临床试验的多平台分析集成招聘:数字健康信息技术实施框架
Melissa L Kramer1, Aileen K Ho2, Emil Lamprecht3
1University of Reading, School of Psychology and Clinical Language Sciences, University of Reading, Harry Pitt Building, Earley Gate, Reading RG6 7BE, United Kingdom; Live UTI Free Ltd, Suite 7, The Courtyard, Carmanhall Rd, Sandyford, Dublin D18NW62, Ireland.
International journal of medical informatics
|November 15, 2025
概括
一个新的信息学框架可以通过使用多道数字活动和分析来改善临床试验的招聘. 这种方法显著超过了点击率的行业基准,提高了患者招募策略.
科学领域:
- 数字健康信息学 数字健康信息学
- 临床试验的管理管理.
- 医疗信息学 医疗信息学
背景情况:
- 临床试验招聘面临重大挑战,由于招生率低,终止率高.
- 使用医疗信息学的标准化数字招聘框架尚不发达.
- 推伙伴关系和多平台分析提供解决方案,但缺乏系统的实施.
研究的目的:
- 展示实施和测量临床试验多道数字招聘活动的结构化方法.
- 整合分析平台和推伙伴关系,以优化临床试验外展.
主要方法:
- 进行了为期六个月的多道数字招聘活动,用于两项III期临床试验 (2022年5月至10月).
- 活动利用了七个道,包括大规模电子邮件,网站公告,浏览器通知,Instagram和电子邮件自动化.
- 一个集成的分析平台跟踪了使用直接和间接道架构实时优化的性能.
主要成果:
- 综合分析框架在七个道上跟踪了4829次点击,实现了2.79%的点击率 (CTR).
- 这个CTR大大超过了临床试验横幅广告基准 (0.1-0.3%) 和医疗保健Facebook广告标准 (0.83%).
- 网站公告 (52.54%) 和大规模电子邮件 (28.00%) 产生了最高的点击量.
结论:
- 介绍了一个可复制的信息框架,用于分析驱动的数字临床试验招聘活动.
- 该方法表明如何整合分析和推合作伙伴以优化外展.
- 这种方法实现了远远高于临床试验招聘行业基准的表现.
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