Effect of EMR Implementation on Clinic Time, Patient and Staff Satisfaction, and Chart Completeness in a Resource-Limited Antenatal Clinic in Kenya
Bibliographic record
Abstract
Electronic Medical Records (EMR) are thought to improve healthcare through a variety of means. However, the study of EMR implementation in resource-poor settings has been minimal. Moi Teaching and Referral Hospital (MTRH) is the second largest tertiary care centre in Kenya, hosting a busy antenatal clinic serving Eldoret and surrounding regions. The recent transition from written to electronic antenatal records at MTRH permits the opportunity to study whether this change improves quality of care, in terms of: TIME: Does the patient or healthcare worker spend the same amount of time at the encounter? SATISFACTION: Is the patient or healthcare worker more or less satisfied with the encounter? COMPLETENESS: Does the antenatal record do a better job of recording key information in the antenatal history? Our Objective wasto determine the effects of EMR implementation on an antenatal clinic in a resource-limited setting.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".