Pharmacist’s perception of the impact of electronic prescribing on medication errors and productivity in community pharmacies
Bibliographic record
Abstract
Paper-based prescriptions have been used for several decades by many healthcare practitioners. The literature suggests that several challenges are associated with handwritten prescriptions that might impact patients’ safety and medication errors. Electronic prescribing (e-prescribing) has been developed to phase out handwritten and computer-generated prescriptions that are printed on paper or faxed directly to a dispensing pharmacy. This research aimed to examine pharmacists’ thoughts about the e-prescribing impact on their practice. We also evaluated the adoption rate of e-prescribing by assessing the proportion of electronic prescriptions (e-Rx) received in community pharmacies across the Canadian provinces. This research was conducted as a secondary analysis of the 2016 National Survey of Community-Based Pharmacists: Use of Digital Health Technology in Practice by Nielson. The survey was conducted in collaboration between Canada Health Infoway and the Canadian Pharmacy Association. The target population of the survey was Canadian pharmacists who were in community practice. The provinces included in this research were Ontario, Quebec, Saskatchewan, Alberta, and British Columbia (n = 450). The findings of this study suggest that community pharmacists in Canada were willing to embrace e-prescribing to support their practice. Most of pharmacists thought that e-prescribing was a useful tool to reduce medication errors and improve efficiency in pharmacies. However, the largest proportion of prescriptions issued by prescribers continue to be in paper form, whether handwritten or computer-generated. Further research is needed to investigate the barriers to the adoption of e-prescribing systems among primary care practitioners in Canada.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".