利用种族背景来改善种族,民族和语言数据的收集
概括
丹佛健康REaL计划改善了种族,种族和语言 (REaL) 数据的收集,大大减少了缺失的信息. 该倡议通过确保准确的患者人口统计数据来提高医疗保健公平性.
科学领域:
- 医疗信息学 医疗信息学
- 健康 公平 研究 健康 公平 研究
- 医疗保健中的数据管理
背景情况:
- 不准确的种族,种族和语言 (REaL) 数据可能会加剧医疗保健差异.
- 解决数据收集障碍对于改善健康公平至关重要.
- 丹佛健康 (DH) 系统发现需要加强其REaL数据收集流程.
研究的目的:
- 实施和评估丹佛健康 (DH) 种族,种族和语言 (REaL) 倡议的有效性.
- 在一个大型安全网健康系统中,提高REaL数据的质量和完整性.
- 为了减少患者种族,种族和首选语言的缺失数据点.
主要方法:
- 开发并实施了标准的数据收集脚本和培训.
- 电子健康记录被调整为收集REaL数据,从种族背景开始.
- 在实施该计划之前和之后,对207,490名患者的数据完整性进行了分析.
主要成果:
- 种族缺失值从7.9%下降到0.5% (p < .001). 这是一个非常明显的变化.
- 种族缺失值从7.6%下降到0.3% (p < .001). 缺失值从7.6%下降到0.3% (p < .001). 缺失值从7.6%下降到0.3% (p < .
- 语言数据的完整性从3%提高到1.6% (p < .001),实现了99%以上的自我识别数据.
结论:
- 通过DH REaL计划,显著减少了缺失的种族,种族和语言数据.
- 利用种族背景作为最初的数据点是倡议成功的关键.
- 改进的REaL数据收集支持减少医疗保健差异的努力.
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