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Identifying and mitigating biases in EHR laboratory tests.

Rimma Pivovarov1, David J Albers1, Jorge L Sepulveda2

  • 1Department of Biomedical Informatics, Columbia University, 622 W. 168th Street, New York, NY, USA.

Journal of Biomedical Informatics
|April 15, 2014
PubMed
Summary
This summary is machine-generated.

Electronic health record (EHR) data analysis can be biased if laboratory test contexts are not separated. This study introduces a method using measurement patterns to identify and reduce these biases for more accurate disease modeling.

Keywords:
BiasConfoundingElectronic health recordInformation theoryLaboratory testingMissing data

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Health Data Science

Background:

  • Electronic health records (EHR) offer valuable data for disease modeling.
  • Current EHR research often relies solely on numerical lab test values, overlooking contextual information.
  • Contextual nuances, such as measurement patterns over time, significantly impact laboratory test interpretation.

Purpose of the Study:

  • To investigate potential biases in EHR research due to unseparated laboratory test measurement contexts.
  • To develop and validate a methodology for identifying and mitigating these biases.
  • To improve the accuracy and reliability of disease modeling using EHR data.

Main Methods:

  • Analyzing temporal measurement patterns of laboratory tests across a large patient cohort (over 14,000 patients).
  • Identifying distinct measurement pattern motifs for 70 different laboratory tests.
  • Performing an association study (lipase and acute pancreatitis) to demonstrate bias mitigation.

Main Results:

  • Laboratory test measurement patterns provide crucial information beyond standalone numerical values.
  • Three distinct measurement pattern motifs were identified, with one linked to potential research bias.
  • Accounting for measurement patterns recovered a diluted association signal in a lipase-pancreatitis study.

Conclusions:

  • Aggregating EHR data without considering distinct laboratory test measurement patterns can lead to signal confounding and biased results.
  • Leveraging measurement frequency and patterns is essential for accurate EHR-based disease analysis.
  • This methodology enhances the reliability of large-scale EHR analyses by reducing laboratory test biases.