A Preliminary Scoping Review of the Impact of e-Prescribing on Pharmacists in Community Pharmacies
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to map the available literature and provide an overview of the published articles discussing the impact of electronic prescribing on medication errors and pharmacy workflow. METHODS: , as well as grey literature reports, using the search terms and related components of "pharmacists", "electronic prescribing", "medication errors", and "efficiency". The search included all articles that were published from January 2011 to September 2023. Twenty-two relevant articles were identified and fully reviewed, ten of which were included in this review. RESULTS: Electronic prescribing (e-prescribing) provides a solution for some of the challenges that are associated with handwritten and paper prescriptions. However, the implementation of e-prescribing systems has been recognized as a source of new unforeseen medication errors in all the reviewed articles. Productivity in community pharmacies has been affected with receiving electronic prescriptions (e-prescriptions) and having to deal with the issues that arise from them. The pharmacists' interventions were not eliminated with e-prescriptions compared to other prescription formats. The most frequently reported reason for intervention was related to incomplete instructions in the field of directions of use. Other common challenges with e-prescriptions were related to missing information, quantity, inappropriate dose, dosage form, and drug. DISCUSSION: This review demonstrates the scarcity of research about the impact of electronic prescribing on medication error and efficiency in community pharmacies. In the literature, most of the studies had mainly focused on hospital pharmacies. The literature search demonstrated that there are still some barriers to overcome with e-prescribing systems and that medication errors were not fully eliminated with e-prescriptions. New errors have been identified with e-prescriptions, all of which caused delays in processing, which affected the productivity of the pharmacy staff, and could have negatively impacted patients' safety if not properly resolved. CONCLUSION: e-Prescribing solved some of the challenges associated with illegibility of handwritten prescriptions. However, more time is required to allow e-prescribing systems to mature. Further training for prescribers and pharmacists is also recommended before and after the implementation.
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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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".