Purpose
This systematic review aimed to evaluate electrocardiogram interpretation competency among emergency and critical care nurses and to examine the diagnostic performance, benefits, and limitations of computerized and artificial intelligence–based electrocardiogram interpretation systems.
Methods
This systematic review was conducted in accordance with PRISMA 2020 guidelines and registered in the International Prospective Register of Systematic Reviews under registration number CRD420251169307. Six electronic databases and additional sources were searched for studies published between January 2020 and October 2025, with the final search conducted in October 2025. Studies were included if they involved registered nurses interpreting electrocardiograms in acute care settings or evaluated computerized electrocardiogram interpretation systems using adult datasets. Methodological quality was assessed using validated tools appropriate to study design, including the Joanna Briggs Institute critical appraisal tools, ROBINS-I, and QUADAS-2.
Results
Mean electrocardiogram interpretation scores among nurses ranged from 43% to 68%, with fewer than 40% of participants meeting predefined competency thresholds. Performance was strongest for asystole recognition and weakest for tachyarrhythmias, myocardial ischemia, and conduction abnormalities. Artificial intelligence–based systems demonstrated high diagnostic accuracy, with area under the curve values ranging from 0.91 to 0.97 and sensitivity exceeding 94% across major diagnostic tasks.
Conclusion
Emergency and critical care nurses demonstrated insufficient electrocardiogram interpretation competency in several safety-critical domains. Computerized and artificial intelligence–based systems showed high diagnostic accuracy and may serve as effective complementary tools when integrated with ongoing nurse education and appropriate clinical oversight.

Purpose
This study examined the relationships among nurses’ readiness for artificial intelligence (AI), attitudes toward AI, and behavioral intention to use AI, focusing on clinical nurses in a tertiary hospital setting.
Methods
A cross-sectional descriptive study was conducted using an online self-report survey of 218 clinical nurses recruited through convenience sampling from a tertiary hospital in South Korea. AI readiness was measured using the Medical Artificial Intelligence Readiness Scale, attitudes toward AI were assessed using the Korean version of the General Attitudes toward Artificial Intelligence Scale, and behavioral intention was measured using items adapted from the Unified Theory of Acceptance and Use of Technology. Open-ended responses were summarized descriptively to explore expected AI applications.
Results
Clinical nurses demonstrated varying levels of AI readiness, attitudes toward AI, and behavioral intention to use AI, and these variables were positively correlated. Among AI readiness dimensions, ability and ethics tended to show stronger bivariate correlations with behavioral intention than vision. Hierarchical regression analysis indicated that attitudes toward AI were strongly associated with behavioral intention (β=.61, p<.001), whereas AI readiness factors showed weaker associations after attitudes were included. Open-ended responses suggested potential AI applications in both direct and indirect nursing care.
Conclusion
Attitudes toward AI were strongly associated with nurses’ behavioral intention to use AI. AI readiness dimensions, particularly ability and ethics, were also associated with behavioral intention in correlation analyses, underscoring the importance of practical competence and ethical awareness. These findings provide empirical evidence to inform AI-related education, clinical integration, and organizational support strategies in nursing.

Purpose
This study aimed to identify the factors affecting patient satisfaction in an emergency department based on the use of the Korea Triage and Acuity Scale (KTAS).
Methods
A survey and medical record review were conducted. Participants included 100 patients and 20 nurses from an emergency medical institution located in B city, between June and August 2020. Data were analyzed using descriptive statistics, independent t-test, one way analysis of variance, partial correlation, and multiple regression.
Results
The mean score of patient satisfaction was 3.99±0.63. The mean waiting time, duration of stay, and occupation rate were 14.29±10.97 min, 104.96 ±67.35 min, and 22.0±7.4%, respectively. From the multiple regression analysis, waiting time (β=-.36, p<.001), nurse’s self-efficacy (β=.19, p=.013), and professional competence (β=.36, p<.001) explained 57.9% of the patients’ satisfaction with their use of the emergency medical institution (F=34.50, p<.001).
Conclusion
Patient satisfaction after experiencing the KTAS was influenced by waiting time, nurses’ self-efficacy, and professional competence. Therefore, institutions need to define an appropriate waiting time that does not reduce patient satisfaction, and introduce an internal marketing strategy to increase nurses’ professional competence and self-efficacy.

Citations

• Patient Satisfaction With the Level of Competence of the Triage Nurse in Hospital Emergency Departments
  Meritxell López Hernández, Montserrat Puig‐Llobet, Sergio Higon Fernández, Marta Franco Freirut, Yolanda Moreno Mateos, Jordi Galimany Masclans
  Journal of Clinical Nursing.2025; 34(9): 3893.     CrossRef
• Factors Influencing Sleep Quality in Patients with Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization: A Cross-Sectional Study
  Ji Young Park, Myoung Soo Kim, Han Byeol Lim
  Korean Journal of Adult Nursing.2024; 36(2): 160.     CrossRef
• Satisfacción de los pacientes sobre la atención recibida por la enfermera en el triaje de los servicios de urgencias hospitalarios: revisión de alcance
  Meritxell López-Hernández, Montserrat Puig-Llobet, Lidia Miralles-Sánchez, Yolanda Moreno-Mateos, Jordi Galimany-Masclans
  Enfermería Clínica.2024; 34(5): 408.     CrossRef
• Patient satisfaction with nursing triage care in hospital emergency departments: Scoping review
  Meritxell López-Hernández, Montserrat Puig-Llobet, Lidia Miralles-Sánchez, Yolanda Moreno-Mateos, Jordi Galimany-Masclans
  Enfermería Clínica (English Edition).2024; 34(5): 408.     CrossRef
• Emergency nurses’ communication experiences with patients and their families during the COVID-19 pandemic: A qualitative study
  Soyoung Shin, Hye Jin Yoo
  International Emergency Nursing.2023; 66: 101240.     CrossRef