基于PICO的数字健康干预证据的评估和分类:一个诱导性框架开发
Uwe Buddrus1, Jan-Oliver Kutza1, Johannes Thye1
1Digital Society, Research Centre for Health and Social Informatics, School of Business Management and Social Sciences, Hochschule Osnabrück-University of Applied Sciences, Osnabrück, Germany.
Frontiers in digital health
|March 6, 2026
概括
PACE4DHI框架简化了数字健康干预措施 (DHIs) 的系统审查选. 这种方法增强了证据差距映射,并支持人工智能驱动的合成,以改善决策.
科学领域:
- 医疗信息学 医疗信息学
- 证据综合 证据综合
- 数字健康数字健康
背景情况:
- 关于数字健康干预 (DHI) 的系统审查经常存在方法上的缺陷,阻碍了明确的证据解释.
- 越来越多的DHI审查需要高效的方法来进行更高层次的综合分析,如总体审查和证据差距地图.
- 抽象层面的快速,系统的证据评估对于管理日益增长的DHI研究机构至关重要.
研究的目的:
- 引入基于PICO的数字健康干预证据评估和分类 (PACE4DHI) 框架.
- 在抽象层面上对系统审查和元分析进行高效,结构化的选,以获得证据和差距映射 (EGM).
主要方法:
- 一项全面的文献搜索确定了2011年至2023年10月期间发表的关于DHI的系统审查和元分析.
- 对250项研究的专题分析为PACE4DHI框架的开发提供了信息,该框架在138篇文章上得到了验证.
- 遵循PRISMA指南,从21,161个初始结果中选了9030个标题/摘要.
主要成果:
- PACE4DHI框架包括41个问题,DHIs,设置,结果和证据水平的类别.
- 与全文审查相比,抽象级别的PICO分类和证据分类显示了不同准确度但一致的结果.
- 摘要提供了比较和结果更详细的关于问题和干预措施的细节,在预测确证据方面更准确.
结论:
- PACE4DHI框架提供了一种系统和务实的方法来访问和综合DHI证据.
- 它可以为未来的系统审查方法提供信息,优化EGM,并确定研究缺口.
- 与AI工具的整合可以促进持续的证据选,并支持可搜索的证据数据库.
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