实施者在卫生服务中常规使用人工智能的观点:使用实施研究综合框架 (CFIR) 进行定性研究
Anna Janssen1, Kavisha Shah1, Helena Teede2,3
1Faculty of Medicine and Health, The University of Sydney, NSW, Australia.
Health services insights
|December 2, 2025
概括
在澳大利亚医疗保健中实施人工智能 (AI) 面临重大障碍. 关键的挑战包括利益相关者参与,数据访问,技术问题,资源限制和长时间的实施时间表,阻碍广泛采用.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 实施科学 实施科学
背景情况:
- 人工智能 (AI) 对医疗保健应用越来越感兴趣.
- 人工智能创新在常规临床实践实施中面临挑战.
- 了解影响澳大利亚医疗保健组织AI实施的因素至关重要.
研究的目的:
- 探索影响人工智能创新在日常实践中的实施因素.
- 从澳大利亚医疗保健中人工智能实施者的角度获得见解.
主要方法:
- 使用深入研究采访的定性方法.
- 通过环境扫描和雪球测试来识别参与者.
- 通过实施研究综合框架 (CFIR) 使用归纳编码和演分类进行数据分析.
主要成果:
- 在澳大利亚医疗保健中研究了11个AI创新和12个实施者.
- 实施因素涵盖了所有五个CFIR领域,重点是创新和实施过程.
- 确定的障碍包括利益相关者参与,数据访问,技术障碍,资源限制和漫长的时间表.
结论:
- 与研究和工业相比,在澳大利亚医疗保健中采用健康AI解决方案的数量有限.
- 实施过程的复杂性和长度,以及已识别的障碍,有助于造成这一差距.
- 解决这些障碍对于成功将人工智能纳入常规医疗保健实践至关重要.
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