Mis-mappings between a producer's quantitative test codes and LOINC codes and an algorithm for correcting them. (7th November 2022)

• Record Type: Journal Article
• Authors: McDonald, Clement J; Baik, Seo H; Zheng, Zhaonian; Amos, Liz; Luan, Xiaocheng; Marsolo, Keith; Qualls, Laura
• Is Part Of: Journal of the American Medical Informatics Association. Volume 30:Number 2(2023)
• Page Start: 301
• Page End: 307
• Publication Date: 2022-11-07
• Subjects: PCORnet -- LOINC -- local test code -- mapping errors; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac215
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objectives: To 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 Methods: We 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. Results: Of 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. Conclusions: Overall, 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.