Medication errors in community pharmacies: Evaluation of a standardized safety program
Bibliographic record
Abstract
Background: The mandated reporting of medication-related errors in community pharmacies including incidents resulting in inappropriate medication use and near misses intercepted before reaching the patient can be utilized as learning opportunities to aid in the prevention of future events. Objectives: To examine reporting uptake, trends, and initial learnings from medication errors reported by community pharmacists to the Assurance and Improvement in Medication Safety (AIMS) Program based in Ontario, Canada between April 1st, 2018, and June 30th, 2021. Methods: A descriptive analysis was conducted of all events reported to the AIMS Program during the study period. The web-based reporting form includes a series of mandatory and optional fields completed by the reporter. Individual medications were grouped into broader classes prior to conducting the analysis. Results: Among the 31,768 event reports received from 2856 community pharmacies, there were 19,639 incidents and 12,129 near misses. Low reporting followed by a rapid increase was observed during expansion of the AIMS Program in 2018, with almost 60% of Ontario community pharmacies submitting at least 1 event over the study period. In most cases (90.5%), no patient harm was reported. The most frequent event types involved the incorrect drug (19.5%), concentration (17.2%) or quantity (14.5%). Approximately 25% of events were identified by the involved patient or their agent. When looking at medication classes, antihypertensives, opioids and antidepressants were involved in over one-quarter of overall and higher severity events. Environmental staffing problems and interruptions were the contributory factor and sub-factor most frequently reported, respectively. Conclusions: This study provides insights into engagement with the AIMS Program by Ontario community pharmacy teams since implementation in 2018. The identification of the circumstances and medications associated with both incidents and near misses, aids in the continued development of strategies and processes to help prevent future events.
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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 | 0.031 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".