Smartphone AI Assistance for Prehospital ECG Interpretation

Proportion of vignettes with correct identification of occlusion myocardial infarction (OMI) status

For each of the 14 vignettes, the participant answers a binary question: "At this stage of care, is an OMI (acute coronary occlusion) likely? Yes / No". Responses are scored against a reference standard defined a priori, vignette by vignette, by the study cardiologist and locked before data collection. This reference standard is the answer expected of a prehospital provider at the point of care, anchored on coronary angiography wherever angiography is discriminant. For non-ischaemic mimics the expected answer depends on whether the acute presentation allows the condition to be distinguished from a coronary occlusion: it does not for the Takotsubo case (expected answer "yes"), whereas acute pericarditis is usually recognisable (expected answer "no"). This pre-specified departure from a purely angiographic standard is reported as such, and the analysis is repeated in a sensitivity analysis classifying all mimics as non-OMI. The outcome is the proportion of correctly classified vignettes

Sensitivity of OMI detection

Proportion of correctly identified OMI among vignettes whose expected answer is "yes", compared between arms. Analysed by mixed-effects logistic regression restricted to these vignettes, with arm as fixed effect and a random intercept per participant.

Specificity of OMI detection

Proportion of correctly identified non-OMI among vignettes whose expected answer is "no" (non-occlusive ischaemia, indeterminate ischaemia, recognisable non-ischaemic mimics, normal or non-specific ECGs), compared between arms. Same modelling approach as for sensitivity.

Accuracy of the prehospital priority decision level

Proportion of vignettes for which the participant selects the correct prehospital priority decision level, on a pre-specified five-level scale ranging from high-risk acute coronary syndrome with activation of the STEMI pathway to clearly non-cardiac symptoms. The expected level is defined a priori for each vignette and locked before data collection.

Diagnostic accuracy on the closed-list diagnosis question

For each vignette the participant selects a single diagnosis from a closed list of 86 items grouped in 8 families, using a filterable selector. Two variables are produced automatically: exact match with the expected diagnosis, and match with the expected diagnosis or with any diagnosis pre-specified as clinically acceptable. Reported descriptively by ECG category and by arm, then modelled by mixed-effects logistic regression if numbers allow. The primary analysis uses the exact-or-acceptable variable; the strict variable is reported as a sensitivity analysis.

Self-reported confidence and calibration

Confidence in the answer is reported by the participant for each vignette on a five-point Likert scale of self-reported diagnostic confidence, ranging from 1 (not at all confident) to 5 (very confident). A higher score indicates greater self-reported confidence; confidence is a descriptive measure and a higher score is not in itself a better outcome. Confidence is compared between arms by mixed-effects ordinal logistic regression. Calibration is assessed as the concordance between reported confidence and the actual correctness of the answer, using calibration curves and the Brier score, which ranges from 0 to 1, a lower score indicating better calibration.

Self-reported influence of the AI on the final answer

Degree to which the participant reports having been influenced by the model's interpretation, on a five-point Likert scale, collected for each vignette in the intervention arm only. Analysed descriptively and in relation to the correctness of the answer. No comparison between AI tools is conducted, the model version being fixed.

Response time per vignette

Time from display of the vignette to submission of the answer, measured automatically in seconds by the platform. Analysed by linear mixed-effects model on the log of the response time, with arm as fixed effect and a random intercept per participant; a sensitivity analysis compares individual median times between arms. A longer time is expected in the intervention arm owing to consultation of the model, and is treated as a feasibility outcome for prehospital use.