| dc.description.abstract | ABSTRACT
Background
Electrocardiography records the heart's electrical signals through electrodes placed on the chest, producing a graph of voltage versus time. The ECG is not only used for diagnostic purposes, but also for heart monitoring, biometric recognition, and even has a potential to decode basic emotional state of a human. The accurate ECG interpretation enables the detection of heart abnormalities and the timely management of life-threatening conditions. Since cardiovascular diseases are a leading cause of death worldwide, making accurate interpretation is crucial for early diagnosis and appropriate treatment. However, the accuracy of ECG interpretation varies among medical professionals, including cardiologists and non-cardiologists, leading to the need for improved education and training. The challenges in ECG interpretation are discussed in this research, including limitations of computerized interpretation, variations in teaching methods, deficiencies in ECG training, and the need to distinguish normal ECG features from abnormal. Factors such as athleticism, sex, and ethnicity also influence ECG interpretation and must be considered for accurate diagnosis and personalized healthcare.
Methodology
To identify the needs for improvement in ECG education and technology, face-to-face interviews were conducted with cardiologists, health providers, scientists and a single semi-structured interview with medical students. The data collected has been analyzed and three grounded theories have been constructed, opening for discussion on possible changes in educational methods and automatized interpretation algorithms in order to evaluate accuracy in ECG interpretation and diagnosis.
Results
The findings from this research identified three needs in the field of electrocardiogram and discuss possible solutions for improvement in this field. The first identified need is based on feedback from medical students and physicians and speaks for an interactive tool for ECG interpretation self-education and practice. The existing online platforms and ECG apps have been explored. It has been found that none of the existing solutions fully satisfied the identified needs. Adjustments and improvements for the interactive tool have been proposed in the discussion.
The second identified need speaks for the creation of a Norwegian ECG database that would provide real-case examples and contribute to machine learning. This idea was discussed with a cardiologist and a professor who expressed their opinions on the process of such a data collection.
The third identified need speaks for implementation of a "machine-human collaboration" course in medical education programs to enhance understanding and trust in machine interpretation. The author believes that educating medical students about the capabilities and limitations of technology would lead to more effective collaboration.
Conclusions
Even with the fast-evolving innovations in the field of heart diagnostics there are still opportunities in ECG interpretation practice to improve the accuracy of ECG analysis and enhance the efficiency of use of technology in healthcare settings.
Keywords
electrocardiogram, heart diagnostics, ECG interpretation | |
