Impact of automatic acquisition of key clinical information on the accuracy of electrocardiogram interpretation: a cross-sectional study

Shaohua Guo, Bufan Zhang, Yuanyuan Feng, Yajie Wang, Gary Tse, Tong Liu, Kang Yin Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Background: The accuracy of electrocardiogram (ECG) interpretation by doctors are affected by the available clinical information. However, having a complete set of clinical details before making a diagnosis is very difficult in the clinical setting especially in the early stages of the admission process. Therefore, we developed an artificial intelligence-assisted ECG diagnostic system (AI-ECG) using natural language processing to provide screened key clinical information during ECG interpretation. Methods: Doctors with varying levels of training were asked to make diagnoses from 50 ECGs using a common ECG diagnosis system that does not contain clinical information. After a two-week-blanking period, the same set of ECGs was reinterpreted by the same doctors with AI-ECG containing clinical information. Two cardiologists independently provided diagnostic criteria for 50 ECGs, and discrepancies were resolved by consensus or, if necessary, by a third cardiologist. The accuracy of ECG interpretation was assessed, with each response scored as correct/partially correct = 1 or incorrect = 0. Results: The mean accuracy of ECG interpretation was 30.2% and 36.2% with the common ECG system and AI-ECG system, respectively. Compared to the unaided ECG system, the accuracy of interpretation was significantly improved with the AI-ECG system (P for paired t-test = 0.002). For senior doctors, no improvement was found in ECG interpretation accuracy, while an AI-ECG system was associated with 27% higher mean scores (24.3 ± 9.4% vs. 30.9 ± 10.6%, P = 0.005) for junior doctors. Conclusion: Intelligently screened key clinical information could improve the accuracy of ECG interpretation by doctors, especially for junior doctors.

Original languageEnglish
Article number936
JournalBMC Medical Education
Volume23
Issue number1
DOIs
Publication statusPublished - Dec 2023

Keywords

  • Artificial intelligence
  • Electrocardiogram interpretation
  • Key clinical information

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