Use of prehospital ultrasound in North America: a survey of emergency medical services medical directors
Bibliographic record
Abstract
BACKGROUND: Advances in ultrasound imaging technology have made it more accessible to prehospital providers. Little is known about how ultrasound is being used in the prehospital environment and we suspect that it is not widely used in North America at this time. We believe that EMS system characteristics such as provider training, system size, population served, and type of transport will be associated with use or non-use of ultrasound. Our study objective was to describe the current use of prehospital ultrasound in North America. METHODS: This study was a cross-sectional survey distributed to EMS directors on the National Association of EMS Physicians (NAEMSP) mailing list. Respondents had the option to complete a paper or electronic survey. RESULTS: Of the 755 deliverable surveys we received 255 responses from across Canada and the United states for an overall response rate of 30%. Of respondents, 4.1% of EMS systems (95% CI 1.9, 6.3) reported currently using ultrasound and an additional 21.7% (95% CI 17, 26.4) are considering implementing ultrasound. EMS services using ultrasound have a higher proportion of physicians (p < 0.001) as their highest trained prehospital providers when compared to the survey group as a whole. The most commonly cited current and projected applications are Focused Abdominal Sonography for Trauma (FAST) and assessment of pulseless electrical activity (PEA) arrest. The cost of equipment and training are the most significant barriers to implementation of ultrasound. Most medical directors want evidence that prehospital ultrasound improves patient outcomes prior to implementation. CONCLUSIONS: Prehospital ultrasound is infrequently used in North America and there are a number of barriers to its implementation, including costs of equipment and training and limited evidence demonstrating improved outcomes. A research agenda for prehospital ultrasound should focus on patient-important outcomes such as morbidity and mortality. Two commonly used indications that could be a focus of standardized training programs are the FAST exam, and assessment of PEA arrest.
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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 | 0.002 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".