Dublin Institute of Technology, School Elect. Eng. Systems, Dublin, Ireland.
J Biomed Inform. 2012 Jun;45(3):408-18. doi: 10.1016/j.jbi.2011.12.001. Epub 2011 Dec 17.
Clinical archetypes provide a means for health professionals to design what should be communicated as part of an Electronic Health Record (EHR). An ever-growing number of archetype definitions follow this health information modelling approach, and this international archetype resource will eventually cover a large number of clinical concepts. On the other hand, clinical terminology systems that can be referenced by archetypes also have a wide coverage over many types of health-care information. No existing work measures the clinical content coverage of archetypes using terminology systems as a metric. Archetype authors require guidance to identify under-covered clinical areas that may need to be the focus of further modelling effort according to this paradigm. This paper develops a first map of SNOMED-CT concepts covered by archetypes in a repository by creating a so-called terminological Shadow. This is achieved by mapping appropriate SNOMED-CT concepts from all nodes that contain archetype terms, finding the top two category levels of the mapped concepts in the SNOMED-CT hierarchy, and calculating the coverage of each category. A quantitative study of the results compares the coverage of different categories to identify relatively under-covered as well as well-covered areas. The results show that the coverage of the well-known National Health Service (NHS) Connecting for Health (CfH) archetype repository on all categories of SNOMED-CT is not equally balanced. Categories worth investigating emerged at different points on the coverage spectrum, including well-covered categories such as Attributes, Qualifier value, under-covered categories such as Microorganism, Kingdom animalia, and categories that are not covered at all such as Cardiovascular drug (product).
临床原型为健康专业人员提供了一种设计电子健康记录 (EHR) 中应传达内容的方法。越来越多的原型定义采用这种健康信息建模方法,这个国际原型资源最终将涵盖大量的临床概念。另一方面,原型可以参考的临床术语系统也涵盖了许多类型的医疗保健信息。目前还没有任何工作使用术语系统作为指标来衡量原型的临床内容覆盖范围。原型作者需要指导,以根据这一范例确定需要进一步建模努力的覆盖不足的临床领域。本文通过创建所谓的术语“影子”,为存储库中的原型在 SNOMED-CT 概念上绘制了第一张地图。这是通过从包含原型术语的所有节点映射适当的 SNOMED-CT 概念来实现的,在 SNOMED-CT 层次结构中找到映射概念的前两个类别级别,并计算每个类别的覆盖率。对结果的定量研究比较了不同类别的覆盖范围,以确定相对覆盖不足和覆盖良好的区域。结果表明,在所有 SNOMED-CT 类别上,广为人知的国家卫生服务 (NHS) 连接健康 (CfH) 原型存储库的覆盖范围并不均衡。在覆盖范围频谱的不同点出现了值得调查的类别,包括覆盖良好的类别,如属性、限定符值,以及覆盖不足的类别,如微生物、动物界 Kingdom 和根本不涵盖的类别,如心血管药物(产品)。