Pharmacist-led educational interventions provided to healthcare providers to reduce medication errors: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Medication errors are avoidable events that can occur at any stage of the medication use process. They are widespread in healthcare systems and are linked to an increased risk of morbidity and mortality. Several strategies have been studied to reduce their occurrence including different types of pharmacy-based interventions. One of the main pharmacist-led interventions is educational programs, which seem to have promising benefits. OBJECTIVE: To describe and compare various pharmacist-led educational interventions delivered to healthcare providers and to evaluate their impact qualitatively and quantitatively on medication error rates. METHODS: A systematic review and meta-analysis was conducted through searching Cochrane Library, EBSCO, EMBASE, Medline and Google Scholar from inception to June 2020. Only interventional studies that reported medication error rate change after the intervention were included. Two independent authors worked through the data extraction and quality assessment using Crowe Critical Appraisal Tool (CCAT). Summary odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using a random-effects model for rates of medication errors. Research protocol is available in The International Prospective Register of Systematic Reviews (PROSPERO) under the registration number CRD42019116465. RESULTS: Twelve studies involving 115058 participants were included. The two main recipients of the educational interventions were nurses and resident physicians. Educational programs involved lectures, posters, practical teaching sessions, audit and feedback method and flash cards of high-risk abbreviations. All studies included educational sessions as part of their program, either alone or in combination with other approaches, and most studies used errors encountered before implementing the intervention to inform the content of these sessions. Educational programs led by a pharmacist were associated with significant reductions in the overall rate of medication errors occurrence (OR, 0.38; 95% CI, 0.22 to 0.65). CONCLUSION: Pharmacist-led educational interventions directed to healthcare providers are effective at reducing medication error rates. This review supports the implementation of pharmacist-led educational intervention aimed at reducing medication errors.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".