Abstract 101: A Learning Collaborative Model to Improve Door to Needle Time for Stroke Thrombolysis in Chicago
Bibliographic record
Abstract
Background: Door-to-needle (DTN) times have remained suboptimal despite overall increases in tissue plasminogen activator use (tPA) for stroke in Chicago. The American Heart Association’s (AHA) Quality Enhancement for Speedy Thrombolysis in Stroke (QUESTS) initiative aimed to identify barriers to reduce DTN times at Chicago’s 15 primary stroke centers (PSCs) and increase the proportion of patients treated with tPA within 60 minutes of hospital arrival. Methods: Starting in January 2013, we used face-to-face and on-site meetings with each PSC’s stroke team members to share AHA Target Stroke best practices and strategies to reduce DTN time. A survey of current practice was completed at each site to determine opportunities for improvement and repeated at 1 year to assess implementation of new strategies. We used the Get With The Guidelines (GWTG) Stroke registry to aggregate baseline data DTN times and track performance in each quarter of 2013. Results: At baseline, 5 strategies were notably under-utilized at Chicago’s 15 PSCs: 1) Direct to CT scanner (baseline: 0 sites; 1 year: 5 sites); 2) pre-mixing tPA (baseline: 1 site; 1 year: 14 sites); 3) tPA prior to laboratory results (baseline: 3 sites; 1 year: 7 sites); 4) stroke code activation at triage (baseline: 4 sites; 1 year: 13 sites); and 5) streamlined consent process (baseline: 0 sites; 1 year: 12 sites. The proportion of patients treated within 60 minutes increased in each quarter of 2013 from 25% in quarter 1 to 60% in quarter 4 (p<0.01). The median DTN time decreased from 89.5 minutes in quarter 1 to 55 minutes in quarter 4 (p<0.01). Conclusions: Using a learning collaborative model to implement strategies to reduce DTN times among 15 PSCs in Chicago, we observed major improvements within a few months. 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".