[The Translation, Innovation, and Interprofessional Application of Evidence-Based Health Care in Clinical Practice]

Only when scientific evidence moves beyond research conclusions and is translated into clinical decision-making, organizational systems, and standard patient care can the true value of evidence-based practice be fully realized. Current scholarship suggests that, in addition to high-quality research, leadership capacity, appropriate implementation strategies, and the effective integration and governance of technology-enabled systems within clinical contexts are all critical to ensuring the adoption, practice, and impact of evidence-based healthcare strategies (Fontaine et al., 2024). Reflecting this broader development trajectory, the articles in this issue tackle evidence-based implementation strategies, nursing workforce considerations, systems thinking, and the evolving role of artificial intelligence (AI) in clinical care. At the nursing leadership and organizational governance level, the findings of a recent systematic review indicate nurse leaders with strong evidence-based competencies are well positioned to facilitate changes in clinical practice, strengthen interprofessional collaboration, and enhance both quality of care and organizational performance (V̈lim̈ki et al., 2024). The studies in this review reinforce the view that evidence-based practice, rather than being a responsibility shouldered by frontline nurses alone, must be a shared institutional capacity embedded within leadership and organizational structures. Implementation strategies are central to effectively translating evidence into practice. The findings of a systematic review and meta-analysis by Fontaine et al. (2024) demonstrate multifaceted and integrated approaches such as education and training, clinical reminders, leadership engagement, and structured feedback mechanisms help promote sustained improvements in nursing practice and, in turn, positively influence patient outcomes. As digital health technologies continue to advance, the role of AI in supporting clinical decision-making has become an increasingly prominent focus of evidence-based care discussions. While AI technologies hold considerable promise in supporting nurse clinical decision-making, enhancing efficiency, and reducing workload, their effectiveness in real-world clinical settings remains highly contingent upon user trust, system transparency, and organizational support (Mikkonen et al., 2026; Ouanes & Farhah, 2024). In high-intensity clinical environments, appropriately designed and well-governed AI systems may enable nurses to direct more time and professional judgment toward making complex, person-centered care decisions. Concurrently, emerging evidence cautions that, more than a technical endeavor, the implementation of AI has significant implications in the realms of workflow design, professional autonomy, and ethical responsibility. Almagharbeh (2025) emphasizes that, in the absence of clear role delineation and meaningful clinical engagement, AI-based decision support systems may inadvertently increase workloads and erode trust among healthcare professionals. This perspective aligns closely with the foundational principles of evidence-based care, under which technology is expected to support, not supplant, professional judgment and human-centered practice. In summary, the articles in this issue all support that the future of evidence-based care lies at the intersection of research evidence, organizational design, leadership, and technology governance. Only through the concurrent consideration and integration of high-quality evidence, local clinical contexts, innovative technologies, and ethical accountability can evidence-based care be sustained as a meaningful driver of patient well-being, professional development, and system-level resilience.