分析患者提供的反应,以改善健康公平数据元素的收集
Jennifer Prey Dawson1, Heather Finn1, Aliasgar Z Chittalia2
1Steele Institute for Health Innovation, Geisinger, Danville, Pennsylvania.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
通过自由文本输入收集患者人口统计数据可能具有挑战性. 这项研究分析了患者门户网站的响应,以改善对性和性别少数群体地位和退伍军人经验的数据收集.
科学领域:
- 医疗信息学 医疗信息学
- 人口统计数据收集 人口统计数据收集
- 健康差距 研究 研究 研究 研究
背景情况:
- 自我报告的人口统计数据对于健康差异研究至关重要.
- 标准化数据收集方法往往缺乏对微妙身份的灵活性.
- 收集有关性和性别少数群体 (SGM) 状态和退伍军人经验的数据需要改进的策略.
研究的目的:
- 分析患者对人口统计数据的自由文本反应.
- 确定患者提供的关于SGM状态和退伍军人经验的信息中的共同主题.
- 为改善患者数据收集工具的改进提供信息,以便更好地衡量健康公平.
主要方法:
- 分析了3,381个来自患者门户的自由文本回复.
- 专注于与性别认同,性取向,代词和退伍军人经历相关的数据.
- 常见的患者提供的术语和概念的定性识别.
主要成果:
- 确定患者使用SGM和退伍军人身份的流行术语和短语.
- 突出了患者自我认同的多样性和特异性.
- 证明了自由文本数据在了解患者群体中的价值.
结论:
- 自由文本回复提供了超越结构化领域的患者人口统计学有价值的见解.
- 对SGM和退伍军人群体需要改进数据收集方法.
- 了解患者提供的数据可以增强健康差异研究和干预措施.
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