10 Minute Tracing: A Literature Review to Improving Acquisition Times of ECGs in Emergency Departments
Bibliographic record
Abstract
Introduction: ST-elevation myocardial infarction is a type of acute coronary syndrome (ACS) that requires immediate diagnosis using an electrocardiogram (ECG) and timely reperfusion with primary percutaneous coronary intervention or fibrinolysis. This expedited diagnosis is crucial in saving cardiac tissue, reducing mortality, and preventing future complications. The 2019 Canadian Cardiovascular Society guidelines present a strong recommendation for an ECG to be acquired and interpreted within 10 minutes from first medical contact (1). Currently this recommendation is not adequately met. Objectives: This literature review investigates the strengths and limitations of several strategies that attempt to reduce the door-to-electrocardiogram (D2E) time in Emergency Departments (EDs). Furthermore, this literature review also investigates which of these strategies are the most effective in improving the acquisition time of ECGs for patients that present with symptoms concerning for ACS. Methods: A literary search using key terms was performed using two databases; Medline and Embase, from 2014-2024 encompassing articles relevant to interventions for improving D2E times within EDs. This search yielded nine articles that met the inclusion criteria. Results: Statistically significant interventions to reduce D2E time include obtaining an immediate ECG for patients with ACS symptoms, having a dedicated ECG machine and technician for triage, triage staff education and optimization, using team members beyond triage nurses to initiate an ECG, and having regular interdisciplinary meetings and team communication. Discussion and Conclusion: While no single ideal strategy to improving D2E times exists, this review concludes four main steps to achieving optimal D2E times: A process to identify patients who require an ECG, having proper infrastructure in EDs to accommodate a dedicated ECG at triage, initiating specific protocols to empower other staff members to identify those with symptoms concerning for ACS, and maintaining ongoing education and communication with the interdisciplinary team. This type of quality improvement work should be individually tailored to each unique ED to get the best yet currently realistic D2E times that are also sustainable for the future.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".