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TRUVACE RECORD VERSION
record: TRV-2026-0651
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-05T06:26:11.500551Z
status: published
lens: trace
sector: health
headline: AI-Assisted Electrocardiogram Interpretation Improves ST-Elevation Myocardial Infarction Diagnostic Accuracy Among Advanced Practice Providers: A Prospective Randomized Crossover Study
dek: Timely and accurate diagnosis of ST-elevation myocardial infarction (STEMI) is critical in military operational environments where evacuation may be delayed. Although artificial intelligence (AI) electrocardiogram (ECG) tools have demonstrated high diagnostic performance, their effectiveness among advanced practice providers (APPs) remains untested. This study evaluated whether AI-ECG interpretation by Queen of Hearts (QoH) AI software by PMcardio improves STEMI diagnostic accuracy, clinician confidence, and tim…
gain_title: AI-assisted interpretation using Queen of Hearts software improved STEMI diagnostic accuracy, sensitivity, specificity, and interrater agreement among certified physician assistants interpreting 12-lead ECGs.
problem_title: AI assistance increased cumulative time-to-decision for ECG interpretation, adding an average of 14.7 seconds per ECG strip.
trace_subject: STEMI diagnosis by physician assistants using Queen of Hearts AI ECG interpretation
gain_reading: AI-assisted interpretation using Queen of Hearts software improved STEMI diagnostic accuracy, sensitivity, specificity, and interrater agreement among certified physician assistants interpreting 12-lead ECGs.
gain_evidence: AI-ECG interpretation significantly improved diagnostic accuracy (92.9% vs. 79.0%; P < .001; Cohen d = 1.68) | With AI assistance, sensitivity was 95.4%, and specificity was 90.5%, compared to 82.5% and 75.6% without AI, respectively
problem_reading: AI assistance increased cumulative time-to-decision for ECG interpretation, adding an average of 14.7 seconds per ECG strip.
problem_evidence: Cumulative time-to-decision was significantly longer with AI (18.8 vs. 13.0 minutes; P < .001; d = 1.13) | The average increase in time per test strip was 14.7 seconds
quick_read: In a prospective randomized crossover study at Carl R. Darnall Army Medical Center, 21 certified physician assistants interpreted 50 de-identified 12-lead ECGs with and without Queen of Hearts AI software by PMcardio. Diagnostic accuracy rose from 79.0% to 92.9% with AI, with sensitivity 95.4% versus 82.5% and specificity 90.5% versus 75.6%, and interrater agreement improved from kappa 0.58 to 0.86.

The improvement matters because timely STEMI recognition is critical where evacuation may be delayed, and advanced practice providers often serve as frontline interpreters. The trade-off was longer decision time, 18.8 versus 13.0 minutes cumulatively, raising questions about workflow impact, generalizability beyond a single center and 50 ECGs, and performance in live operational environments with more variable ECG quality and clinical pressures.
limitation: 
tag: Automated dual reading
key_points: Prospective randomized multi-reader multi-case crossover study at Carl R. Darnall Army Medical Center, Fort Hood, TX, with 21 PA-Cs as their own controls. | Each participant interpreted 50 de-identified 12-lead ECGs (25 STEMI, 25 non-STEMI) with and without Queen of Hearts AI software by PMcardio. | Primary outcome was diagnostic accuracy; secondary outcomes were confidence on 5-point Likert scale and time-to-decision, analyzed with paired t-tests and random-effects crossover model. | Confidence improved with AI: mean Likert total 46.8 vs 56.9, lower score indicates higher confidence, with significant improvement reported.
rundown: The study used a crossover design where each of the 21 PA-Cs interpreted the same 50 ECGs both with and without AI, with analysis facilitated by the Baylor University Statistical Consulting Center.

Authors reported that although time increased, the mean increase of approximately 14.7 seconds per ECG is unlikely to be clinically meaningful, and concluded findings support integration into frontline and operational settings to enhance timely and accurate STEMI recognition.
sources:
- peer_reviewed | Military Medicine | https://doi.org/10.1093/milmed/usag365 | 2026-08-04
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