User Perceptions and Use of an Enhanced Electronic Health Record in Rwanda With and Without Clinical Alerts: Cross-sectional Survey
Bibliographic record
Abstract
BACKGROUND: Electronic health records (EHRs) have been implemented in many low-resource settings but lack strong evidence for usability, use, user confidence, scalability, and sustainability. OBJECTIVE: This study aimed to evaluate staff use and perceptions of an EHR widely used for HIV care in >300 health facilities in Rwanda, providing evidence on factors influencing current performance, scalability, and sustainability. METHODS: A randomized, cross-sectional, structured interview survey of health center staff was designed to assess functionality, use, and attitudes toward the EHR and clinical alerts. This study used the associated randomized clinical trial study sample (56/112, 50% sites received an enhanced EHR), pulling 27 (50%) sites from each group. Free-text comments were analyzed thematically using inductive coding. RESULTS: Of the 100 participants, 90 (90% response rate) were interviewed at 54 health centers: 44 (49%) participants were clinical and 46 (51%) were technical. The EHR top uses were to access client data easily or quickly (62/90, 69%), update patient records (56/89, 63%), create new patient records (49/88, 56%), generate various reports (38/85, 45%), and review previous records (43/89, 48%). In addition, >90% (81/90) of respondents agreed that the EHR made it easier to make informed decisions, was worth using, and has improved patient information quality. Regarding availability, (66/88) 75% said they could always or almost always count on the EHR being available, whereas (6/88) 7% said never/almost never. In intervention sites, staff were significantly more likely to update existing records (P=.04), generate summaries before (P<.001) or during visits (P=.01), and agree that "the EHR provides useful alerts, and reminders" (P<.01). CONCLUSIONS: Most users perceived the EHR as well accepted, appropriate, and effective for use in low-resource settings despite infrastructure limitation in 25% (22/88) of the sites. The implementation of EHR enhancements can improve the perceived usefulness and use of key functions. Successful scale-up and use of EHRs in small health facilities could improve clinical documentation, care, reporting, and disease surveillance in low- and middle-income countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".