Association of Rapid Care Process Implementation on Reperfusion Times Across Multiple ST-Segment–Elevation Myocardial Infarction Networks
Bibliographic record
Abstract
BACKGROUND: The Mission: Lifeline STEMI Systems Accelerator program, implemented in 16 US metropolitan regions, resulted in more patients receiving timely reperfusion. We assessed whether implementing key care processes was associated with system performance improvement. METHODS AND RESULTS: Hospitals (n=167 with 23 498 ST-segment-elevation myocardial infarction patients) were surveyed before (March 2012) and after (July 2014) program intervention. Data were merged with patient-level clinical data over the same period. For reperfusion, hospitals were grouped by whether a specific process of care was implemented, preexisting, or never implemented. Uptake of 4 key care processes increased after intervention: prehospital catheterization laboratory activation (62%-91%; P<0.001), single call transfer protocol from an outside facility (45%-70%; P<0.001), and emergency department bypass for emergency medical services direct presenters (48%-59%; P=0.002) and transfers (56%-79%; P=0.001). There were significant differences in median first medical contact-to-device times among groups implementing prehospital activation (88 minutes implementers versus 89 minutes preexisting versus 98 minutes nonimplementers; P<0.001 for comparisons). Similarly, patients treated at hospitals implementing single call transfer protocols had shorter median first medical contact-to-device times (112 versus 128 versus 152 minutes; P<0.001). Emergency department bypass was also associated with shorter median first medical contact-to-device times for emergency medical services direct presenters (84 versus 88 versus 94 minutes; P<0.001) and transfers (123 versus 127 versus 167 minutes; P<0.001). CONCLUSIONS: The Accelerator program increased uptake of key care processes, which were associated with improved system performance. These findings support efforts to implement regional ST-segment-elevation myocardial infarction networks focused on prehospital catheterization laboratory activation, single call transfer protocols, and emergency department bypass.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".