自动化临床试验队列定义和评估使用CQL和CDS-Hooks
Anja Schwab1, Anna M Lin1, Stephan Winkler1
1Department of Medical and Bioinformatics, University of Applied Sciences Upper Austria, Hagenberg, Austria.
Studies in health technology and informatics
|April 29, 2024
概括
使用电子健康记录 (EHR) 和临床决策支持 (CDS) 标准自动化临床试验患者招募,简化了受试者识别. 这种方法提高了效率,并降低了与研究研究手动匹配患者相关的成本.
科学领域:
- 临床信息学 临床信息学
- 健康数据标准 卫生数据标准
- 临床试验管理 临床试验管理
背景情况:
- 为临床试验招募患者是一个重大挑战.
- 目前的方法是昂贵和耗时的,需要广泛的病史审查和手动对象匹配.
研究的目的:
- 设计和实施用于临床试验招聘的自动化架构.
- 使用电子健康记录 (EHR) 和临床决策支持 (CDS) 标准进行域独立自动化.
- 将试验标准整合到临床医生的工作流程中,以便在不增加工作量的情况下自动评估患者.
主要方法:
- 将队列标准翻译成临床质量语言 (CQL).
- 将CQL集成到措施和CDS-Hooks中,用于患者和人口层面的评估.
- 将简化现实世界试验标准应用于快速医疗互操作资源 (FHIR®) 测试数据.
主要成果:
- 获得个体患者的资格和试验细节的证明可行性.
- 成功获取了人口资格统计数据和符合条件的患者列表.
- 使用FHIR®测试数据验证了自动化方法.
结论:
- 通过使用CDS标准自动化队列定义和评估,有望简化患者选择.
- 这种方法符合对标准化医疗保健数据的立法要求.
- 该方法为传统的患者招募策略提供了一种更有效和潜在的更便宜的替代方案.
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