Impact of a standardized emergency department asthma care pathway on health services utilization
Bibliographic record
Abstract
BACKGROUND: An evidence-based standardized ED asthma care pathway (EDACP) was developed and implemented in Ontario, Canada. OBJECTIVE: To determine the impact of EDACP implementation and access to ED asthma management resources and specialists on return ED visits. METHODS: All 173 Ontario hospitals were surveyed regarding their access to community and ED asthma specialists and ED asthma management resources, including EDACP implementation date and status as of August 2017. Survey data were linked to provincial health administrative data to quantify acute health services utilization. A Poisson regression interrupted time series analysis was conducted. RESULTS: Of the 123 hospitals responding to the survey, 44 (35.8%) had approved the EDACP. Data were analyzed for the 5 years preceding (30,028 asthma visits) and 17 months following (7,916 asthma visits) implementation, with a 3-month implementation black-out period. After controlling for auto-regressive factors, EDACP implementation was associated with a 2% reduction in the absolute rate of return ED visits within 72 h (p = 0.0124), and within 7 days (p = 0.0295) at teaching hospitals. The same effect was not seen at community hospitals. Peak expiratory flow testing (available at 77% of sites) and spirometry (available at 45% of sites) were associated with 34% (p = 0.0071) and 23% (p = 0.028) reductions in the odds of return ED visits within 72 h, respectively. CONCLUSION: The positive results from this large-scale effort to implement an evidence-based knowledge translation initiative in diverse settings, suggests there is merit in continuing to invest time and resources to overcome barriers to adoption and implementation of this EDACP.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".