110 Improving Asthma Education in the Emergency Department: A Quality Improvement Initiative
Bibliographic record
Abstract
Abstract Introduction/Background Asthma education and action plans have been shown to improve compliance and symptom control. Provincial guidelines, created in 2015, included asthma action plans, but use of these resources across our site was not consistent. Objectives The objectives of this study were to develop and implement an asthma education package, standardize discharge instructions and improve appropriate referrals to Asthma Clinics. Design/Methods Using process mapping, staff surveys and patient interviews, we undertook a current state analysis. The resulting change ideas were implemented between October 2018 and January 2020 in 5 PDSA (Plan Do Study Act) cycles, utilizing chart reviews and a standardized data tool to measure outcomes. Rates of repeat Emergency Department (ED) attendances, 2 weeks following the initial encounter and overall rates of ED asthma visits were assessed, using patient medical record data. Results Two-hundred-and-twenty-five ED presentations were reviewed, 65.2% (146/224) had a previous diagnosis of asthma. 48.9% (110/225) reported using an inhaled corticosteroid (ICS) at presentation. 89.7% (201/224) had not seen a healthcare provider during this acute illness. Asthma action plan utilization increased from 0% at baseline to an average of 60%, sustained over 2 years. 74.2% (167/225) had an ICS prescribed or advised at discharge. Only 3.8% (8/209) of patients re-presented to an ED within 2 weeks of this asthma visit. 57.3% (129/225) children were referred for ongoing pediatric care: either by a community pediatrician (72.9%; 94/129) or our hospital Asthma Clinic (34.8%; 32/129). Between 2017 and 2019, there was no significant change in total asthma presentations to our ED/per year (1300, 1395 and 1307, respectively) (Figure 1). Conclusion A standardized asthma education package including pre-printed discharge resources, asthma action plans, and a provincially adopted, multi-language education video was successfully implemented into our ED. This demonstrates a multi-disciplinary approach to asthma education that can be utilized across the province. Our data highlights the need for a strong community-based approach for asthma care, and further work is ongoing to assess the efficacy of this education package on medication compliance and recurrent ED visits.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".