J Korean Med Sci.  2025 Jan;40(1):e4. 10.3346/jkms.2025.40.e4.

Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care

• Park K
• Kim MS
• Oh Y
• Rim JH
• Yu S
• Ryu H
• Cho EJ
• Lee K
• Kim HN
• Chun I
• Kwon A
• Kim S
• Chung JW
• Chae H
• Oh JS
• Park HD
• Kang M
• Yun YM
• Lim JB
• Lee YK
• Chun S

• KMID: 2564204
• DOI: http://doi.org/10.3346/jkms.2025.40.e4

Abstract

Background
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.
Methods
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.
Results
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%).
Conclusion
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.

Keyword

Common Data Model; LOINC; Harmonization; Interoperability; Standardization; Terminology

Figure

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  Fig. 1 Flowchart outlining the process for selecting test items and determining the optimal and suboptimal codes.PT = proficiency testing, LOINC = Logical Observation Identifiers Names and Codes.

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  Fig. 2 Distribution of the LOINC parts with inconsistent LOINC codes (N = 114). Other denotes inconsistencies identified in other than six LOINC parts.LOINC = Logical Observation Identifiers Names and Codes.

Reference

1. Kim S, Cho EJ, Jeong TD, Park HD, Yun YM, Lee K, et al. Proposed model for evaluating real-world laboratory results for big data research. Ann Lab Med. 2023; 43(1):104–107. PMID: 36045065.

2. Kim S. Laboratory data quality evaluation in the big data era. Ann Lab Med. 2023; 43(5):399–400. PMID: 37080739.

3. Sabutsch S, Weigl G. Using HL7 CDA and LOINC for standardized laboratory results in the Austrian electronic health record. J Lab Med. 2018; 42(6):259–266.

4. Lee S, Yu J, Cho CI, Cho EJ, Jeong TD, Kim S, et al. Impact of Academia-Government Collaboration on Laboratory Medicine Standardization in South Korea: analysis of eight years creatinine proficiency testing experience. Clin Chem Lab Med. 2023; 62(5):861–869. PMID: 37999449.

5. Kim S, Jeong TD, Lee K, Chung JW, Cho EJ, Lee S, et al. Quantitative evaluation of the real-world harmonization status of laboratory test items using external quality assessment data. Ann Lab Med. 2024; 44(6):529–536. PMID: 38919008.

6. Yoon YA, Jang MA, Lee JS, Min WK, Kwon KC, Lee YW, et al. Effect of accreditation on the accuracy of diagnostic hematologic tests: standard deviation index analysis. Ann Lab Med. 2018; 38(1):67–70. PMID: 29071823.

7. Park HA. Why terminology standards matter for data-driven artificial intelligence in healthcare. Ann Lab Med. 2024; 44(6):467–471. PMID: 38955364.

8. Carter AB, Berger AL, Schreiber R. Laboratory test names matter: a survey on what works and what doesn’t work for orders and results. Arch Pathol Lab Med. 2024; 148(2):155–167. PMID: 37134236.

9. Forrey AW, McDonald CJ, DeMoor G, Huff SM, Leavelle D, Leland D, et al. Logical observation identifier names and codes (LOINC) database: a public use set of codes and names for electronic reporting of clinical laboratory test results. Clin Chem. 1996; 42(1):81–90. PMID: 8565239.

10. Centers for Medicare and Medicaid Services (CMS). Updated 2024. Accessed July 1, 2024. https://loinc.org/category/government/centers-for-medicare-and-medicaid-services-cms/ .

11. Bietenbeck A, Ganslandt T. Shaping the digital transformation of laboratory medicine. J Lab Med. 2018; 42(6):215–217.

12. Dahlweid FM, Kämpf M, Leichtle A. Interoperability of laboratory data in Switzerland – a spotlight on Bern. J Lab Med. 2018; 42(6):251–258.

13. Yoon SY, Yoon JH, Min WK, Lim HS, Song J, Chae SL, et al. Standardization of terminology in laboratory medicine I. Korean J Lab Med. 2007; 27(2):151–155. PMID: 18094568.

14. Jung BK, Kim J, Cho CH, Kim JY, Nam MH, Shin BK, et al. Report on the project for establishment of the standardized Korean laboratory terminology database, 2015. J Korean Med Sci. 2017; 32(4):695–699. PMID: 28244299.

15. Lin MC, Vreeman DJ, McDonald CJ, Huff SM. Correctness of voluntary LOINC mapping for laboratory tests in three large institutions. AMIA Annu Symp Proc. 2010; 2010:447–451. PMID: 21347018.

16. Stram M, Seheult J, Sinard JH, Campbell WS, Carter AB, de Baca ME, et al. A survey of LOINC code selection practices among participants of the College of American Pathologists coagulation (CGL) and cardiac markers (CRT) Proficiency Testing programs. Arch Pathol Lab Med. 2020; 144(5):586–596. PMID: 31603714.

17. McDonald CJ, Baik SH, Zheng Z, Amos L, Luan X, Marsolo K, et al. Mis-mappings between a producer’s quantitative test codes and LOINC codes and an algorithm for correcting them. J Am Med Inform Assoc. 2023; 30(2):301–307. PMID: 36343113.

18. Cholan RA, Pappas G, Rehwoldt G, Sills AK, Korte ED, Appleton IK, et al. Encoding laboratory testing data: case studies of the national implementation of HHS requirements and related standards in five laboratories. J Am Med Inform Assoc. 2022; 29(8):1372–1380. PMID: 35639494.

19. Introducing the LOINC ontology: a LOINC and SNOMED CT interoperability solution. Updated 2023. Accessed July 1, 2024. https://loincsnomed.org/ .

20. McDonald CJ, Huff SM, Suico JG, Hill G, Leavelle D, Aller R, et al. LOINC, a universal standard for identifying laboratory observations: a 5-year update. Clin Chem. 2003; 49(4):624–633. PMID: 12651816.

21. LOINC Mapping Guides. Updated 2022. Accessed July 01, 2024. https://loinc.org/guides/ .

22. Royal College of Pathologists of Australasia. RCPA Standardised Pathology Informatics in Australia (SPIA) Guidelines V4.0. Surry Hills, NSW, Australia: Royal College of Pathologists of Australasia;2021.

23. LOINC order code. Updated 2017. Accessed August 23, 2024. https://oncprojectracking.healthit.gov/wiki/display/TechLabSC/LOINC%2BOrder%2BCode .

24. Recommendations for the Submission of LOINC^® Codes in Regulatory Applications to the U.S. Food and Drug Administration. Updated 2017. Accessed August 23, 2024. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/recommendations-submission-loincr-codes-regulatory-applications-us-food-and-drug-administration .

25. Kruse CS, Stein A, Thomas H, Kaur H. The use of electronic health records to support population health: a systematic review of the literature. J Med Syst. 2018; 42(11):214. PMID: 30269237.

26. D’Amore JD, Mandel JC, Kreda DA, Swain A, Koromia GA, Sundareswaran S, et al. Are meaningful use stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative. J Am Med Inform Assoc. 2014; 21(6):1060–1068. PMID: 24970839.

27. You SC, Lee S, Choi B, Park RW. Establishment of an international evidence sharing network through common data model for cardiovascular research. Korean Circ J. 2022; 52(12):853–864. PMID: 36478647.

28. Kim JE, Choi YJ, Oh SW, Kim MG, Jo SK, Cho WY, et al. The effect of statins on mortality of patients with chronic kidney disease based on data of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) and Korea National Health Insurance Claims Database. Front Nephrol. 2022; 1:821585. PMID: 37674813.

29. Son N, Kim B, Chung S, Han S. Korean pharmacovigilance system based on EHR-CDM. Stud Health Technol Inform. 2019; 264:1592–1593. PMID: 31438247.

30. Choi W, Ko SJ, Jung HJ, Kim TM, Choi I. Expansion of EHR-based Common Data Model (CDM). Stud Health Technol Inform. 2019; 264:1443–1444. PMID: 31438172.