Can an interprofessional tracheostomy team improve weaning to decannulation times? A quality improvement evaluation.
Bibliographic record
Abstract
BACKGROUND: Percutaneous tracheostomy is a common procedure in the intensive care unit and, on patient transfer to the wards, there is a gap in ongoing tracheostomy management. There is some evidence that tracheostomy teams can shorten weaning to decannulation times. In response to lengthy weaning to decannulation times at Trillium Health Partners - Credit Valley Hospital site (Mississauga, Ontario), an interprofessional tracheostomy team, led by respiratory therapists and consisting of speech-language pathologists and intensive care physicians, was implemented. OBJECTIVE: To evaluate the interprofessional tracheostomy team and its impact on time from weaning off mechanical ventilation to decannulation; and time from weaning to speech-language pathology referral. METHODS: Performance metrics were collected retrospectively through chart review pre- and post-team implementation. The primary metrics evaluated were the time from weaning off mechanical ventilation to decannulation, and time to referral to speech-language pathology. RESULTS: Following implementation of the interprofessional tracheostomy team, there was no improvement in decannulation times or time from weaning to speech-language pathology referral. A significant improvement was noted in the average time to first tracheostomy tube change (36.2 days to 22.9 days; P=0.01) and average time to speech-language pathology referral following initial tracheostomy insertion (51.8 days to 26.3 days; P=0.01). CONCLUSION: An interprofessional tracheostomy team can improve the quality of tracheostomy care through earlier tracheostomy tube changes and swallowing assessment referrals. The lack of improved weaning to decannulation time was potentially due to poor adherence with established protocols as well as a change in mechanical ventilation practices. To validate the findings from this particular institution, a more rigorous quality improvement methodology should be considered in addition to strategies to improve protocol compliance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".