减少种族和种族数据的缺失,包括首选的语言用于映射到聚合类别
Zoe Grabinski1,2, Farah Kader3, Danielle Bayer1
1Ronald O. Perelman Department of Emergency Medicine, New York University Langone Health, New York, NY 10016, United States.
Health affairs scholar
|December 22, 2025
概括
使用首选语言可以减少缺失的患者种族和种族数据. 这种归算方法有助于提高数据的完整性,以提高质量和研究努力.
科学领域:
- 医疗信息学 医疗信息学
- 患者数据管理患者数据管理
- 健康 公平 卫生 公平
背景情况:
- 准确的患者种族和种族数据对于医疗保健质量和患者安全至关重要.
- 缺少这些人口类别的数据仍然是医疗保健系统面临的重大挑战.
- 现有的数据收集方法往往导致不完整或不准确的种族和种族信息.
研究的目的:
- 调查使用首选语言作为赋予失踪患者种族和种族数据的代理人的有效性.
- 评估基于语言的归算在提高患者人口统计信息的完整性方面的潜力.
- 探索一种新的方法来解决医疗保健质量倡议的种族和种族数据差距.
主要方法:
- 追溯分析了纽约市三个急诊室 (2023年6月至2024年5月) 的数据.
- 开发和应用一个映射方案,以使用首选语言类别来归纳缺失的种族和民族.
- 基于管理和预算办公室的7个类别,评估首选语言和归算的种族/种族类别之间的一致性.
主要成果:
- 缺乏种族和种族数据的患者比例从9.7%降至8.6%,失踪率下降了11.1%.
- 基于语言的归算显示,对中东和北非患者的影响最大,变化为14.7%.
- 该研究表明,通过使用首选语言,数据完整性得到了可衡量的改善.
结论:
- 首选语言归算是减少缺失的种族和种族数据的可行策略.
- 这种方法可以成为卫生系统的宝贵工具,旨在提高数据的准确性和完整性.
- 基于语言的归算通过提供对患者群体的更全面的理解,支持提高质量和研究.
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