关于在医疗保健中使用不同类型数据进行预测的公众观点
Paige Nong1, Julia Adler-Milstein2, Sharon Kardia3
1Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN 55455, United States.
Journal of the American Medical Informatics Association : JAMIA
|February 1, 2024
概括
对于使用数据进行预测模型的公众舒适度很低,特别是对于敏感信息. 对卫生系统的信任与舒适度有积极的相关性,突出了政策调整的必要性.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生政策 公共卫生政策
- 数据科学 伦理学 数据科学
背景情况:
- 预测模型在医疗保健中越来越多地使用,但公众对数据使用的看法对于其采用至关重要.
- 了解公众对各种数据类型的舒适程度对于这些技术的道德和有效实施至关重要.
研究的目的:
- 评估预测建模中使用的不同数据类型的公众舒适度.
- 确定影响公众舒适度和对数据隐私的看法的因素.
主要方法:
- 一项针对1436名美国成年人的全国调查于2021年底进行.
- 因子分析将15种数据类型分为三个类别:个人特征,与健康有关的数据和敏感数据.
- 用描述性统计和相关性分析来评估舒适度和与信任的关系.
主要成果:
- 总体而言,公众对数据用于预测的舒适度很低,大多数受访者对15种数据类型中使用13种数据类型感到不舒服.
- 舒适度水平因数据类别而异:与健康相关的数据 (平均2.45) >个人特征数据 (平均2.36) >敏感数据 (平均1.88).
- 在对卫生系统的信任和对数据用于预测的使用舒适度之间发现了显著的积极关系.
结论:
- 目前的政策并没有充分解决公众对预测建模数据使用的担忧.
- 需要将政策与公众对非临床数据类别的敏感性保持一致,以保持对医疗保健的信任.
- 关于公众观点的经验证据对于指导负责任的开发和部署预测工具至关重要.
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