Implementation of an E-Prescribing Service: Users' Satisfaction and Recommendations
Bibliographic record
Abstract
The aim of the present study was to measure the attitudes and satisfaction of various stakeholders about an electronic prescribing service (EPS), in order to generate best practice recommendations for further improvement. Relevant stakeholders included physicians from different specialties, pharmacy staff (pharmacists and assistant pharmacists), nurses and outpatients. Participants ( n = 283) were randomly selected from several clinical settings in Muscat, Oman. They were asked to fill out a questionnaire to measure their satisfaction with the EPS, as well as their attitude toward it, both before and after its integration with a computerized hospital information and management system (Al-Shifa). The overall level of satisfaction with the integrated EPS was high. Physicians, pharmacy staff and nurses highly agreed that the EPS reduced prescribing errors and they did not want to go back to the paper-based prescription system. Pharmacy staff and nurses viewed the EPS more positively and were more satisfied with it than were physicians (p < 0.05). It was also found that 74% of patients who responded to the survey were either satisfied or very satisfied with the EPS and preferred it over paper-based prescriptions. In conclusion, the majority of stakeholders were generally satisfied with the current status of the EPS, but they also perceived a few key weaknesses. A total of 12 recommendations were offered to improve the EPS in clinical settings in the Sultanate of Oman.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".