Logical observation identifiers names and codes (Loinc®) applied to microbiology: A national laboratory mapping experience in Taiwan

Chih Yang Yeh, Syu Jyun Peng, Hsuan Chia Yang, Mohaimenul Islam, Tahmina Nasrin Poly, Chien Yeh Hsu, Stanley M. Huff, Huan Chieh Chen, Ming Chin Lin

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Background and Objective: Logical Observation Identifiers Names and Codes (LOINC) is a universal standard for identifying laboratory tests and clinical observations. It facilitates a smooth information exchange between hospitals, locally and internationally. Although it offers immense benefits for patient care, LOINC coding is complex, resource-intensive, and requires substantial do-main expertise. Our objective was to provide training and evaluate the performance of LOINC mapping of 20 pathogens from 53 hospitals participating in the National Notifiable Disease Surveillance System (NNDSS). Methods: Complete mapping codes for 20 pathogens (nine bacteria and 11 vi-ruses) were requested from all participating hospitals to review between January 2014 and December 2016. Participating hospitals mapped those pathogens to LOINC terminology, utilizing the Re-genstrief LOINC mapping assistant (RELMA) and reported to the NNDSS, beginning in January 2014. The mapping problems were identified by expert panels that classified frequently asked ques-tionnaires (FAQs) into seven LOINC categories. Finally, proper and meaningful suggestions were provided based on the error pattern in the FAQs. A general meeting was organized if the error pattern proved to be difficult to resolve. If the experts did not conclude the local issue’s error pat-tern, a request was sent to the LOINC committee for resolution. Results: A total of 53 hospitals participated in our study. Of these, 26 (49.05%) used homegrown and 27 (50.95%) used outsourced LOINC mapping. Hospitals who participated in 2015 had a greater improvement in LOINC mapping than those of 2016 (26.5% vs. 3.9%). Most FAQs were related to notification principles (47%), LOINC system (42%), and LOINC property (26%) in 2014, 2015, and 2016, respectively. Conclusions: The findings of our study show that multiple stage approaches improved LOINC mapping by up to 26.5%.

Original languageEnglish
Article number1564
Issue number9
Publication statusPublished - Sept 2021


  • Automated mapping
  • Electronic health record
  • LOINC laboratory test

ASJC Scopus subject areas

  • Clinical Biochemistry


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