Correctness of Voluntary LOINC Mapping for Laboratory Tests in Three Large Institutions

Ming Chin Lin, Daniel J. Vreeman, Clement J. McDonald, Stanley M. Huff

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


With IRB approval, we obtained de-identified laboratory test data from 3 large institutions (ARUP, Intermountain, and Regenstrief). In this study we evaluated correctness of mapping local laboratory result codes to Logical Observation Identifier Names and Codes (LOINC®). We received 9,027 laboratory tests mapped to 3,669 unique LOINC codes. A one tenth sample (884 tests) was manually reviewed for correctness of the mappings. After review, there were 4 tests mapped to totally unrelated LOINC codes and there were 36 tests containing at least one error in mapping to the 6 axes of LOINC. The errors of LOINC mapping could be categorized into 4 systematic errors: 1) human errors, 2) mapping to different granularity, 3) lack of knowledge of the meaning of laboratory tests and 4) lack of knowledge of LOINC naming rules. Finally, we discuss how these systematic mapping errors might be avoided in the future.

Original languageEnglish
Pages (from-to)447-451
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Publication statusPublished - 2010
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)


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