The electrocardiogram (ECG) is the most commonly used and key diagnostic test in medicine. The 12-lead ECG is used for screening and diagnosis of heart diseases, including many life-threatening conditions. Incorrect ECG interpretation can lead to grave fatal outcome in patient management. Although the integration of computerized ECG interpretation software into modern ECG machines, the sensitivity and specificity of current technology still remain poor, that make need of human medical doctors to perform ECG interpretation accurately. Contemporary data on the diagnostic accuracy of computerized ECG interpretation in determining cardiac rhythm were recently demonstrated to be 88% overall, with 95% correct identification of sinus rhythm, but poor interpretation of non-sinus rhythm at only 53.5%. ECG interpretation is poorly performed among both undergraduate and postgraduate students. Several studies have highlighted insufficiencies in ECG interpretation among medical students and residents from different countries. Polish medical students from 10 medical schools in their clinical years have a good level of competency in interpreting the primary ECG parameters such as heart rate, the origin of heart rhythm, and electrical axis of the heart. However, their ability to recognize ECG signs of life threatening disorders and common heart abnormalities is low. Nigel et al. reported only 52% accuracy in interpreting various ECGs among 52 final-year medical students from New Zealand. According to the review article (Fent et al, 2014), no single teaching strategy is most effective in delivering ECG interpretation skills. Methods described in the literature include tutorials, lectures, teaching rounds and self-directed learning. Web-based packages have risen to prominence in recent years. Currently, there is a lack of data about the best promising method of ECG interpretation teaching. Clinical educators should look for the new methods to enhance traditional undergraduate medical education. Research team developed a new learning platform that is game based ECG learning platform using principles of gamification to deliver and assess interpretation skills among undergraduate medical students at University Malaysia Sabah (UMS) in 2016–2017. Gamification is stated as ‘the use of game design and mechanic to enhance non-game contexts’. Any application, task, process or context can theoretically be gamified. Gamification’s main goal is to rise the engagement of users by using game-like techniques such as scoreboards and personalized fast feedback (Flatla et al, 2011) making people feel more ownership and purpose when engaging with tasks (Pavlus, 2010). We named our software application as GaMED-ECG. Gamification elements included in GaMED-ECG were (1) voluntary participation; (2) explicit, consistent, software-enforced rules of competition for all participants; (3) immediate feedback (response correct or incorrect, followed by explanation of key concepts); (4) individual participation and (5) participants could increase in rank or level badges and (5) awarding system. The students can play (GaMED) during their free time using mobile apps. The new ECG game apps is strategically designed by researcher collaboration with educationist, information technologists and game specialist in accordance with gamification design theory by Professor Werbach and Dan Hunter six step (“D6”) gamification design framework. ECGs interpretation are verified by two independent physiologists, cardiologists and emergency physicians. For the evaluation of the new ECG learning platform, CIPP evaluation model was used to improve upon the programme itself. CIPP stands for Context, Input, Process, Product.
[1]
I. Mainie,et al.
Electrocardiogram and rhythm strip interpretation by final year medical students.
,
2001,
The Ulster medical journal.
[2]
S. Trzeciak,et al.
Variation in patient management based on ECG interpretation by emergency medicine and internal medicine residents.
,
2002,
The American journal of emergency medicine.
[3]
C. Tracy,et al.
ACC/AHA clinical competence statement on electrocardiography and ambulatory electrocardiography. A report of the ACC/AHA/ACP-ASIM Task Force on Clinical Competence (ACC/AHA Committee to Develop a Clinical Competence Statement on Electrocardiography and Ambulatory Electrocardiography).
,
2001,
Journal of the American College of Cardiology.
[4]
Juho Hamari,et al.
Does Gamification Work? -- A Literature Review of Empirical Studies on Gamification
,
2014,
2014 47th Hawaii International Conference on System Sciences.
[5]
Alan D. Lopez,et al.
The global burden of disease: a comprehensive assessment of mortality and disability from diseases injuries and risk factors in 1990 and projected to 2020.
,
1996
.
[6]
Daniel L. Stufflebeam,et al.
The CIPP Model for Program Evaluation
,
1983
.
[7]
Dan,et al.
[ACM Press the 15th International Academic MindTrek Conference - Tampere, Finland (2011.09.28-2011.09.30)] Proceedings of the 15th International Academic MindTrek Conference on Envisioning Future Media Environments - MindTrek \'11 - From game design elements to gamefulness
,
2011
.
[8]
C. Fisch.
Evolution of the clinical electrocardiogram.
,
1989,
Journal of the American College of Cardiology.