Abstract 218: Expanding a Learning Collaborative Model in Chicago to Improve Door to Needle for Stroke Thrombolysis: Raising the Bar
Bibliographic record
Abstract
Background: There remains significant opportunities to reduce door-to-needle (DTN) times for stroke despite regional and national efforts. In Chicago, Quality Enhancement for the Speedy Thrombolysis for Stroke (QUESTS) was a one year learning collaborative (LC) which aimed to reduce DTN times at 15 Chicago Primary Stroke Centers. Identification of barriers and sharing of best practices resulted in achieving DTN < 60 minutes within the first quarter of the 2013 initiative and has sustained progress to date. Aligned with Target: Stroke goals, QUESTS 2.0, funded for the 2016 calendar year, invited 9 additional metropolitan Chicago area hospitals to collaborate and further reduce DTN times to a goal < 45 minutes in 50% of eligible patients. Methods: All 24 hospitals participate in the Get With The Guidelines (GWTG) Stroke registry and benchmark group to track DTN performance improvement in 2016. Hospitals implement American Heart Association’s Target Stroke program and share best practices uniquely implemented at sites to reduce DTN times. The LC included a quality and performance improvement leader, a stroke content expert, site visits and quarterly meetings and learning sessions, and reporting of experiences and data. Results: In 2015, the year prior to QUESTS 2.0, the proportion of patients treated with tPA within 45 minutes of hospital arrival increased from 21.6% in Q1 to 31.4% in Q2. During the 2016 funded year, this proportion changed from 31.6% in Q1 to 48.3% in Q2. Conclusions: Using a learning collaborative model to implement strategies to reduce DTN times among 24 Chicago area hospitals continues to impact times. Regional collaboration, data sharing, and best practice sharing should be a model for rapid and sustainable system-wide quality improvement.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".