从电子医疗记录中建立一个跨性别和非二进制患者队列
Lauren B Beach1,2, Paige Hackenberger3, Mona Ascha3
1Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
LGBT health
|December 28, 2023
概括
在电子医疗记录 (EMR) 中收集性取向,性别认同和出生时记录的性别 (SOGI) 数据可以显著改善跨性别和非二进制 (TGNB) 患者的识别. 结构化的SOGI数据捕获,结合ICD代码,对于准确的TGNB队列识别至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 患者的人口统计数据
- 健康 公平 卫生 公平
背景情况:
- 人口统计数据收集,包括性取向,性别认同和出生时记录的性别 (SOGI),通常被排除在电子医疗记录 (EMR) 系统之外.
- 准确识别跨性别和非二进制 (TGNB) 个人对于改善医疗保健结果和解决健康差异至关重要.
研究的目的:
- 评估结构化SOGI数据捕获在EMR中识别TGNB患者的有效性.
- 为了比较结构化SOGI数据的正预测值 (PPV),国际疾病分类 (ICD) 代码和TGNB队列识别的文本挖掘.
- 评论这些队列形成方法的伦理考虑.
主要方法:
- 在一个单一的机构进行了回顾性图表审查.
- 用ICD-10代码,结构化SOGI数据字段和2019年3月至2021年2月期间提交的患者的文本挖掘来分类患者的性别.
- 报告了每个方法的正预测值 (PPV).
主要成果:
- 总共有1,530,154名EMR被查询,通过多方方法识别了1685名TGNB患者.
- 鉴定TGNB患者的整体积极预测值 (PPV) 为56.8%.
- ICD-10代码显示PPV高 (99.2%),而结构化SOGI数据 (47.9%) 和文本挖掘 (62.2%) 的PPV较低,尽管结构化SOGI数据识别了大多数 (79.7%) 的TGNB患者.
结论:
- 结构化SOGI数据采集,尽管初始记录率低 (<10%),但与其他方法相结合,可显著提高TGNB患者的识别能力.
- 卫生系统应促进SOGI的患者EMR文档,以改善TGNB人群的健康和健康.
- 在实施SOGI数据收集策略时,优先考虑患者隐私至关重要.
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