通过模拟加强护理实践：解决障碍并推进人工智能在医疗保健中的整合

IF 6.3 4区医学 Q1 NURSING

Journal of Nursing Regulation Pub Date : 2025-10-01 DOI:10.1016/j.jnr.2025.08.004

Mohamed Benfatah PhD , Ilham Elazizi MSN , Hajar Belhaj PhD , Abderrahmane Lamiri PhD

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引用次数: 0

摘要

人工智能（AI）在护理实践中的整合代表了医疗保健领域的重大进步，在临床决策、工作流程效率和患者护理管理方面提供了有希望的改进。然而，它的广泛实施面临着障碍，例如培训不足、对技术变革的抵制和监管的不确定性。目的本研究评估护士在重症监护环境中对人工智能的接受程度，确定阻碍人工智能采用的主要障碍，并评估基于人工智能的模拟培训在提高护士能力和促进临床实践中对人工智能技术接受度方面的有效性。方法采用准实验混合方法设计。护士使用人工智能工具（包括IBM Watsonx和Qventus）参与模拟临床场景。数据收集方法包括直接临床观察、能力评估、满意度调查和定性访谈，以全面了解用户体验和结果。结果研究显示护士使用人工智能的信心显著增加，从培训前的35.9%增加到培训后的81.3% (p < 0.001)，同时临床反应时间显著减少（从21.4秒减少到13.0秒）。结论运用人工智能工具进行模拟培训，有效提高了护士的临床能力和信心，有助于提高患者安全和操作效率。为了支持人工智能在护理实践中的成功整合，医疗机构必须解决培训差距和监管障碍。未来的举措应侧重于实施结构化的教育计划和制定明确的政策，以促进在临床环境中道德和有效地采用人工智能技术。

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Enhancing nursing practice through simulation: Addressing barriers and advancing the integration of artificial intelligence in healthcare

Background

The integration of Artificial Intelligence (AI) in nursing practice represents a significant advancement in healthcare, offering promising improvements in clinical decision-making, workflow efficiency, and patient care management. However, its widespread implementation faces obstacles, such as inadequate training, resistance to technological change, and regulatory uncertainties.

Purpose

This study assesses nurses' receptiveness to AI in critical care settings, to identify the main barriers hindering its adoption, and to evaluate the effectiveness of AI-based simulation training in enhancing nurses’ competencies and promoting acceptance of AI technologies in clinical practice.

Methods

A quasi-experimental mixed-methods design was employed. Nurses participated in simulated clinical scenarios using AI tools, including IBM Watsonx and Qventus. Data collection methods included direct clinical observation, competency assessments, satisfaction surveys, and qualitative interviews to gain comprehensive insight into user experience and outcomes.

Results

The study revealed a significant increase in nurses’ confidence in using AI—from 35.9 % before training to 81.3 % after training (p < 0.001)—along with a notable reduction in clinical response time (from 21.4 s to 13.0 s).

Conclusion

Simulation-based training involving AI tools effectively improves nurses’ clinical competencies and confidence, contributing to enhanced patient safety and operational efficiency. To support successful AI integration in nursing practice, healthcare institutions must address training gaps and regulatory barriers. Future initiatives should focus on implementing structured educational programs and developing clear policies to facilitate the ethical and efficient adoption of AI technologies in clinical settings.

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来源期刊

Journal of Nursing Regulation NURSING-

CiteScore

4.60

自引率

12.50%

发文量

审稿时长

54 days

期刊介绍： Journal of Nursing Regulation (JNR), the official journal of the National Council of State Boards of Nursing (NCSBN®), is a quarterly, peer-reviewed, academic and professional journal. It publishes scholarly articles that advance the science of nursing regulation, promote the mission and vision of NCSBN, and enhance communication and collaboration among nurse regulators, educators, practitioners, and the scientific community. The journal supports evidence-based regulation, addresses issues related to patient safety, and highlights current nursing regulatory issues, programs, and projects in both the United States and the international community. In publishing JNR, NCSBN''s goal is to develop and share knowledge related to nursing and other healthcare regulation across continents and to promote a greater awareness of regulatory issues among all nurses.