ECG Wave-Maven: a self-assessment program for students and clinicians.

Area of Science:

• Medical Education
• Cardiology
• Digital Health

Background:

• Proficiency in electrocardiogram (ECG) interpretation is crucial for clinicians.
• Existing educational resources for ECG literacy are often limited, difficult to maintain, and become outdated.
• Current methods struggle to keep pace with the need for continuous learning in ECG interpretation.

Purpose of the Study:

• To introduce ECG Wave-Maven, an innovative web-based tutorial designed to enhance ECG interpretation skills.
• To address the limitations of traditional ECG educational tools by leveraging live clinical data.
• To provide a dynamic and accessible platform for medical professionals to improve their ECG literacy.

Main Methods:

• Developed a web-based tutorial, ECG Wave-Maven, directly linked to a hospital's clinical ECG repository.
• Interfaced the educational tool with live ECG data to ensure content relevance and reduce manual effort.
• Implemented features for sequential, random, or list-based case review, functioning as both a quiz and a reference tool.

Main Results:

• ECG Wave-Maven overcomes the maintenance and stagnation issues of traditional
• ECG of the week
• websites.
• The direct link to live data significantly reduces the time and effort for case inclusion.
• The platform offers flexible learning options, catering to different user preferences for ECG case review.

Conclusions:

• ECG Wave-Maven provides a sustainable and effective solution for improving ECG literacy among healthcare professionals.
• This innovative tool can help mitigate medical errors stemming from ECG misinterpretations.
• The integration of live clinical data offers a scalable model for medical education resources.