减轻医疗保健算法中的种族偏见:改善获得支持性住房的公平性
Krista R Noam1, Timothy Schmutte1, Christopher Bory1
1Carelon Behavioral Health, Rocky Hill, Connecticut (Noam); Department of Psychiatry, School of Medicine, Yale University, New Haven (Schmutte); Mathematica, Princeton, New Jersey (Bory); private practice, Middlefield, Connecticut (Plant).
Psychiatric services (Washington, D.C.)
|June 28, 2024
概括
研究人员在医疗保健算法中减少了种族和种族偏见. 精细的算法公平地识别了有资格的医疗补助受益人,以获得协调护理和住房支持.
科学领域:
- 卫生公平性健康公平性
- 算法的公平性算法公平性
- 医疗保健差异 医疗保健的差异
背景情况:
- 医疗保健决策算法面临种族和种族偏见的审查.
- 识别弱势群体,如那些经历无家可归和慢性健康需求的人,对于有针对性的干预至关重要.
- 国家医疗补助计划的受益者通常需要协调的医疗保健和住房支持.
研究的目的:
- 在用于识别州医疗补助受益人的算法中减少种族和种族偏见.
- 确保公平地选择有资格获得协调医疗保健和住房支持的个人.
- 开发一个公平有效的工具,用于公共卫生资源分配.
主要方法:
- 使用了一种多步骤的过程,涉及3465个人的数据.
- 采用了一种代方法来调整算法输入.
- 整合了各种利益相关者审查和严格的质量保证措施.
主要成果:
- 在算法中成功减少了种族和民族偏见.
- 在选择符合条件的医疗补助受益人时实现了种族和民族平等.
- 证明了算法的有效性在识别个人协调护理和住房.
结论:
- 开发的算法促进了医疗保健决策中的公平性.
- 代改进和利益相关者参与是缓解算法偏差的关键.
- 这种方法可以改善弱势群体获得基本健康和住房服务的平等机会.
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