Systemic analysis of medication administration omission errors in a tertiary-care hospital in Quebec
Bibliographic record
Abstract
OBJECTIVE: Medication administration omission errors (MAOEs) occur frequently in hospitals and can significantly affect patient health. An interdisciplinary committee was formed in summer 2012 to analyse incident/accident reports (AH-223-1 forms) of MAOEs for the 2011-2012 fiscal year in order to identify contributing factors and to propose preventive solutions. Special attention was paid to events with consequences for patients. METHOD: An aggregate data analysis involving four major steps was conducted: sampling, categorisation, identification of contributing factors, and seeking preventive solutions. One hundred omissions were randomly selected from the 889 reported for this period. All omissions categorised as having had consequences for patients were then added, making a final total of 145 omissions. The omissions were categorised using an Ishikawa diagram developed from an exploratory literature review and process mapping. Subsequent to failure modes, effects and criticality analysis, cause-and-effect diagrams were constructed with the main prioritised categories to differentiate the proximal causes from the root causes. Brainstorming was used to develop solutions, which were then prioritised with an impact/effort matrix. RESULTS: This study identified 27 categories of MAOEs, of which the 7 most frequent and the most critical accounted for 79.3% of the reports. The event categories, in decreasing order of importance, were related to intravenous (IV) therapy (29.0%), failure in using the medication administration record (MAR; 23.4%), failure in creating/updating the MAR (10.3%), medications on the patient's bedside (7.6%), and three types of MAOEs related to transcribing prescriptions (9.0%). CONCLUSION: The interdisciplinary committee formulated 10 main recommendations related to these 7 categories, including 3 for IV therapy and 4 for failure in using or creating/updating the MAR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".