Identifying Nurses at Risk of Nursing Interruptions During Medication Administration Using Machine Learning: A Multicenter Cross‐Sectional Study

Key Points

• The study aims to identify factors influencing nursing interruptions during medication administration and create risk prediction models.
• Conducted a multicenter cross-sectional study to evaluate factors influencing nursing interruptions during medication administration.
• Developed three risk prediction models using machine learning techniques, including a LR-based nomogram and web-based calculators.
• Performed internal validation of the models for reliability.
• The LR-based nomogram and web-based calculators demonstrated consistent performance in identifying at-risk nurses.
• Risk stratification tools may enhance targeted nursing management practices.
• Models require further external validation for practical application.