Improving Electrocardiography Diagnostic Accuracy in Emergency Medical Services Personnel
Bibliographic record
Abstract
BackgroundAccuracy of electrocardiogram (ECG) interpretation is important for identification of ST-elevation myocardial infarction (STEMI) by Emergency Medical Services (EMS) personnel who recognize STEMI in the field and activate the coronary catheterization laboratory. According to previous research, there is improvement in diagnosis of STEMIs for healthcare providers who read an average of > 20 ECGs per week. This study evaluated the effectiveness of online ECG modules on improving diagnostic accuracy.MethodsEMS personnel received 25 ECGs per week to interpret via an online program. Diagnostic accuracy was assessed for improvement via completion of an ECG evaluation package before and after the intervention. Job satisfaction data were collected to determine the impact of the educational initiative.ResultsA total of 64 participants completed the study. Overall, there was an improvement in ECG diagnostic accuracy from 50.8% to 61.2% (95% confidence interval [CI], 7.7-13.2; P < 0.0001). Specifically, there was significant improvement in the diagnosis of STEMI (8.5%; 95% CI, 4.9-12.3; P < 0.003) and supraventricular tachycardia (39.0%; 95% CI, 17.2-60.8; P < 0.008), with a trend toward improvement in all other diagnoses. These effects were sustained to 3 months (9.6%; 95% CI, 6.4-12.7; P < 0.0001). Improvement was seen regardless of employment experience and training. There was no significant impact on job satisfaction.ConclusionsECG exposure remains an important factor in improving the accuracy of ECG diagnosis in EMS personnel. Online education modules provide an easily accessible way of improving ECG interpretation with the opportunity for positive downstream effects on patient outcomes and resource use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".