Evidence map›Paper›PMID 35639494›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2022

Encoding laboratory testing data: case studies of the national implementation of HHS requirements and related standards in five laboratories.

Raja A Cholan, Gregory Pappas, Greg Rehwoldt, Andrew K Sills, Elizabeth D Korte, I Khalil Appleton, Natalie M Scott, Wendy S Rubinstein, Sara A Brenner, Riki Merrick and 3 more

Abstract read
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Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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

10 citing papers in PubMed.

  1. Article
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  4. Review
  5. Review
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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

13 authors.

Raja A CholanDeloitte Consulting LLP, Washington, District of Columbia, USA.
Gregory PappasOffice of the National Coordinator for Health Information Technology, Washington, District of Columbia, USA.
Greg RehwoldtDeloitte Consulting LLP, Washington, District of Columbia, USA.
Andrew K SillsDeloitte Consulting LLP, Washington, District of Columbia, USA.
Elizabeth D KorteDeloitte Consulting LLP, Washington, District of Columbia, USA.
I Khalil AppletonDeloitte Consulting LLP, Washington, District of Columbia, USA.
Natalie M ScottDeloitte Consulting LLP, Washington, District of Columbia, USA.
Wendy S RubinsteinU.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Sara A BrennerU.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Riki MerrickAssociation for Public Health Laboratories, Silver Spring, Maryland, USA.
Wilbur C HaddenUniversity of Maryland, College Park, Maryland, USA.
Keith E CampbellU.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Michael S WatersU.S. Food and Drug Administration, Silver Spring, Maryland, USA.

Funding

Center for Devices and Radiological Health # 75F40119C10164Implementation of SHIELD Harmonized Laboratory Data Standards in Healthcare InstitutionsU.S. Food and Drug Administration Office of In Vitro Diagnostics and Radiological HealthU.S. Food and Drug Administration Office of In Vitro Diagnostics and Radiological Health within the Center for Devices and Radiological Health 75F40119C10164
6 · The paper itself

Abstract

objectiveAssess the effectiveness of providing Logical Observation Identifiers Names and Codes (LOINC®)-to-In Vitro Diagnostic (LIVD) coding specification, required by the United States Department of Health and Human Services for SARS-CoV-2 reporting, in medical center laboratories and utilize findings to inform future United States Food and Drug Administration policy on the use of real-world evidence in regulatory decisions. MATERIALS AND

methodsWe compared gaps and similarities between diagnostic test manufacturers' recommended LOINC® codes and the LOINC® codes used in medical center laboratories for the same tests.

resultsFive medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (chi-square 45.0, 4 df, P < .0001). DISCUSSION: The five medical centers and three test manufacturers vary in how they organize, categorize, and store LIS catalog information. This variation impacts data quality and interoperability.

conclusionThe results of the study indicate that providing the LIVD mappings was not sufficient to support laboratory data interoperability. National implementation of LIVD and further efforts to promote laboratory interoperability will require a more comprehensive effort and continuing evaluation and quality control.

Indexed as

Clinical Laboratory Information SystemsCOVID-19HumansLaboratoriesLogical Observation Identifiers Names and CodesSARS-CoV-2United Statesclinical laboratory information systemshealth information interoperabilityLogical Observation Identifiers Names and Codespublic reporting of healthcare data

Identifiers

PMID35639494
PMCPMC9277627

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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.