Asthma care bundle and pathway to improve asthma management in a community paediatric inpatient unit, a quality improvement initiative
Bibliographic record
Abstract
Background: Asthma is the most common chronic paediatric condition and a frequent cause of emergency department visits and hospitalizations. Objectives: The project objective was to decrease inpatient length of stay (LOS) for asthma exacerbations between May 2021 and 2022. Methods: The Institute for Healthcare Improvement Model for improvement was employed to study if systemic changes to asthma management could reduce hospital LOS. The inpatient asthma care bundle consisted of a discharge checklist, standardized care pathway that allowed nurse titration of bronchodilator based on an objective scoring tool and standardized team education. Results: The pre-intervention mean inpatient LOS was 56 h, 100 h for patients with a Paediatric Intensive Care Unit (PICU) stay, and 52 h for patients without a PICU stay. While the mean PICU LOS remained unchanged, the mean LOS for patients without PICU stay decreased to 34 h and was sustained through the project's completion. The percentage of healthcare professionals feeling "comfortable"/'very comfortable' caring for asthmatic patients remained unchanged during the project (100%). Caregivers' confidence regarding asthma management mean score increased from 6/10 to 9/10 after hospital discharge. No statistical increase respiratory-based emergency department presentation within 10 days of discharge, use of high-flow ventilation and transfer to PICU was noted. Conclusions: Implementing an inpatient asthma care bundle reduced the mean LOS for patients without PICU stay from 52 to 34 h, which represents a 35% decrease. The most impactful intervention was the implementation of the inpatient asthma management pathway.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".