Agrawal Ankur, Perl Yehoshua, Elhanan Gai
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
Stud Health Technol Inform. 2013;192:773-7.
SNOMED CT (SCT) has been endorsed as a premier clinical terminology by many organizations with a perceived use within electronic health records and clinical information systems. However, there are indications that, at the moment, SCT is not optimally structured for its intended use by healthcare practitioners. A study is conducted to investigate the extent of inconsistencies among the concepts in SCT. A group auditing technique to improve the quality of SCT is introduced that can help identify problematic concepts with a high probability. Positional similarity sets are defined, which are groups of concepts that are lexically similar and the position of the differing word in the fully specified name of the concepts of a set that correspond to each other. A manual auditing of a sample of such sets found 38% of the sets exhibiting one or more inconsistent concepts. Group auditing techniques such as this can thus be very helpful to assure the quality of SCT, which will help expedite its adoption as a reference terminology for clinical purposes.
SNOMED CT（SCT）已被许多组织认可为首要的临床术语集，在电子健康记录和临床信息系统中具有预期用途。然而，有迹象表明，目前SCT的结构对于医疗从业者的预期用途而言并非最佳。开展了一项研究以调查SCT中概念之间的不一致程度。引入了一种用于提高SCT质量的分组审核技术，该技术有助于以高概率识别有问题的概念。定义了位置相似性集，即词汇相似的概念组，以及一组概念的完全指定名称中相互对应的不同单词的位置。对这类集合的一个样本进行人工审核发现，38%的集合存在一个或多个不一致的概念。因此，诸如此类的分组审核技术对于确保SCT的质量非常有帮助，这将有助于加快其作为临床用途参考术语集的采用。
