SNOMED和LOINC与多样性最小项目集有多大结合?
Michael Rusongoza Muzoora1, Nina Haffer1, Sylvia Thun1,2
1Berlin Institute of Health at Charité - Universitätsmedizin Berlin.
Studies in health technology and informatics
|August 8, 2025
概括
现有的医疗术语,如LOINC和SNOMED CT在代表患者多样性 (DiMIS) 中存在重大差距. 为了准确,公平的医疗保健数据,需要改进.
科学领域:
- 医疗信息学 医疗信息学
- 医学术语 医学术语 医学术语
- 健康 公平 卫生 公平
背景情况:
- 社会认同分配可以在患者和提供者之间造成脱节,导致偏见的医疗治疗.
- 公平和尊重的护理需要解决这些差距,并在医疗保健中超越社会规范.
- 标准化术语对于准确的患者数据至关重要,包括多样性信息.
研究的目的:
- 评估当前多样性最小项目集 (DiMIS) 多样性领域的语义术语 (LOINC,SNOMED CT) 的局限性和模糊性.
- 评估SNOMED CT和LOINC对于表示患者多样性数据的适用性.
- 识别医疗术语中的差距,以准确地捕捉不断变化的,患者定义的描述符.
主要方法:
- 使用ISO/TS 21564评分系统评估了SNOMED CT和LOINC的适用性.
- 在两个独立的映射专家之间评估了编码器间的可靠性.
- 分析了DiMIS多样性域与现有术语的映射.
主要成果:
- 显著比例的DiMIS项目无法映射到SNOMED CT (34%) 或LOINC (19%).
- 观察到代码间可靠性较低,概念等价性有限 (LOINC为30%,SNOMED CT为36%).
- 迪米斯本身包含含糊不清的术语,突出了对标准化术语的需求.
结论:
- 像SNOMED CT和LOINC这样的现有术语在代表不同患者身份方面存在关键差距.
- 迪米斯的模糊性强调了医疗保健数据中需要清晰,准确的术语.
- 医疗术语的更新和增强对于支持不断变化的,患者定义的多样性描述器至关重要.
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