Presentation Description
Aline D. Scherff1
Stefan Kääb22, Martin R. Fischer1 and Markus Berndt1
1 LMU University Hospital, LMU Munich, Institute of Medical Education, Munich, Germany
2 LMU University Hospital, LMU Munich, Department of Medicine I, Munich, Germany
Stefan Kääb22, Martin R. Fischer1 and Markus Berndt1
1 LMU University Hospital, LMU Munich, Institute of Medical Education, Munich, Germany
2 LMU University Hospital, LMU Munich, Department of Medicine I, Munich, Germany
Background
Electrocardiography (ECG) interpretation is a crucial part of medical education and an important professional activity that medical students must master prior to graduation. However, students often struggle with ECG interpretation, with a recent meta-analysis showing a mean diagnostic accuracy of only 40%. Improving ECG interpretation skills early on has the potential to positively impact patient care. Currently, ECG interpretation is usually taught using visual schemas, but research has shown that as expertise increases, physicians rely less on schemas and arrive at more accurate diagnoses more quickly.
Electrocardiography (ECG) interpretation is a crucial part of medical education and an important professional activity that medical students must master prior to graduation. However, students often struggle with ECG interpretation, with a recent meta-analysis showing a mean diagnostic accuracy of only 40%. Improving ECG interpretation skills early on has the potential to positively impact patient care. Currently, ECG interpretation is usually taught using visual schemas, but research has shown that as expertise increases, physicians rely less on schemas and arrive at more accurate diagnoses more quickly.
Summary of work
A study by Scherff et al. (submitted) used eye-tracking and verbal explanations from an expert cardiologist to develop a learning intervention for medical students (EYE-ECG1). This first study showed that students who received the intervention tended to have better ECG interpretation skills and gave more favorable feedback compared to training as usual. The present study EYE-ECG2 built upon the previous study's recommendations to modify the video-audio synchronization of the intervention video and randomize the learning cases.
In the EYE-ECG2 study, 94 medical students underwent a 3.5-hour online learning intervention, which included a pre-test, four cases presenting complex ECG scenarios, and a post-test (cf. Berndt et al., 2020; Schwehr, 2018). The intervention group viewed a 12-minute expert video before the learning cases. This video allowed the students to see the expert's gaze on the ECGs during interpretation (eye tracking) and to hear the expert's verbal explanations.
Summary of results
Employing a multiple regression model, the study found moderate and significant improvement in ECG interpretation skills and replicated the results of the previous study, showing greater gains when using the expert video. However, modifying the video-audio synchronization did not improve student performance, and all patient scenarios meaningfully predicted learning outcome, regardless of the order of presentation.
Discussion and conclusion
In conclusion, presenting students with expert ECG interpretation using eye-tracking and verbal explanations has the potential to improve ECG interpretation skills.
In conclusion, presenting students with expert ECG interpretation using eye-tracking and verbal explanations has the potential to improve ECG interpretation skills.
Take Home Messages
All presented patient scenarios are important for learning outcomes. The conference presentation will include further analyses suggesting that expert videos could hold greater value for advanced learners compared to early learners.
References (maximum three)
Berndt M, Thomas F, Bauer D, Hartl A, Hege I, Kaab S, et al. The influence of prompts on final year medical students’ learning process and achievement in ECG interpretation. GMS Journal for Medical Education. 2020;37(1):Doc 11.
Jarodzka H, Scheiter K, Gerjets P, Van Gog T. In the eyes of the beholder: How experts and novices interpret dynamic stimuli. Learning and Instruction. 2010;20(2):146-54.
Schwehr KA. Klassifizierung und Analyse von Fehlern bei der EKG-Beschreibung, Befundung und Interpretation: LMU München; 2018.