Trends and Contributing Factors in Medication Home Delivery Incidents in Community Pharmacies Before and After COVID-19: A Retrospective Analysis
Bibliographic record
Abstract
This study examines medication home delivery incidents reported in community pharmacies before and after the onset of the COVID-19 pandemic. Medication home delivery incidents are defined as medication errors that occur during the transportation of medication to patients outside the pharmacy through shipping, courier, or pharmacy delivery services. The objective was to analyze trends over time and identify contributing factors to inform patient safety improvements. A retrospective analysis was conducted on medication home delivery incidents reported to a national Community Pharmacy Incident Reporting system, Pharmapod (a Think Research company). Reports from 4091 community pharmacies across 10 provinces and 2 Territories were reviewed, covering the period from January 1, 2019, to January 27, 2022. A total of 156 medication home delivery incidents were identified and analyzed. Of the 156 incidents, 55 (35%) occurred pre-COVID and 101 (65%) post-COVID. The most frequent incident type was delivery to the incorrect patient, which decreased from 52.8% to 32.7%. In contrast, privacy breaches increased significantly from 29.2% to 41.6%. Contributing factors include staffing distribution, lack of quality control or independent checks, environmental distraction, operational workflow gaps, and insufficient staff training. Medication home delivery incidents increased following the onset of the COVID-19 pandemic, revealing emerging safety risks in pharmacy delivery practices. The shift in incident types and contributing factors highlights the need for improved verification protocols, enhanced privacy protections, and dedicated staff training. As home delivery services continue to expand, these findings underscore the importance of system-level interventions to safeguard medication safety in the community pharmacy setting.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".