Abstract
BACKGROUND: Electrocardiography (ECG) plays a pivotal role in diagnosing certain cardiac pathologies. Interpreters receive significant training in this area; however, an electrocardiogram must be performed correctly for accurate interpretation. It should also be recorded and interpreted swiftly. The aim of this study was to assess the level of technical knowledge regarding ECG performance among healthcare professionals. METHODS: The study was conducted between June 2023 and September 2023. A total of 243 participants, including nurses, doctors, and medical and nursing faculty students, were enrolled. The participants were asked 16 questions via a Google survey. The participants were statistically compared based on their professions and workplaces. RESULTS: The relationships between the descriptive characteristics of the participants and electrocardiography knowledge scores were not statistically significant in terms of age, gender, employment status, or duration of employment (p > 0.05). However, medical faculty students had significantly higher ECG knowledge scores compared to nurses (p < 0.01). Among the nurses, those working in surgical units had significantly lower scores than those working in internal medicine and pediatric units (p < 0.05). CONCLUSIONS: In this study, 16 questions were used to evaluate ECG acquisition knowledge. The ECG knowledge scores of the participants ranged between 0-14 and the mean score was 6.95 ± 3.13, which is quite low for a simple and applicable test. A simple mistake or delay in performing an electrocardiogram can lead to catastrophic consequences. Although nurses are the primary personnel performing ECG, their technical knowledge in performing the procedure was not as proficient as doctors. Some areas of focus may lead to errors. Although research has consistently focused on interpreting ECG, we still do not know how to perform ECG properly. More emphasis should be placed on ECG training, with the potential for regular training throughout the year as needed. These simple and preventable errors can be more easily overcome with artificial intelligence-supported programs.