ArticleJournal of the American Medical Informatics Association : JAMIA2023
Mis-mappings between a producer's quantitative test codes and LOINC codes and an algorithm for correcting them.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Semantic enrichment of Pomeranian health study data using LOINC and WHO-FIC terminology mapping principles.JAMIA open · 2025Article
- Assessing the harmonization of structured electronic health record data to reference terminologies and data completeness through data provenance.Learning health systems · 2025Article
- Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care.Journal of Korean medical science · 2025Article
- Laboratory Data as a Potential Source of Bias in Healthcare Artificial Intelligence and Machine Learning Models.Annals of laboratory medicine · 2025Review
- Toward High-Quality Real-World Laboratory Data in the Era of Healthcare Big Data.Annals of laboratory medicine · 2025Review
- Knowledge Representation and Management: 2023 Highlights and the Rise of Knowledge Graph Embeddings.Yearbook of medical informatics · 2024Review
- Article
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Authors and funding
7 authors.
Funding
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Abstract
objectivesTo access the accuracy of the Logical Observation Identifiers Names and Codes (LOINC) mapping to local laboratory test codes that is crucial to data integration across time and healthcare systems. MATERIALS AND
methodsWe used software tools and manual reviews to estimate the rate of LOINC mapping errors among 179 million mapped test results from 2 DataMarts in PCORnet. We separately reported unweighted and weighted mapping error rates, overall and by parts of the LOINC term.
resultsOf included 179 537 986 mapped results for 3029 quantitative tests, 95.4% were mapped correctly implying an 4.6% mapping error rate. Error rates were less than 5% for the more common tests with at least 100 000 mapped test results. Mapping errors varied across different LOINC classes. Error rates in chemistry and hematology classes, which together accounted for 92.0% of the mapped test results, were 0.4% and 7.5%, respectively. About 50% of mapping errors were due to errors in the property part of the LOINC name. DISCUSSIONS: Mapping errors could be detected automatically through inconsistencies in (1) qualifiers of the analyte, (2) specimen type, (3) property, and (4) method. Among quantitative test results, which are the large majority of reported tests, application of automatic error detection and correction algorithm could reduce the mapping errors further.
conclusionsOverall, the mapping error rate within the PCORnet data was 4.6%. This is nontrivial but less than other published error rates of 20%-40%. Such error rate decreased substantially to 0.1% after the application of automatic detection and correction algorithm.
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