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分析患者提供的回复,以改进健康公平数据元素的收集。

Analyzing Patient-Provided Responses to Improve Collection of Health Equity Data Elements.

机构信息

Steele Institute for Health Innovation, Geisinger, Danville, Pennsylvania.

Community Medicine, Geisinger, Danville, Pennsylvania.

出版信息

AMIA Annu Symp Proc. 2024 Jan 11;2023:569-578. eCollection 2023.

Abstract

Self-report is purported to be the gold standard for collecting demographic information. Many entry forms include a free-text "write-in" option in addition to structured responses. Balancing the flexibility of free-text with the value of collecting data in a structured format is a challenge if the data are to be useful for measuring and mitigating health disparities. While much work has been done to improve collection of race and ethnicity information, how to best collect data related to sexual and gender minority status and military veteran status has been less commonly studied. We analyzed 3,381 patient-provided free-text responses collected via a patient portal for gender identity, sexual orientation, pronouns, and veteran experiences. We identified common responses to better understand our patient population and help improve future iterations of data collection tools.

摘要

自陈报告据称是收集人口统计学信息的黄金标准。许多登记表除了结构化回答外,还包括一个自由文本“写入”选项。如果要通过衡量和减轻健康差距来利用数据,则需要在自由文本的灵活性和以结构化格式收集数据的价值之间取得平衡。虽然已经做了很多工作来改进种族和族裔信息的收集,但如何最好地收集与性和性别少数群体状况以及退伍军人状况相关的数据,研究得还比较少。我们分析了通过患者门户收集的 3381 名患者提供的关于性别认同、性取向、代词和退伍军人经历的自由文本回复。我们确定了常见的回复,以更好地了解我们的患者群体,并帮助改进未来的数据收集工具的迭代。

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