Harnessing Generative AI in Nursing Informatics: A Theoretical Critique and Policy Innovation Perspective From Japan

IF 3.7 3区医学 Q1 NURSING

International Nursing Review Pub Date : 2025-09-19 DOI:10.1111/inr.70108

Kazumi Kubota, Miya Aishima, Takanori Fujita

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引用次数: 0

Abstract

Aim

This article provides a theoretical critique and policy innovation perspective on integrating generative artificial intelligence (AI) into nursing informatics. It draws on a 2023 nationwide survey in Japan and established models, including the technology acceptance model and risk perception frameworks, to examine AI's transformative potential and its challenges.

Background

Rapid AI evolution is reshaping healthcare by enhancing clinical decision-making and efficiency. Generative AI shows promise in improving patient outcomes, yet its integration into nursing practice raises ethical, educational, and regulatory concerns. Nursing informatics now requires robust governance, ethical oversight, and updated educational frameworks.

Sources of Evidence

Evidence is derived from a 2023 survey by the Health and Global Policy Institute and a comprehensive literature review. Key studies (e.g., Nashwan et al. 2025; Booth et al. 2021) and foundational models (Davis 1989; Venkatesh et al. 2003) underpin the analysis.

Discussion

The findings suggest that while generative AI may enhance access to information and service quality, significant challenges—such as low digital literacy, liability issues, data accuracy, and privacy concerns—persist. A multidimensional strategy, incorporating targeted education, ethical governance, and continuous feedback from healthcare practitioners, is essential for effective integration.

Conclusion

Generative AI holds transformative promise for nursing informatics. However, its successful adoption depends on balancing innovation with comprehensive regulatory and educational strategies to mitigate associated risks.

Implications for Nursing Practice and Policy

Enhancing digital literacy, establishing clear guidelines for liability and data security, and fostering international collaboration are critical to the safe and effective integration of AI in nursing practice.

查看原文本刊更多论文

在护理信息学中利用生成式人工智能：来自日本的理论批判和政策创新视角。

目的：本文为生成式人工智能（AI）与护理信息学的融合提供了理论批判和政策创新视角。它借鉴了日本2023年的一项全国性调查，并建立了包括技术接受模型和风险感知框架在内的模型，以研究人工智能的变革潜力及其挑战。背景：人工智能的快速发展正在通过提高临床决策和效率来重塑医疗保健。生成式人工智能有望改善患者的治疗效果，但它与护理实践的结合引发了伦理、教育和监管方面的担忧。护理信息学现在需要强有力的治理、道德监督和更新的教育框架。证据来源：证据来自卫生与全球政策研究所2023年的一项调查和一项全面的文献综述。关键研究（如Nashwan et al. 2025; Booth et al. 2021）和基础模型（Davis 1989; Venkatesh et al. 2003）支持了这一分析。讨论：研究结果表明，虽然生成式人工智能可以提高信息获取和服务质量，但仍存在重大挑战，如数字素养低、责任问题、数据准确性和隐私问题。多维战略，包括有针对性的教育、道德治理和医疗保健从业人员的持续反馈，对于有效整合至关重要。结论：生成式人工智能为护理信息学带来了变革的希望。然而，它的成功采用取决于平衡创新与全面的监管和教育战略，以减轻相关风险。对护理实践和政策的影响：提高数字素养，制定明确的责任和数据安全准则，促进国际合作，对于安全有效地将人工智能融入护理实践至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Nursing Review 医学-护理

CiteScore

7.90

自引率

7.30%

发文量

审稿时长

6-12 weeks

期刊介绍： International Nursing Review is a key resource for nurses world-wide. Articles are encouraged that reflect the ICN"s five key values: flexibility, inclusiveness, partnership, achievement and visionary leadership. Authors are encouraged to identify the relevance of local issues for the global community and to describe their work and to document their experience.