整合预测分析在社区 overdose 预防中的伦理挑战和机会
Bennett Allen1, Adelya Urmanche2, Brenda Curtis3
1Department of Population Health, NYU Grossman School of Medicine, New York, NY, United States.
预测分析可以改善过量预防,但必须解决道德挑战. 负责任的实施需要社区的投入,以确保公平和对公共卫生工具的信任.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 生物伦理学生物伦理学
背景情况:
- 预测分析和机器学习工具在公共卫生中越来越多地用于打击过量疫情.
- 基于社区的物质使用提供者正在采用这些工具来指导过量预防服务.
- 这些工具的整合在社区环境中带来了重大的伦理和实际挑战.
研究的目的:
- 检查使用预测分析在社区过量预防中的伦理和实际影响.
- 通过公共卫生伦理镜头分析这些工具的应用,重点关注正义,透明度,参与和准备.
- 为开发人员确定关键的伦理考虑,为服务提供商确定实际挑战.
主要方法:
- 对过量预防中的预测分析的观点分析.
- 应用公共卫生伦理原则 (分配正义,透明度,社区参与,实施准备).
- 识别伦理考虑和实际挑战.
主要成果:
- 开发商的五个主要道德考虑:机构责任,过度简化,数据/算法偏见,社区流离失所和股权权交易.
- 服务提供商在实施这些工具时面临相应的实际挑战.
- 为负责任和公平驱动的实施提出五项建议.
结论:
- 伦理和参与性框架对于负责任地实施过量预防中的预测分析至关重要.
- 这些框架至关重要,以确保数据驱动工具增强社区信任和健康公平,而不是破坏它们.
- 开发商,公共卫生当局和前线组织之间的合作至关重要.
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