Evidence map›Paper›PMID 34573905›Full record

ArticleDiagnostics (Basel, Switzerland)2021

Logical Observation Identifiers Names and Codes (LOINC

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

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. The Immunopeptidomics Ontology (ImPO).Database : the journal of biological databases and curation · 2024
    Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Chih-Yang YehGraduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan.
Syu-Jyun PengProfessional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan.ORCID 0000-0001-5002-6581
Hsuan Chia YangGraduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan.
Mohaimenul IslamGraduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan.ORCID 0000-0001-6026-2748
Tahmina Nasrin PolyGraduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan.
Chien-Yeh HsuDepartment of Information Management, National Taipei University of Nursing and Health Science, Taipei 11219, Taiwan.
Stanley M HuffDepartment of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT 84132, USA.ORCID 0000-0001-8595-2665
Huan-Chieh ChenDepartment of Neurosurgery, Taipei Medical University-Wan Fang Hospital, Taipei 116, Taiwan.
Ming-Chin LinGraduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan.ORCID 0000-0001-9624-9705

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveLogical 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 domain 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).

methodsComplete mapping codes for 20 pathogens (nine bacteria and 11 viruses) were requested from all participating hospitals to review between January 2014 and December 2016. Participating hospitals mapped those pathogens to LOINC terminology, utilizing the Regenstrief 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 questionnaires (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 pattern, a request was sent to the LOINC committee for resolution.

resultsA 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.

conclusionsThe findings of our study show that multiple stage approaches improved LOINC mapping by up to 26.5%.

Indexed as

automated mappingelectronic health recordLOINC laboratory testRELMA

Identifiers

PMID34573905
PMCPMC8464801

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.