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在大型儿科卫生系统中,在种族和种族指定中的错误率.

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电子医疗记录 (EMR) 种族和种族数据的错误率很高,影响了健康公平研究. 整合数据选项提高了准确性,但仍然存在严重的错误归因,可能会破坏改善护理的努力.

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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
  • 临床数据管理 临床数据管理

背景情况:

  • 准确的种族和民族名称对于识别健康差异和改善临床护理至关重要.
  • 在电子医疗记录 (EMR) 中错误地归因种族和种族,可能会忽视现有的不平等现象或创造虚假的不平等现象.

研究的目的:

  • 确定密歇根州三个主要儿科卫生系统EMR中种族和种族归因的错误率.
  • 评估数据整合对种族和种族匹配准确性的影响.

主要方法:

  • 一项涉及4333项来自门诊,急诊和住院机构的家长/监护人调查的横截面研究.
  • 父母对孩子的种族和种族的报告被用作黄金标准,与EMR名称相比.
  • 数据匹配涉及精确的比较,优先考虑少数群体,并将类别缩小到更少的选择.

主要成果:

  • 父母种族报告与EMR的准确匹配显示出高错误率 (41%-78%).
  • 巩固种族选项改善了匹配率,缩小了卫生系统之间的差异 (79%-88%).
  • 种族匹配率在65%至95%之间,缺少的数据也导致了不匹配.

结论:

  • 在EMR种族和种族数据中存在重大错误,可能会破坏解决健康差异的努力.
  • 数据类别的复杂性可能导致错误的归因,突出需要标准化和验证的数据收集.
  • 需要进一步调查,以了解数据类别设计和差异分析之间的脱节.