Delvaux Nicolas, Vaes Bert, Aertgeerts Bert, Van de Velde Stijn, Vander Stichele Robert, Nyberg Peter, Vermandere Mieke
Department of Public Health and Primary Care, Katholieke Universiteit Leuven, Leuven, Belgium.
Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway.
JMIR Form Res. 2020 Oct 21;4(10):e16094. doi: 10.2196/16094.
Effective clinical decision support systems require accurate translation of practice recommendations into machine-readable artifacts; developing code sets that represent clinical concepts are an important step in this process. Many clinical coding systems are currently used in electronic health records, and it is unclear whether all of these systems are capable of efficiently representing the clinical concepts required in executing clinical decision support systems.
The aim of this study was to evaluate which clinical coding systems are capable of efficiently representing clinical concepts that are necessary for translating artifacts into executable code for clinical decision support systems.
Two methods were used to evaluate a set of clinical coding systems. In a theoretical approach, we extracted all the clinical concepts from 3 preventive care recommendations and constructed a series of code sets containing codes from a single clinical coding system. In a practical approach using data from a real-world setting, we studied the content of 1890 code sets used in an internationally available clinical decision support system and compared the usage of various clinical coding systems.
SNOMED CT and ICD-10 (International Classification of Diseases, Tenth Revision) proved to be the most accurate clinical coding systems for most concepts in our theoretical evaluation. In our practical evaluation, we found that International Classification of Diseases (Tenth Revision) was most often used to construct code sets. Some coding systems were very accurate in representing specific types of clinical concepts, for example, LOINC (Logical Observation Identifiers Names and Codes) for investigation results and ATC (Anatomical Therapeutic Chemical Classification) for drugs.
No single coding system seems to fulfill all the needs for representing clinical concepts for clinical decision support systems. Comprehensiveness of the coding systems seems to be offset by complexity and forms a barrier to usability for code set construction. Clinical vocabularies mapped to multiple clinical coding systems could facilitate clinical code set construction.
有效的临床决策支持系统需要将实践建议准确地转化为机器可读的工件；开发代表临床概念的代码集是这一过程中的重要一步。目前电子健康记录中使用了许多临床编码系统，尚不清楚所有这些系统是否都能够有效地表示执行临床决策支持系统所需的临床概念。
本研究的目的是评估哪些临床编码系统能够有效地表示将工件转化为临床决策支持系统可执行代码所需的临床概念。
使用两种方法评估一组临床编码系统。在理论方法中，我们从3项预防保健建议中提取了所有临床概念，并构建了一系列包含来自单个临床编码系统代码的代码集。在使用真实环境数据的实际方法中，我们研究了一个国际可用临床决策支持系统中使用的1890个代码集的内容，并比较了各种临床编码系统的使用情况。
在我们的理论评估中，SNOMED CT和ICD-10（国际疾病分类第十版）被证明是大多数概念最准确的临床编码系统。在我们的实际评估中，我们发现国际疾病分类（第十版）最常被用于构建代码集。一些编码系统在表示特定类型的临床概念方面非常准确，例如，用于检查结果的LOINC（逻辑观察标识符名称和代码）和用于药物的ATC（解剖治疗化学分类）。
似乎没有一个单一的编码系统能够满足临床决策支持系统表示临床概念的所有需求。编码系统的全面性似乎被复杂性所抵消，并成为代码集构建可用性的障碍。映射到多个临床编码系统的临床词汇表可以促进临床代码集的构建。
