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 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 | 0.007 | 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 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".