Abstract WP237: Improved Thrombolysis Rates and Quality of Care for Stroke Patients Through a Provincial Emergency Department Quality Improvement Initiative
Bibliographic record
Abstract
Background: The care of stroke patients in the emergency department (ED) is time sensitive and complex. We sought to improve quality of care for stroke patients in British Columbia (B.C.), Canada, emergency departments. Objectives: To measure the outcomes of a large-scale quality improvement initiative on thrombolysis rates and other ED performance measures. Methods: This was an evaluation of a large-scale stroke quality improvement initiative, within ED’s in B.C., Canada, in a before-after design. Baseline data was derived from a medical records review study performed between December 1, 2005 to January 31, 2007. Adherence to best practice was determined by measuring selected performance indicators. The quality improvement initiative was a collaboration between multidisciplinary clinical leaders within ED’s throughout B.C. in 2007, with a focus on implementing clinical practice guidelines and pre-printed order sets. The post data was derived through an identical methodology as baseline, from March to December 2008. The primary outcome was the thrombolysis rate; secondary outcomes consisted of other ED stroke performance measures. Results: 48 / 81 (59%) eligible hospitals in B.C. were selected for audit in the baseline data; 1258 TIA and stroke charts were audited. For the post data, 46 / 81 (57%) acute care hospitals were selected: 1199 charts were audited. The primary outcome of the thrombolysis rate was 3.9% (23 / 564) before and 9.3% (63 / 676) after, an absolute difference of 5.4% (95% CI: 2.3% - 7.6%; p=0.0005). Other measures showed changes: administration of aspirin to stroke patients in the ED improved from 23.7% (127 / 535) to 77.1% (553 / 717), difference = 53.4% (95% CI: 48.3% - 58.1%; p=0.0005); and, door to imaging time improved from 2.25 hours (IQR = 3.81 hours) to 1.57 hours (IQR 3.0), difference = 0.68 hours (p=0.03). Differences were found in improvements between large and small institutions, and between health regions. Conclusions: Implementation of a provincial emergency department quality improvement initiative showed significant improvement in thrombolysis rates and adherence to other best practices for stroke patients. The specific factors that influenced improvement need to be further explored.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".