Impact of AI-powered adaptive ECG simulation training on nurses' knowledge, clinical decision-making and...

Area of Science:

• Nursing Education
• Medical Simulation
• Artificial Intelligence in Healthcare

Background:

• Artificial intelligence (AI) tools show promise in enhancing cognitive skills and clinical decision-making.
• Evidence on the effectiveness of AI in improving nurses' Electrocardiogram (ECG) interpretation knowledge, decision-making, and self-efficacy is limited.

Purpose of the Study:

• To evaluate the impact of AI-Powered ECG simulation training on nurses' knowledge, clinical decision-making, and self-efficacy.
• To assess the effectiveness of interactive AI ECG scenarios with adaptive difficulty, real-time feedback, and performance analytics.

Main Methods:

• An explanatory sequential mixed-methods randomized controlled trial involving 64 nurses.
• Participants were randomized into an intervention (AI simulation) or control group.
• Outcomes measured included ECG knowledge, clinical decision-making (vignette-based and Likert scale), and self-efficacy, assessed pre- and post-intervention.
• Focus group discussions were conducted with 24 nurses to explore their experiences.

Main Results:

• The AI intervention group demonstrated significantly higher scores in ECG knowledge (p < 0.001), clinical decision-making (p < 0.001), and self-efficacy (p = 0.003).
• Qualitative data from focus groups indicated that AI simulation training enhanced the learning experience, improved clinical decision-making, and fostered a sense of skill mastery among nurses.

Conclusions:

• AI-Powered ECG simulation training effectively enhances nurses' knowledge, clinical decision-making abilities, and self-efficacy in ECG interpretation.
• Further research is recommended to investigate the long-term effects of this educational approach on various nursing clinical skills.