Evidence map›Paper›PMID 28505339›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2018

LabRS: A Rosetta stone for retrospective standardization of clinical laboratory test results.

Ronald George Hauser, Douglas B Quine, Alex Ryder

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Interactive Machine Learning for Laboratory Data Integration.Studies in health technology and informatics · 2019
    Article
  5. Review
  6. Semantic data interoperability, digital medicine, and e-health in infectious disease management: a review.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2019
    Review
  7. Observational
  8. Article
  9. Article
  10. Article
  11. 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

3 authors.

Ronald George HauserVeterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
Douglas B QuineVeterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
Alex RyderChildren's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN, USA.

Funding

IMMUNOHEMATOLOGY/TRANSFUSION MEDICINE RESEARCH TRAININGT32HL007974 · NHLBI · YALE UNIVERSITY · PI JEANNE E HENDRICKSON, Diane S Krause · 2001 to 2026
$8.1M
NHLBI NIH HHS T32 HL007974
6 · The paper itself

Abstract

Objective: Clinical laboratories in the United States do not have an explicit result standard to report the 7 billion laboratory tests results they produce each year. The absence of standardized test results creates inefficiencies and ambiguities for secondary data users. We developed and tested a tool to standardize the results of laboratory tests in a large, multicenter clinical data warehouse. Methods: Laboratory records, each of which consisted of a laboratory result and a test identifier, from 27 diverse facilities were captured from 2000 through 2015. Each record underwent a standardization process to convert the original result into a format amenable to secondary data analysis. The standardization process included the correction of typos, normalization of categorical results, separation of inequalities from numbers, and conversion of numbers represented by words (eg, "million") to numerals. Quality control included expert review. Results: We obtained 1.266 × 109 laboratory records and standardized 1.252 × 109 records (98.9%). Of the unique unstandardized records (78.887 × 103), most appeared <5 times (96%, eg, typos), did not have a test identifier (47%), or belonged to an esoteric test with <100 results (2%). Overall, these 3 reasons accounted for nearly all unstandardized results (98%). Conclusion: Current results suggest that the tool is both scalable and generalizable among diverse clinical laboratories. Based on observed trends, the tool will require ongoing maintenance to stay current with new tests and result formats. Future work to develop and implement an explicit standard for test results would reduce the need to retrospectively standardize test results.

Indexed as

Vocabulary, ControlledAlgorithmsClinical Laboratory Information SystemsClinical Laboratory TechniquesData AnalysisDatabases, FactualElectronic Health RecordsHumansLogical Observation Identifiers Names and CodesQuality ControlUnited Statesclinical laboratory information systemscontrolled vocabularyLOINC

Identifiers

PMID28505339
PMCPMC6251547

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.