Evidence‐Based Practice Competence Mediates the Link Between AI Readiness and Clinical Judgment Among Critical Care Nurses

BACKGROUND: The integration of artificial intelligence (AI) into clinical practice is reshaping healthcare, particularly in critical care settings where rapid and complex decisions are required. Nurses' AI readiness and evidence-based practice (EBP) competence are essential for supporting sound clinical judgment. AIM: To examine the influence of AI readiness on clinical judgment among critical care nurses and to determine whether EBP competence mediates this relationship. METHODS: A descriptive cross-sectional study was conducted from August to November 2024 among 450 critical care nurses working in intensive and critical care units across three governmental university hospitals in XX, XX. AI readiness, EBP competence, and clinical judgment were measured using validated self-report instruments. Structural equation modeling (SEM) was used to assess direct and indirect effects, with model fit indices applied to evaluate model adequacy. The structural equation model demonstrated acceptable fit indices (CFI = 1.000, IFI = 1.000, RMSEA = 0.073). CONCLUSIONS: AI readiness and EBP competence are significant contributors to clinical judgment in critical care nursing. Strengthening these competencies is essential to enhance nurses' decision-making capability in increasingly digitalized healthcare environments. However, the cross-sectional design and reliance on self-report instruments limit causal inference and may introduce response bias. LINKING EVIDENCE TO ACTION: Integrating AI readiness and EBP competence training into critical care nursing education and continuing professional development may strengthen nurses' clinical judgment and support safer, evidence-informed decision-making in technology-enhanced care environments.