Evidence map›Paper›PMID 20725694›Full record

ArticleMethods of information in medicine2011

A characterization of local LOINC mapping for laboratory tests in three large institutions.

M C Lin, D J Vreeman, C J McDonald, S M Huff

Open access · greenAbstract read
In one paragraph

Article in Methods of information in medicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
2.2field-weighted citation impact, top 13% of its field
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 43 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Logical Observation Identifiers Names and Codes (LOINCDiagnostics (Basel, Switzerland) · 2021
    Article
  7. Unit conversions between LOINC codes.Journal of the American Medical Informatics Association : JAMIA · 2018
    Article
  8. Article
  9. Consolidating CCDs from multiple data sources: a modular approach.Journal of the American Medical Informatics Association : JAMIA · 2016
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. A comparison of phenotype definitions for diabetes mellitus.Journal of the American Medical Informatics Association : JAMIA · 2013
    Article
  15. Article
  16. Article
  17. Article
  18. Enabling international adoption of LOINC through translation.Journal of biomedical informatics · 2012
    Article
  19. Article
  20. Standardizing clinical laboratory data for secondary use.Journal of biomedical informatics · 2012
    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

4 authors at 3 institutions in 1 country.

M C LinThe Department of Biomedical Informatics, The University of Utah, Salt Lake City, UT, USA.
D J Vreeman
C J McDonald
S M Huff
National Institutes of Health · USRegenstrief Institute · USUniversity of Utah · US

Funding

National Biomedical Information ServicesZIHLM200888 · NLM · NATIONAL LIBRARY OF MEDICINE · PI PALTOO, DINA · 2009 to 2025
$5200.7M
NLM's Personal Health Record and Consumer Service R&DZIALM008925 · NLM · NATIONAL LIBRARY OF MEDICINE · PI MCDONALD, CLEMENT · 2009 to 2020
$11.8M
Intramural NIH HHS Z99 LM999999
6 · The paper itself

Abstract

objectivesWe characterized the use of laboratory LOINC® codes in three large institutions, focused on the following questions: 1) How many local codes had been voluntarily mapped to LOINC codes by each institution? 2) Could additional mappings be found by expert manual review for any local codes that were not initially mapped to LOINC codes by the local institution? and 3) Are there any common characteristics of unmapped local codes that might explain why some local codes were not mapped to LOINC codes by the local institution?

methodsWith Institutional Review Board (IRB) approval, we obtained deidentified data from three large institutions. We calculated the percentage of local codes that have been mapped to LOINC by personnel at each of the institutions. We also analyzed a sample of unmapped local codes to determine whether any additional LOINC mappings could be made and identify common characteristics that might explain why some local codes did not have mappings.

resultsConcept type coverage and concept token coverage (volume of instance data covered) of local codes mapped to LOINC codes were 0.44/0.59, 0.78/0.78 and 0.79/0.88 for ARUP, Intermountain, and Regenstrief, respectively. After additional expert manual mapping, the results showed mapping rates of 0.63/0.72, 0.83/0.80 and 0.88/0.90, respectively. After excluding local codes which were not useful for inter-institutional data exchange, the mapping rates became 0.73/0.79, 0.90/0.99 and 0.93/0.997, respectively.

conclusionsLocal codes for two institutions could be mapped to LOINC codes with 99% or better concept token coverage, but mapping for a third institution (a reference laboratory) only achieved 79% concept token coverage. Our research supports the conclusions of others that not all local codes should be assigned LOINC codes. There should also be public discussions to develop more precise rules for when LOINC codes should be assigned.

Indexed as

Health FacilitiesLogical Observation Identifiers Names and CodesClinical Laboratory TechniquesElectronic Health RecordsHealth Level SevenMedical AuditUnited States

Identifiers

PMID20725694
PMCPMC3034110
OpenAlexW2035955358

What OpenQuestion holds

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Registered trials

None linked

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.