评估结果和评估信息集中种族和种族数据的准确性
Steven C Martino1, Marc N Elliott2, Ann Haas1
1RAND Corporation, Pittsburgh, Pennsylvania, USA.
Journal of the American Geriatrics Society
|March 21, 2024
概括
在医疗保险中,准确的种族和民族数据对于评估护理差异至关重要. 结果和评估信息集 (OASIS) 中观察员报告的数据不如自我报告或归算数据准确.
科学领域:
- 医疗信息学 医疗信息学
- 医疗服务研究 医疗服务研究
- 健康 公平 卫生 公平
背景情况:
- 医疗保险数据系统在种族和种族信息方面存在局限性,这阻碍了差异评估.
- 标准化的患者评估可能会提高数据的准确性,但其可靠性需要验证.
- 本研究评估了结果和评估信息集 (OASIS) 中种族和种族数据的准确性.
研究的目的:
- 评估OASIS数据集中患者层面的种族和种族数据的准确性.
- 将OASIS数据的准确性与自我报告数据和其他估计方法进行比较.
- 识别观察员报告的种族和种族数据中不准确的潜在来源.
主要方法:
- 将OASIS-D (2017-2022) 种族和族裔数据与CAHPS调查的黄金标准自我报告数据进行比较.
- 使用了304,804名医疗保险受益人的匹配样本.
- 还将OASIS数据与医疗保险的贝叶斯改进姓名和地理编码 (MBISG) 估计和CMS行政记录进行了比较.
主要成果:
- 与CMS行政数据相比,OASIS数据显示西班牙裔,亚洲裔,夏威夷原住民/太平洋岛民和白人个体的准确性更高.
- 与CMS数据相比,OASIS数据的准确性在美国印第安人/阿拉斯加土著人中略低,在黑人个人中类似.
- 与OASIS和CMS数据相比,MBISG方法在所有种族和种族类别中都表现出卓越的准确性.
结论:
- 观察者报告的健康记录中的种族和种族数据可能不如自我报告的数据或高级归算方法准确.
- 需要促进收集自我报告的种族和种族数据.
- 关于种族和种族信息来源的明确记录级数据对于提高数据质量至关重要.
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