Regional Learning Collaboratives Produce Rapid and Sustainable Improvements in Stroke Thrombolysis Times
Bibliographic record
Abstract
BACKGROUND: Reduction in door-to-needle (DTN) times in patients with acute ischemic stroke treated with tissue-type plasminogen activator is associated with improved outcomes. We hypothesized that a learning collaborative would rapidly reduce DTN times at Chicago's primary stroke centers. METHODS AND RESULTS: We analyzed data from all adult patients with out-of-hospital ischemic stroke hospitalized between January 1, 2010 and March 31, 2015 and who received tissue-type plasminogen activator in the emergency department at 15 primary stroke centers in Chicago and 15 primary stroke centers in St. Louis. We implemented a structured learning collaborative in Chicago in quarter 1 of 2013 that included (1) a quality improvement leader, (2) stroke content expert, (3) multidisciplinary teams from each site, (4) a targeted goal for the program (DTN time <60 minutes in >50% of patients treated with tissue-type plasminogen activator), and (5) face-to-face meetings with on-site visits. We used interrupted time-series analysis to compare the impact of the learning collaborative on DTN times in Chicago pre- and post implementation and also concurrently versus St. Louis. We prespecified adjustment for mode of arrival, emergency medical services prenotification, and onset-to-arrival times. P values less than 0.05 were considered significant. In adjusted analysis, the reduction in DTN time within 1 quarter of implementation was 15.5 minutes (P=0.046) at Chicago sites versus 1.17 minutes at St. Louis sites (P=0.601). CONCLUSIONS: Using a learning collaborative model at Chicago's 15 primary stroke centers, we observed major reductions in DTN times within 1 quarter of implementation. Regional collaboration and best practices sharing should be a model for rapid and sustainable system-wide quality improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".