探索决策者在选择多次系统审查时所面临的挑战和策略:对医疗保健中人工智能决策支持工具的见解
Carole Lunny1,2, Sera Whitelaw3, Emma K Reid4
1Knowledge Translation Program, Li Ka Shing Knowledge Institute, UBC, Toronto, Ontario, Canada carole.lunny@ubc.ca.
BMJ open
|July 5, 2024
概括
医疗保健决策者经常面临比较多个系统性审查 (SRs) 的挑战. 人工智能 (AI) 工具可以帮助评估和选择最值得信赖的SR,改善基于证据的决策.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 医疗信息学 医疗信息学
- 基于证据的实践.
背景情况:
- 系统性审查 (SRs) 越来越普遍,这给决策者在同一主题的多个审查中进行选择带来了挑战.
- 医疗保健从业者,政策制定者和研究人员需要有效的方法来利用SR证据进行知情决策.
研究的目的:
- 了解医疗保健决策者如何使用SR.
- 探索人工智能 (AI) 工具的潜力,以协助SRs的批判性评估和选择.
主要方法:
- 开发了一项由21个问题组成的调查,并通过社交媒体和专业网络传播.
- 该调查针对医疗保健决策者,包括研究人员,从业人员,学生和政策制定者.
主要成果:
- 观察到一个低的响应率 (7.9%),684名参与者. 大多数受访者 (97.1%) 使用SR作为证据,并且经常遇到多个SR (97.9%).
- 超过一半 (50.8%) 由于时间限制,方法质量差异,结果差异或包含的研究多样性,因此很难选择最值得信赖的SR.
- 在比较SRs时,受访者优先考虑相关性,方法质量和最新性. 显著的多数 (87.0%) 表示对人工智能评估和比较工具感兴趣.
结论:
- 使用SR证据的障碍凸显了需要工具来简化选择过程的必要性.
- 人工智能工具可以提高SR相关性,搜索质量和方法的比较,促进有效的选择和医疗保健决策.
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