E-prescribing and medication safety in community settings: A rapid scoping review
Bibliographic record
Abstract
Medication prescribing is essential for the treatment, curing, maintenance, and/or prevention of an illness and disease, however, medication errors remain common. Common errors including prescribing and administration, pose significant risk to patients. Electronic prescribing (e-prescribing) is one intervention used to enhance the safety and quality of prescribing by decreasing medication errors and reducing harm. E-prescribing in community-based settings has not been extensively examined. To map and characterize the current evidence on e-prescribing and medication safety in community pharmacy settings. We conducted a rapid scoping review of quantitative, qualitative, and mixed methods studies reporting on e-prescribing and medication safety. MEDLINE All (OVID), Embase (Elsevier), CINAHL Full Text (EBSCOHost), and Scopus (Elsevier) databases were searched December 2022 using keywords and MeSH terms related to e-prescribing, medication safety, efficiency, and uptake. Articles were imported to Covidence and screened by two reviewers. Data were extracted by a single reviewer and verified by a second reviewer using a standardized data extraction form. Findings are reported in accordance with JBI Manual for Evidence Synthesis following thematic analysis to narratively describe results. Thirty-five studies were included in this review. Most studies were quantitative (n = 22), non-experimental study designs (n = 16) and were conducted in the United States (n = 18). Half of included studies reported physicians as the prescriber (n = 18), while the remaining reported a mix of nurse practitioners, pharmacists, and physician assistants (n = 6). Studies reported on types of errors, including prescription errors (n = 20), medication safety errors (n = 9), dispensing errors (n = 2), and administration errors (n = 1). Few studies examined patient health outcomes, such as adverse drug events (n = 5). Findings indicate that most research is descriptive in nature and focused primarily on rates of prescription errors. Further research, such as experimental, implementation, and evaluation mixed-methods research, is needed to investigate the effects of e-prescribing on reducing error rates and improving patient and health system outcomes.
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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 | 0.023 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".