Safety implications of the dose change alert function in smart infusion pumps on the administration of high-alert medications.
Bibliographic record
Abstract
BACKGROUND: Most intravenous medication errors occur during administration. Smart pumps can reduce the incidence of dose or rate errors using soft and hard limits. However, industry standard dose error reduction software misses errors that occur during titration. The dose change alert was developed to detect errors during titration. PURPOSE: To evaluate the safety implications of the dose change alert in the SIGMA Spectrum Infusion System on the administration of high-alert medications at The Ottawa Hospital. METHOD: This retrospective analysis included all titratable high-alert medication infusions administered between May 1 and October 31, 2014 (inclusive). Analysis of continuous quality improvement reports included drug library compliance, dose change alerts, soft limit confirmations and cancellations, and hard limit pull-back reports for each high-alert medication and care area. FINDINGS: Compliance with using the drug library was 96.8%. The percentage of dose change alert confirmations and cancellations within the soft limits were 48.1% and 1.9%, respectively. The titration of vasopressors resulted in the highest percentage (75%) of dose change alert confirmations. The titration of anticoagulants resulted in the highest percentage (12%) of dose change alert cancellations. Titration within the soft limits accounted for 65% of the alerts. CONCLUSIONS: This study provided insight into the safety implications of the dose change alert on the titration of high-alert medications. Key-press errors during titration of high-alert medications can cause patient harm, even within the soft limits. Nurses can be involved in customizing the percentage dose change limit for individual drugs within each care area to provide an additional safety check during titration.
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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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".