage implementation. RESULTS: Out of the total 322 respondents, 256 (73%) respondents with 95% CI 67.4-78.2 had a good acceptance of using EMRs. In regression analysis, significant predictors including work experience over 10 years (odds ratio [OR] 14.32, 95% CI 4.60-44.58), income dissatisfaction (OR 0.28, 95% CI 0.10-0.82), owning a personal computer (OR 11.08, 95% CI 4.03-30.24), EMR-specific training (OR 4.71, 95% CI 1.52-14.54), basic electronic health management information system/district health information system 2 training (OR 3.06, 95% CI 1.02-9.17), and system usability (OR 38.24, 95% CI 12.26-119.27) were identified. CONCLUSIONS: The study demonstrated a moderate level of EMR acceptance among health care workers, with system usability identified as the strongest predictor. Significant factors influencing EMR acceptance included longer work experience, ownership of a personal computer, and prior EMR or electronic health management information system/district health information system 2 training. Context-specific strategies are needed to enhance system usability, provide targeted digital health training, and improve access to technological resources in order to support broader EMR adoption in health care settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".