Unknown

Dataset Information

0

Validation of a Novel Digital Tool in Automatic Scoring of an Online ECG Examination at an International Cardiology Meeting.


ABSTRACT:

Background

We have previously developed a novel digital tool capable of automatically recognizing correct electrocardiography (ECG) diagnoses in an online exam and demonstrated a significant improvement in diagnostic accuracy when utilizing an inductive-deductive reasoning strategy over a pattern recognition strategy. In this study, we sought to validate these findings from participants at the International Winter Arrhythmia School meeting, one of the foremost electrophysiology events in Canada.

Methods

Preregistration to the event was sent by e-mail. The exam was administered on day 1 of the conference. Results and analysis were presented the following morning to participants.

Results

Twenty-five attendees completed the exam, providing a total of 500 responses to be marked. The online tool accurately identified 195 of a total of 395 (49%) correct responses (49%). In total, 305 responses required secondary manual review, of which 200 were added to the correct responses pool. The overall accuracy of correct ECG diagnosis for all participants was 69% and 84% when using pattern recognition or inductive-deductive strategies, respectively.

Conclusion

Utilization of a novel digital tool to evaluate ECG competency can be set up as a workshop at international meetings or educational events. Results can be presented during the sessions to ensure immediate feedback.

SUBMITTER: Quinn KL 

PROVIDER: S-EPMC6931661 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

altmetric image

Publications

Validation of a Novel Digital Tool in Automatic Scoring of an Online ECG Examination at an International Cardiology Meeting.

Quinn Kieran L KL   Crystal Eugene E   Lashevsky Ilan I   Arouny Banafsheh B   Baranchuk Adrian A  

Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc 20150922 4


<h4>Background</h4>We have previously developed a novel digital tool capable of automatically recognizing correct electrocardiography (ECG) diagnoses in an online exam and demonstrated a significant improvement in diagnostic accuracy when utilizing an inductive-deductive reasoning strategy over a pattern recognition strategy. In this study, we sought to validate these findings from participants at the International Winter Arrhythmia School meeting, one of the foremost electrophysiology events in  ...[more]

Similar Datasets

| S-EPMC8608207 | biostudies-literature
| S-EPMC8214673 | biostudies-literature
| S-EPMC8527193 | biostudies-literature
| S-EPMC7838287 | biostudies-literature
| S-EPMC8138193 | biostudies-literature
| 2753375 | ecrin-mdr-crc
| S-EPMC8826162 | biostudies-literature
| S-EPMC7113740 | biostudies-literature
| S-EPMC7113722 | biostudies-literature