From “Airway scares me” to “I would say I’m pretty comfortable”: quality improvement for reducing time to obtain equipment for adult advanced airway management in a rural emergency department
Bibliographic record
Abstract
BACKGROUND: Management of the adult airway is one of the most stressful and time-critical procedures in emergency medicine. In the Cowichan District Hospital, a rural hospital in British Columbia, Emergency Department (ED) staff were uncomfortable with acquiring the equipment needed for adult advanced airway management and the mean length of time to acquire the equipment was 319 s. The aim of this quality improvement (QI) project was to decrease the time to obtain the equipment needed for adult advanced airway management by nurses and physicians in the Cowichan District Hospital ED to less than 90 s by May 2023. METHODS: The Institute for Healthcare Improvement model of improvement was used to reduce the amount of time required to obtain the equipment for adult difficult airway management in the ED, which was measured using a standardised tabletop simulation every 2 weeks. Change ideas included using a colour-coded airway cart and employing translational simulation. Qualitative interviews with emergency department staff after intubations of patients in the ED captured process measures by examining provider comfort. RESULTS: From December 2022 to May 2023, the mean time to obtain equipment for adult advanced airway management decreased from an initial value of 319 s to 76 s, a 76% improvement from the baseline. Qualitative interviews obtained pre-intervention, mid-intervention and post-intervention reflected themes of initial discomfort, shifting discomfort to comfort and finally to comfort. CONCLUSION: The change ideas of using a colour-coded airway cart and translational simulation were associated with a reduction in time to obtain equipment for management of the adult advanced airway as well as improved provider comfort with the procedure in a rural ED.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".