在线数据库的临床算法与种族和种族
Shyam Visweswaran1,2, Eugene M Sadhu3, Michele M Morris3
1Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, PA, USA. shv3@pitt.edu.
Scientific reports
|March 29, 2025
概括
临床算法经常使用种族和种族,可能会加剧健康差异. 这项研究确定了使用这些因素的众多工具,突出了需要提高认识和消除不适当的基于种族的医学实践的需要.
科学领域:
- 医疗信息学 医疗信息学
- 健康差异 在健康上的差异
- 临床决策支持 临床决策支持
背景情况:
- 临床算法越来越多地纳入了患者的种族和种族.
- 使用这些人口因素可以延续健康不平等和基于种族的医学.
- 意识和对这些算法的批判性评估是必不可少的.
研究的目的:
- 识别和编目利用种族和种族作为输入变量的临床算法.
- 提高人们对这些算法的普及率和潜在影响的认识.
- 追踪消除在临床决策中不适当使用种族和民族的进展.
主要方法:
- 主要是数据库分析,以确定相关的临床算法.
- 对风险计算器,实验室测试,治疗建议,药物和医疗器械进行系统审查.
- 根据种族和民族的使用,收集和分类信息.
主要成果:
- 确定了42个使用种族/种族作为预测因素的风险计算器.
- 发现了5个基于种族/种族的参考范围的实验室测试.
- 记录了1个治疗建议,15种药物和5种医疗设备,考虑了不同的种族/种族因素.
- 将信息编制成一个公开可访问的数据库.
结论:
- 种族和种族被纳入了大量的临床工具.
- 不适当地使用这些因素可能会加剧健康差异.
- 建立了一个集中资源,以促进透明度,推动向公平医疗保健的转变.
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