Speed and accuracy of text-messaging emergency department electrocardiograms from a small community hospital to a provincial referral center
Bibliographic record
Abstract
BACKGROUND: Currently, transmission of electrocardiograms (EKGs) from a small emergency department (ED) to specialists at referral hospitals can be a time-consuming and laborious process. We investigate whether text messaging by use of short message service (SMS) of EKGs from a small hospital to consultants at a large hospital is rapid and accurate. METHODS: This study involved a one-month prospective evaluation of consecutive EKGs recorded in a small community ED. Investigators obtained de-identified photographs of each EKG via a mobile phone camera. Each EKG picture, along with a brief patient clinical history, was sent via SMS to on-call emergency physicians located at a large referral care site. All images were evaluated solely on a mobile phone. The primary outcome was the proportion of SMS that were received within two minutes of being sent. As a secondary outcome, the intra-rater evaluation of the initial EKG and the SMS EKG image were compared on 13 standardized features. The tertiary outcome was cost of text messaging. RESULTS: A total of 298 patients (14.6%) had 409 EKGs performed and a total of 926 SMS were sent. 921 SMS (99.5%, 95% confidence interval (CI) 98.7-99.8%) arrived within two minutes with a median transmission time of nine seconds (interquartile range (IQR) 3-32 s). Between the gold standard original EKG, and the interpretation of the texted image, six out of 409 (1.5%, 95% CI 0.6-3.3%) had any differences recorded, across all 13 categories. Overall, the study cost 4.1 cents per texted image. CONCLUSIONS: Systematic text messaging of ED EKGs from a small community hospital to a referral center is a rapid, accurate, portable, and inexpensive method of data transfer. This may be a safe and effective strategy to communicate vital patient information.
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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 | 0.004 | 0.059 |
| 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 | 0.001 | 0.001 |
| 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 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".