University of British Columbia, School of Nursing, Vancouver, BC, Canada. Electronic address: https://twitter.com/lori_block1.
University of British Columbia, School of Nursing and Centre for Health Services and Policy Research, Vancouver, BC, Canada.
Int J Med Inform. 2021 Sep;153:104539. doi: 10.1016/j.ijmedinf.2021.104539. Epub 2021 Jul 22.
Standardized clinical terminologies are increasingly used to design and support advanced information systems. In order to examine the representativeness of these terminologies for different professional groups or clinical areas, researchers may perform different methods of terminology mapping.
The purpose of this study was to evaluate the ability of four mapping methods to identify concepts related to wound care in SNOMED CT.
A class diagram of 107 concepts was developed to represent the nursing context of wound assessment, wound diagnosis, and goal of care for wound management. All concepts were mapped to SNOMED CT and identified as a direct match, a one-to-many match, or no match using four mapping methods (manual, automated, comparison, and concordance). The manual, automated and comparison methods produced candidate lists of SNOMED CT concepts, which were then used by two nursing wound care experts. The experts completed concordance mapping, which produced the final list. The SNOMED CT concepts from the manual, automated and comparison mappings were compared to the concordance mapping to generate a proportion of representation by each mapping method.
The manual, automated and comparison mappings produced partial lists of unique candidate concept matches not found in the other mapping methods. The concordance mapping produced a final list which included: 43 terms (40%) that had direct matches, 2 terms (2%) that had one-to-many matches, and 62 terms (58%) that had no matches to SNOMED CT. All mapping methods were necessary to achieve the representativeness captured in the final list.
To increase the representativeness of candidate mapping lists, multiple mapping methods and considerations may be necessary.
标准化临床术语越来越多地用于设计和支持高级信息系统。为了检查这些术语对于不同专业群体或临床领域的代表性,研究人员可能会执行不同的术语映射方法。
本研究旨在评估四种映射方法在 SNOMED CT 中识别与伤口护理相关概念的能力。
开发了一个包含 107 个概念的类图,以表示伤口评估、伤口诊断和伤口管理目标的护理背景。使用四种映射方法(手动、自动、比较和一致性)将所有概念映射到 SNOMED CT,并将其识别为直接匹配、一对多匹配或不匹配。手动、自动和比较方法生成了 SNOMED CT 概念的候选列表,然后由两名护理伤口护理专家使用。专家完成一致性映射,生成最终列表。将手动、自动和比较映射中的 SNOMED CT 概念与一致性映射进行比较,以生成每种映射方法的代表性比例。
手动、自动和比较映射生成了其他映射方法中未找到的独特候选概念匹配的部分列表。一致性映射生成了一个最终列表,其中包括:43 个术语(40%)具有直接匹配,2 个术语(2%)具有一对多匹配,62 个术语(58%)与 SNOMED CT 没有匹配。所有映射方法都需要实现最终列表中捕获的代表性。
为了提高候选映射列表的代表性,可能需要使用多种映射方法和考虑因素。