A Quality Improvement Initiative to Increase Confirmation of Prehospital Endotracheal Tube Placement at Emergency Department Transfer of Care
Bibliographic record
Abstract
OBJECTIVES: Rates of prehospital unplanned extubation (UE) range from 0 to 25% and are the result of many factors, including patient movement. Transfer of care of intubated patients to the emergency department (ED) involves significant patient movement and represents a high-risk event for UE. Frequent confirmation of endotracheal tube (ETT) placement is imperative for early recognition of UE and to minimize patient harm. METHODS: Local Practice-Our baseline rate of verbal ETT position confirmation with a member of the ED team during ED transfer of care was 74%. Our goal was to increase this practice to >90% in six months. This project was completed in partnership with Toronto Paramedic Services. Prehospital electronic patient care records (ePCRs) were reviewed weekly to determine the proportion of intubated patients who had ETT placement confirmed in the ED at transfer of care. Interventions-Pre- and post-project paramedic focus groups were conducted to identify potential drivers, change ideas, and project feedback. Three staggered interventions were introduced over five months: (1) memorandums to paramedics, ED chiefs and respiratory therapy leads, (2) individualized paramedic feedback e-mails, and (3) ePCR changes and closing rules. RESULTS: The pre-project focus group identified several potential drivers, such as physical barriers, interprofessional relationships, and communication. ETT confirmation remained ≥90% for the last eight weeks and interventions resulted in special cause variation. Median cases without verbal confirmation between paramedics and ED staff reduced from 5/week (IQR 2.5, 6.5) to 1/week (IQR 0, 2). UE was identified in 0.6% (2/340) of patients with ETT confirmation. The post-project focus group noted improvements in perceived accountability, interprofessional relationships, and satisfaction with interventions. CONCLUSION: Through a series of interventions, we improved the rate of ETT confirmation during ED transfer of care. Although rates of UE were low, improvement in ETT confirmation may lead to faster recognition of UE when it does occur thereby mitigating complications. The observed improvement was sustained after interventions ended.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".