Improving the management of acute myocardial infarctions: There's an App for that
Bibliographic record
Abstract
Background ST-Elevation Myocardial Infarction (STEMI) is a critical emergency. Managing care requires accurate diagnosis, shared communication between decision-makers, and timely transport and reperfusion at a hospital with capacity for such interventions. This study examines the implementation of a smartphone application (SMART AMI-ACS App) to facilitate real-time ECG sharing, enhancing communication and decision-making in STEMI management. Methods This multi-centre study evaluated the implementation, acceptability and uptake of the App among interventional cardiologists and emergency medicine (EM) physicians managing suspected STEMI patients between April 1st 2022 and March 31st 2023. Guided by the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework, STEMI registry data and post-implementation surveys from a large regional cardiac centre and its 13 partner emergency departments in Ontario, Canada, were used to assess App uptake and effectiveness. Results During the 12-month evaluation 254 (84 %) of the eligible 300 regional EM physicians downloaded the App, with > 1400 ECG images sent from 724 patients. Users reported the App helped in communication and timing of care. No degradation of ECG images was observed. App use was associated with lower door-in-door-out (DIDO) times 48 min (IQR 31–67) vs 55 min (IQR 39–77) and lower proportion of non-STEMI cases accepted to interventional cardiology (22 % vs 39 %, p < 0.0001). Conclusion Uptake of the SMART AMI-ACS App was positive and may be associated with lower non-STEMI cases and lower DIDO times. The App provided a secure channel for communication of information and point-of-care transfer of images across healthcare providers. Uptake of the App has expanded to other regions.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".