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

Guo, Shaohua, Zhang, Bufan, Feng, Yuanyuan, Wang, Yajie, Tse, Gary, Liu, Tong, Chen, Kang-Yin (2023) Impact of automatic acquisition of key clinical information on the accuracy of electrocardiogram interpretation: a cross-sectional study. BMC Medical Education, 23 (1). Article Number 936. ISSN 1472-6920. (doi:10.1186/s12909-023-04907-9) (KAR id:104247)


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.

Item Type: Article
DOI/Identification number: 10.1186/s12909-023-04907-9
Uncontrolled keywords: Artificial intelligence, Electrocardiogram interpretation, Key clinical information
Subjects: R Medicine
Divisions: Divisions > Division of Natural Sciences > Kent and Medway Medical School
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 16 Feb 2024 15:04 UTC
Last Modified: 16 Feb 2024 21:42 UTC
Resource URI: (The current URI for this page, for reference purposes)

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