Abstract 163: Hurry Acute Stroke Treatment and Evaluation (HASTE): Improving Door-to-Needle Times and Reducing Variation for Acute Ischemic Stroke using a Six-Sigma Approach
Bibliographic record
Abstract
Background: The importance of immediate tissue-type plasminogen activator (tPA) therapy for acute ischemic strokes has long been recognized to achieve better outcomes. Clinical Best Practice Guidelines state that an acceptable treatment window for acute ischemic stroke patients is 4.5 hours from symptom onset. Within this time window, general consensus is the patient should arrive at the hospital within 3.5 hours of symptom onset, and the door-to-needle (DTN) time should be within 60 minutes of arrival. However, compliance with this DTN time is low with as few as 27% of acute stroke patients in the US being treated within 60 minutes. Methods: The Calgary Stroke Program, a comprehensive stroke center at the Foothills Medical Centre, engaged in a six-sigma quality improvement project to improve DTN times. Six-sigma is a data-driven improvement approach with five phases: define the system and project goals; measure the process; analyze the data; improve the process; and control the improvements. The factors that led to improvement were: physician leadership; stroke team engagement with EMS, emergency department, admitting and diagnostic imaging; use of STAT! stroke pager to alert the stroke team when EMS or triage identified an eligible tPA patient; distribution of a weekly reports for DTN; and provision of a weekly award for the best case of the week under 30 minutes. Results: The six-sigma approach resulted in a significant reduction in DTN. Data analysis was done for three periods: 1) the pre-implementation period (June 2011 to May 2013, n=300); 2) the implementation period (June 2013 to September 2013, n=46); and 3) the post implementation period (October 2013 to December 2013, n=39). The results showed a reduction in DTN from a median time of 53 minutes (mean=61 min) in the pre period to a median time of 51 minutes (mean=58 min) in the implementation period, and finally to a median time of 39 minutes (mean=46 minutes) in the post period. Based on six-sigma data analysis, the defects, defined by a DTN greater than 60 minutes, went down from 38 for every 100 patients (sigma level of 1.81) in the pre period to 26 for every 100 patients (sigma level of 2.15) in the post period. Furthermore, the results from a one-way ANOVA for the DTN times for these 3 periods reached statistical significance (p=0.013) with post-hoc analysis revealing that the difference between the pre and post periods results were significant. Conclusions: The use of quality improvement approaches such as six-sigma can result in significant improvement in DTN. The data-driven approach of six-sigma allowed the Calgary Stroke Program to identify and improve various processes that effect the DTN time, which resulted in a lower average DTN time and a reduction in variation.
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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 | 0.003 | 0.000 |
| 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".