Literature DB >> 30394981

Variation in Laboratory Test Naming Conventions in EHRs Within and Between Hospitals: A Nationwide Longitudinal Study.

Wyndy L Wiitala1, Brenda M Vincent1, Jennifer A Burns1, Hallie C Prescott1,2, Akbar K Waljee1,2, Genna R Cohen3, Theodore J Iwashyna1,2.   

Abstract

BACKGROUND: Electronic health records provide clinically rich data for research and quality improvement work. However, the data are often unstructured text, may be inconsistently recorded and extracted into centralized databases, making them difficult to use for research.
OBJECTIVES: We sought to quantify the variation in how key laboratory measures are recorded in the Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) across hospitals and over time. We included 6 laboratory tests commonly drawn within the first 24 hours of hospital admission (albumin, bilirubin, creatinine, hemoglobin, sodium, white blood cell count) from fiscal years 2005-2015.
RESULTS: We assessed laboratory test capture for 5,454,411 acute hospital admissions at 121 sites across the VA. The mapping of standardized laboratory nomenclature (Logical Observation Identifiers Names and Codes, LOINCs) to test results in CDW varied within hospital by laboratory test. The relationship between LOINCs and laboratory test names improved over time; by FY2015, 109 (95.6%) hospitals had >90% of the 6 laboratory tests mapped to an appropriate LOINC. All fields used to classify test results are provided in an Appendix (Supplemental Digital Content 1, http://links.lww.com/MLR/B635).
CONCLUSIONS: The use of electronic health record data for research requires assessing data consistency and quality. Using laboratory test results requires the use of both unstructured text fields and the identification of appropriate LOINCs. When using data from multiple facilities, the results should be carefully examined by facility and over time to maximize the capture of data fields.

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Year:  2019        PMID: 30394981      PMCID: PMC6417968          DOI: 10.1097/MLR.0000000000000996

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  5 in total

1.  Veterans Affairs patient database (VAPD 2014-2017): building nationwide granular data for clinical discovery.

Authors:  Xiao Qing Wang; Brenda M Vincent; Wyndy L Wiitala; Kaitlyn A Luginbill; Elizabeth M Viglianti; Hallie C Prescott; Theodore J Iwashyna
Journal:  BMC Med Res Methodol       Date:  2019-05-08       Impact factor: 4.615

2.  Variation in model performance by data cleanliness and classification methods in the prediction of 30-day ICU mortality, a US nationwide retrospective cohort and simulation study.

Authors:  Theodore J Iwashyna; Cheng Ma; Xiao Qing Wang; Sarah Seelye; Ji Zhu; Akbar K Waljee
Journal:  BMJ Open       Date:  2020-12-02       Impact factor: 2.692

3.  Pulse oximetry and supplemental oxygen use in nationwide Veterans Health Administration hospitals, 2013-2017: a Veterans Affairs Patient Database validation study.

Authors:  Xiao Qing Wang; Theodore Iwashyna; Hallie Prescott; Valeria Valbuena; Sarah Seelye
Journal:  BMJ Open       Date:  2021-10-08       Impact factor: 3.006

Review 4.  Diagnostic Stewardship as a Team Sport: Interdisciplinary Perspectives on Improved Implementation of Interventions and Effect Measurement.

Authors:  Kyle D Hueth; Andrea M Prinzi; Tristan T Timbrook
Journal:  Antibiotics (Basel)       Date:  2022-02-15

5.  Deriving Weight From Big Data: Comparison of Body Weight Measurement-Cleaning Algorithms.

Authors:  Richard Evans; Jennifer Burns; Laura Damschroder; Ann Annis; Michelle B Freitag; Susan Raffa; Wyndy Wiitala
Journal:  JMIR Med Inform       Date:  2022-03-09
  5 in total

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