Abstract WP399: Improving Stroke and TIA Care in the Emergency Department: Results from a Provincial Stroke Improvement Collaborative
Bibliographic record
Abstract
Background: Stroke and transient ischemic attack (TIA) are common disorders cared for by emergency physicians. Best practices exist for the management of these disorders; however, these are not always followed. OBJECTIVES: This study sought to measure the impact of a coordinated, provincial structured improvement collaborative, on care for stroke and TIA patients within emergency departments (ED’s) in British Columbia. METHODS: This study was a qualitative and quantitative process evaluation of a provincial improvement collaborative. The collaborative followed an Institute of Healthcare Improvement (IHI) Methodology, consisting of five workshops, bi-weekly webinars, and improvement coaching and support, over a 10-month period, from Sept 2011 to June 2012. This evaluation examined process measures of success, such as self-reported improvement and adherence to certain clinical process measures (e.g. time to CT). Collaborative participants submitted monthly reports that quantified their improvement using the Self Assessment Score, which is used in IHI Collaborative Methodology. RESULTS: 17 multidisciplinary teams participated, representing 27% of all ED’s in British Columbia (29 / 108), and all five Health Authorities. Further, all but one of the province’s tPA enabled sites were represented. 90% of teams (15 / 17) reported a median Self Assessment Score greater than 4.5/5.0, indicating significant and sustainable improvements in stroke and TIA care. No teams dropped out of the Collaborative. Teams reported a mean of 5.5 (SD 2.4) significant improvements within their ED. For example, shortened door-to-CT times, improved triage and training, and implementation of standardized swallowing screens were shown. This collaborative showed higher success rates of self-reported improvement (typically only 30% of teams achieve significant improvement) and lower rates of dropouts, than the published literature. CONCLUSIONS: Using an IHI structured collaborative format, a provincial stroke & TIA emergency department quality improvement initiative was able to achieve significant improvements in adherence to best practices. A formal outcome evaluation is planned to measure the impact on stroke incidence, disability, and mortality.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".