Smartphone AI Assistance for Prehospital ECG Interpretation

Summary

Prehospital providers interpret 12-lead electrocardiograms (ECGs) under time pressure and without immediate expert support. Missed acute coronary occlusion - occlusion myocardial infarction (OMI) - delays reperfusion, while false positive interpretations trigger unnecessary catheterization laboratory activations. Multimodal large language models (LLMs) available on any smartphone can now analyze a photographed ECG, and prehospital providers have begun using them spontaneously. No randomized trial has evaluated whether this practice improves diagnostic performance. This randomized controlled trial compares the diagnostic performance of prehospital providers interpreting ECG clinical vignettes with and without mandatory assistance from a single, version-locked smartphone large language model. Participants - paramedics, emergency medical technicians, nurses and physicians practicing in prehospital care in French-speaking Switzerland - are randomized 1:1 on a dedicated digital platform and answer 14 clinical vignettes presented in individually randomized order. Each vignette is built around a real, anonymized 12-lead ECG obtained during routine clinical care. The primary outcome is the proportion of vignettes for which the participant correctly identifies the presence or absence of an OMI. Secondary outcomes are sensitivity, specificity, and the accuracy of the prehospital priority decision level.

Eligibility

Inclusion Criteria:

* Prehospital care provider practising in French-speaking Switzerland
* Any level of training: emergency medical technician, paramedic (ES), nurse (ES/HES) in prehospital emergency care, or prehospital emergency physician
* Electronic informed consent signed before randomization

Exclusion Criteria:

* Cardiologist
* Any person not practising in prehospital care
* Insufficient command of written French to answer the vignettes reliably
* Refusal to participate or withdrawal of consent

Conditions6

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