Park Kuenyoul, Kim Min-Sun, Oh YeJin, Rim John Hoon, Yu Shinae, Ryu Hyejin, Cho Eun-Jung, Lee Kyunghoon, Kim Ha Nui, Chun Inha, Kwon AeKyung, Kim Sollip, Chung Jae-Woo, Chae Hyojin, Oh Ji Seon, Park Hyung-Doo, Kang Mira, Yun Yeo-Min, Lim Jong-Baeck, Lee Young Kyung, Chun Sail
Department of Laboratory Medicine, Sanggye Paik Hospital, College of Medicine, Inje University, Seoul, Korea.
Department of Laboratory Medicine, Soonchunhyang University Cheonan Hospital, Soonchunhyang University College of Medicine, Cheonan, Korea.
J Korean Med Sci. 2025 Jan 6;40(1):e4. doi: 10.3346/jkms.2025.40.e4.
The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings.
We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes.
A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests. Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%).
This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.
据报道，逻辑观察标识符名称和代码（LOINC）映射的准确性较低，并且韩国用于研究目的的LOINC代码尚未经过准确性或可用性验证。我们的研究旨在评估在实际患者护理环境中使用现有LOINC映射时互操作性的差异和相似性。
我们从七家大学医院收集了本地测试代码及其相应LOINC映射的数据。我们的分析集中在经常要求的实验室测试上，不包括临床微生物学和分子测试。全国能力验证测试的代码用作比较的中间基准。一个由临床病理学家和术语专家组成的研究团队利用LOINC手册就确定最合适的LOINC代码达成共识。
总共为162项常见测试指定了235个LOINC代码作为最佳代码。其中，51个测试项目，包括34个尿液测试，需要多个最佳LOINC代码，主要是由于未注明的属性，如测试是定量还是定性，或测量单位的差异。我们分析了与七家机构的162项测试相关的962个LOINC代码，发现其中792个（82.3%）代码是一致的。不一致最常见于分析物成分（38处不一致，33.3%），其次是方法（33处不一致，28.9%）和属性（13处不一致，11.4%）。
本研究揭示了大学医院用于研究目的的LOINC映射中超过15%的显著不一致率，强调了专家验证对于提高实际患者护理中互操作性的必要性。
