Secondary Logo

Institutional members access full text with Ovid®

Variation in Laboratory Test Naming Conventions in EHRs Within and Between Hospitals

A Nationwide Longitudinal Study

Wiitala, Wyndy L., PhD*; Vincent, Brenda M., MS*; Burns, Jennifer A., MHSA*; Prescott, Hallie C., MD, MSc*,†; Waljee, Akbar K., MD*,†; Cohen, Genna R., PhD; Iwashyna, Theodore J., MD, PhD*,†

doi: 10.1097/MLR.0000000000000996
Online Article: Applied Methods

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,

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.

*Veterans Affairs Center for Clinical Management Research

Department of Internal Medicine and Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI

Mathematica Policy Research, Washington, DC

Supported by VA Health Services Research & Development IIR #109. T.J.I. is supported in part by K12 HL138039 and Veterans Health Administration's Office of Reporting, Analytics, Performance, Improvement and Deployment (RAPID). H.C.P. is supported in part by K08 GM115859 [from the National Institutes of Health]. A.K.W. is supported in part by VA Health Services Research & Development IIR #16-024.

This paper does not necessarily represent the position or policy of the US government or the Department of Veterans Affairs.

The authors declare no conflict of interest.

Reprints: Wyndy L. Wiitala, PhD, North Campus Research Complex, 2800 Plymouth Rd, Building 16/Room 346, Ann Arbor, MI 48109-2800. E-mail:

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.