在专业环境中采用生成式人工智能的不断变化的认知和态度:中国高级医院领导的多中心纵向定性研究。

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Zheng Zhi, Jing Zhao, Qiang Li, Qingxia Li, Meifang Xu, Yan Zuo, Ming Wang, Jiankang Liu, Jingyi Guan, Jia Wang
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引用次数: 0

摘要

背景:生成式人工智能(GenAI)的快速发展正在改变全球卫生保健。在中国,医院正在迅速接受数字化转型。高级领导人在影响和决定在专业环境中采用基因人工智能技术方面发挥着关键作用。然而,缺乏关于他们的观念和态度以及他们如何随时间演变的证据。目的:本研究旨在调查中国医院高层领导对GenAI的看法,以及他们在专业环境中采用GenAI技术的态度,并记录他们的观点如何演变。方法:采用现象学方法进行纵向描述性定性研究。有目的地抽取河北、广东、四川三省三级医院的院长/副院长、科室主任/副院长等高层领导。在两个时间点(基线和6个月后)通过半结构化电话访谈收集数据。使用Colaizzi的方法对访谈记录进行分析,以提取与知识获取、态度转变以及对GenAI采用的不断发展的障碍和促进因素相关的主题。结果:2024年8月至2025年2月共访谈32人,其中河北11人(34.4%),四川12人(37.5%),广东9人(28.1%)。他们的角色包括8名(25%)医院主任/副主任和24名(75%)部门主任/副主任。对采访记录的分析揭示了三个主要主题。首先,随着时间的推移,参与者对GenAI的理解显著提高,因为他们接触了不同的信息来源并获得了实践经验。其次,尽管个人使用得到广泛支持,但高层领导人从最初的不情愿转变为正式的机构采用。第三,最初的障碍,如有限的技术素养和资源限制问题,随着时间的推移而减少,而新的驱动因素,包括同行影响和技术突破,如DeepSeek,出现了,促进了采用。结论:随着时间的推移,中国医院高层领导对GenAI的看法和态度发生了显著变化。通过各种信息渠道提高的知识增强了他们的理解力,并将他们的立场从谨慎的个人实验转变为正式的制度采用。障碍的减少和新出现的催化剂之间的动态相互作用,特别是同行实践和技术突破(如DeepSeek)的影响,强调了医疗保健管理变革的潜力。需要有针对性的教育活动、试点项目和强有力的政策框架来促进GenAI的整合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolving Perceptions and Attitudes to Adopting Generative AI in Professional Settings: Multicenter Longitudinal Qualitative Study of Senior Chinese Hospital Leaders.

Background: The rapid evolution of generative artificial intelligence (GenAI) is transforming health care globally. In China, hospitals are rapidly embracing digital transformation. Senior leaders are pivotal in influencing and deciding the adoption of GenAI technologies in professional settings. However, evidence on their perceptions and attitudes and how they evolve over time is lacking.

Objective: This study aims to investigate senior Chinese hospital leaders' perceptions relating to GenAI and their attitudes toward adopting GenAI technologies in professional settings and to document how their perspectives evolve.

Methods: A longitudinal, descriptive qualitative study was conducted using a phenomenological approach. Senior leaders, including hospital directors/deputies and department heads/deputies, from 3 tertiary hospitals across the Hebei, Guangdong, and Sichuan provinces were purposively sampled. Data were collected through semistructured telephone interviews at two time points (baseline and 6 months later). Interview transcriptions were analyzed using Colaizzi's method to extract themes related to knowledge acquisition, attitudinal shifts, and evolving barriers and facilitators to GenAI adoption.

Results: A total of 32 participants were interviewed in August 2024 and February 2025, including 11 (34.4%) participants from Hebei, 12 (37.5%) from Sichuan, and 9 (28.1%) from Guangdong. Their roles included 8 (25%) hospital directors/deputies and 24 (75%) department heads/deputies. The analysis of the interview transcriptions revealed three primary themes. First, participants' understanding of GenAI improved markedly over time as they engaged with diverse information sources and gained practical experience. Second, despite widespread support for personal use, senior leaders shifted from initial reluctance to formal institutional adoption. Third, initial obstacles, such as limited technical literacy and resource constraint concerns, diminished over time, while new drivers, including peer influence and technological breakthroughs such as DeepSeek, emerged to catalyze adoption.

Conclusions: Senior Chinese hospital leaders' perceptions and attitudes toward GenAI have evolved significantly over time. Improved knowledge through diverse information channels has enhanced their comprehension and shifted their stance from cautious personal experimentation toward formal institutional adoption. The dynamic interplay between diminishing obstacles and emerging catalysts, notably the influence of peer practices and technological breakthroughs such as DeepSeek, underscores the potential for transformative change in health care management. Targeted educational initiatives, pilot projects, and robust policy frameworks are needed to facilitate GenAI integration.

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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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