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探索人工智能在护理领域的深度学习:运用沃克和阿凡特方法进行的概念分析

Exploring the deep learning of artificial intelligence in nursing: a concept analysis with Walker and Avant's approach.

作者信息

Wangpitipanit Supichaya, Lininger Jiraporn, Anderson Nick

机构信息

Visiting Assistant Professor, Division of Health Informatics, Department of Public Health Sciences, UC Davis School of Medicine, University of California, Davis, USA, Division of Community Health Nursing, Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.

Division of Community Health Nursing, Ramathibodi School of Nursing,  Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.

出版信息

BMC Nurs. 2024 Aug 1;23(1):529. doi: 10.1186/s12912-024-02170-x.

Abstract

BACKGROUND

In recent years, increased attention has been given to using deep learning (DL) of artificial intelligence (AI) in healthcare to address nursing challenges. The adoption of new technologies in nursing needs to be improved, and AI in nursing is still in its early stages. However, the current literature needs more clarity, which affects clinical practice, research, and theory development. This study aimed to clarify the meaning of deep learning and identify the defining attributes of artificial intelligence within nursing.

METHODS

We conducted a concept analysis of the deep learning of AI in nursing care using Walker and Avant's 8-step approach. Our search strategy employed Boolean techniques and MeSH terms across databases, including BMC, CINAHL, ClinicalKey for Nursing, Embase, Ovid, Scopus, SpringerLink and Spinger Nature, ProQuest, PubMed, and Web of Science. By focusing on relevant keywords in titles and abstracts from articles published between 2018 and 2024, we initially found 571 sources.

RESULTS

Thirty-seven articles that met the inclusion criteria were analyzed in this study. The attributes of evidence included four themes: focus and immersion, coding and understanding, arranging layers and algorithms, and implementing within the process of use cases to modify recommendations. Antecedents, unclear systems and communication, insufficient data management knowledge and support, and compound challenges can lead to suffering and risky caregiving tasks. Applying deep learning techniques enables nurses to simulate scenarios, predict outcomes, and plan care more precisely. Embracing deep learning equipment allows nurses to make better decisions. It empowers them with enhanced knowledge while ensuring adequate support and resources essential for caregiver and patient well-being. Access to necessary equipment is vital for high-quality home healthcare.

CONCLUSION

This study provides a clearer understanding of the use of deep learning in nursing and its implications for nursing practice. Future research should focus on exploring the impact of deep learning on healthcare operations management through quantitative and qualitative studies. Additionally, developing a framework to guide the integration of deep learning into nursing practice is recommended to facilitate its adoption and implementation.

摘要

背景

近年来,人工智能(AI)的深度学习(DL)在医疗保健领域用于应对护理挑战受到了更多关注。护理领域新技术的采用情况有待改善,护理中的人工智能仍处于早期阶段。然而,当前的文献需要更加清晰明确,这影响了临床实践、研究和理论发展。本研究旨在阐明深度学习的含义,并确定护理领域中人工智能的定义属性。

方法

我们采用沃克和阿凡特的八步分析法对护理中人工智能的深度学习进行了概念分析。我们的检索策略运用布尔技术和医学主题词在多个数据库中进行检索,包括BMC、CINAHL、护理临床关键信息库、Embase、Ovid、Scopus、SpringerLink和施普林格自然、ProQuest、PubMed以及科学引文索引。通过关注2018年至2024年间发表文章的标题和摘要中的相关关键词,我们最初找到了571个来源。

结果

本研究分析了37篇符合纳入标准的文章。证据的属性包括四个主题:专注与沉浸、编码与理解、排列层与算法,以及在用例过程中实施以修改建议。先行因素、不明确的系统与沟通、数据管理知识和支持不足以及复合挑战可能导致痛苦和有风险的护理任务。应用深度学习技术使护士能够模拟场景、预测结果并更精确地规划护理。使用深度学习设备使护士能够做出更好的决策。它使护士具备更丰富的知识,同时确保为护理人员和患者的福祉提供必要的支持和资源。获得必要的设备对于高质量的家庭医疗保健至关重要。

结论

本研究使人们对深度学习在护理中的应用及其对护理实践的影响有了更清晰的认识。未来的研究应侧重于通过定量和定性研究探索深度学习对医疗保健运营管理的影响。此外,建议制定一个框架来指导深度学习融入护理实践,以促进其采用和实施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08bd/11295627/cbc9e81e3196/12912_2024_2170_Fig1_HTML.jpg

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