<![CDATA[Introduction: Electrocardiogram (ECG) interpretation is an important skill for physicians to identify both cardiac and non-cardiac irregularities. As technology accelerates, ECG learning methods have expanded to online platforms. However, implementation still met challenges, particularly the newly graduated doctors working in remote, border, and island areas. Learning Management System (LMS) using a website offers a pivotal approach, enabling learning experiences without limitations of time or location, while simultaneously contributing to continuing medical education (CME). This study aims to develop a website-based LMS for emergency ECG training to enhance the diagnostic abilities and ECG interpretation skills of newly graduated doctors in rural areas. Methods: A quasi-experimental pretest-posttest study with consecutive sampling was conducted to recruit participants. They were given a 6-week timeframe, including completing all modules and live discussions. Pre-tests and post-tests were administered, and learning perspectives were assessed using a questionnaire. Results: A total of 48 newly graduated doctors participated in the study, but only 32 (66.7%) finished all the courses, including the post-test. The majority of participants were female, and all were under 30 years old. The average post-test score (66.00 ± SD 15.447) was significantly higher than the average pre-test score (56.67 ± SD 14.223), demonstrating a 10.67 point knowledge increment (p=0.009). Furthermore, the course yielded a significant increase in self-reported interpretation skills, confidence in managing cases, and perceived importance of ECG knowledge (p<0.001), although motivation to continue learning showed no significant change (p=0.82).Conclusion: Website-based ECG learning significantly improves knowledge, self-reported ECG interpretation skills, and clinical confidence among newly graduated doctors in remote settings, demonstrating a scalable and flexible educational model vital for bridging educational and diagnostic skill gaps in resource-limited areas.]]>
