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使用数据流图来支持研究知情同意数据管理通讯:参与者的观点
Brian J McInnis1, Ramona Pindus2, Daniah H Kareem2
1School of Information, University of Texas Austin, Austin, TX 78712, United States.
Journal of the American Medical Informatics Association : JAMIA
|February 4, 2025
概括
数据流图 (DFD) 通过提高对数据管理的理解,可以增强对数字健康研究的知情同意. 虽然DFD不是文本的替代品,但它有助于澄清风险,并鼓励参与者提出问题.
科学领域:
- 数字健康数字健康
- 在知情同意的情况下同意.
- 数据管理数据管理
背景情况:
- 数字健康研究产生大量的个人健康数据,使知情同意过程复杂化.
- 有效地向参与者传达数据管理实践和相关风险是一个重大挑战.
研究的目的:
- 探索数据流图 (DFDs) 的实用性,以补充对数字健康研究的知情同意.
- 评估DFD是否可以提高参与者对数据管理和相关风险的理解.
主要方法:
- 进行了8个半结构化的焦点小组,共有34名参与者.
- 探索了参与者对DFDs的看法,以传达数据管理和共享实践.
主要成果:
- DFD可以通过澄清数据管理和共享来补充基于文本的知情同意,并促使参与者提出问题.
- 参与者强调需要明确,简单,以参与者为中心的DFD,特别是在第三方数据访问等高风险领域.
- DFDs补充,但不能取代传统的知情同意文件.
结论:
- 潜在的参与者重视各种方法,包括像DFD这样的可视化,以了解研究风险和收益.
- 可视化同意信息可以帮助克服参与研究的数字和数据素养障碍.
- 开发DFD需要大量的努力;需要共享的基础设施和激励来支持其创建.
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