ARTICLE Medication Error Reporting Systems: A Survey of Canadian Intensive Care Units
Bibliographic record
Abstract
Background: Patients in the intensive care unit (ICU) have complex problems and experience many medical errors. Currently, little is known about the measurement of medication errors and adverse drug events in Canadian ICUs. Objective: To investigate methods of measuring medication errors and adverse drug events in ICUs in Canada. Methods: A questionnaire was constructed and uploaded to an online survey tool, SurveyMonkey. Through the mailing list software of the Critical Care Pharmacy Specialty Network of the Canadian Society of Hospital Pharmacists, the survey was sent by e-mail to 146 pharmacists working in 79 ICUs across Canada; 2 reminder e-mails followed. The survey was open from July 18 to September 18, 2007. Results: A total of 34 individual responses were received from 31 (39%) of the 79 ICUs. Responses were from academic hospitals (11/31 [35%]), community teaching hospitals (9/31 [29%]), and community nonteaching hospitals (11/31 [35%]). Twenty-six (84%) of the 31 responding ICUs had a process for tracking medication errors and adverse drug events: non-anonymous voluntary reporting (19 or 73%), direct observation (14 or 54%), anonymous voluntary reporting (12 or 46%), chart review (6 or 23%), computerized system (3 or 12%), trigger tools (2 or 8%), pharmacist intervention (2 or 8%), and weekly ICU “safety huddles ” (1 or 4%). Fourteen (54%) of the 26 ICUs that had a method of measuring medication errors and adverse drug events had implemented changes to address identified problems. Conclusions: Most respondents were measuring the frequency of medication errors and adverse drug events, but a wide variety of methods were in use. Only about half of the ICUs had implemented changes as a result of these measurements. There is an opportunity to improve standardization of the measurement of medication errors and adverse drug events in Canadian ICUs.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".