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Measurement Error and Misclassification in Electronic Medical Records: Methods to Mitigate Bias

  • Pharmacoepidemiology (S Toh, Section Editor)
  • Published:
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Abstract

Purpose of Review

We sought to (1) examine common sources of measurement error in research using data from electronic medical records (EMR), (2) discuss methods to assess the extent and type of measurement error, and (3) describe recent developments in methods to address this source of bias.

Recent Findings

We identified eight sources of measurement error frequently encountered in EMR studies, the most prominent being that EMR data usually reflect only the health services and medications delivered within the specific health facility/system contributing to the EMR data. Methods for assessing measurement error in EMR data usually require gold standard or validation data, which may be possible using data linkage. Recent methodological developments to address the impact of measurement error in EMR analyses were particularly rich in the multiple imputation literature.

Summary

Presently, sources of measurement error impacting EMR studies are still being elucidated, as are methods for assessing and addressing them. Given the magnitude of measurement error that has been reported, investigators are urged to carefully evaluate and rigorously address this potential source of bias in studies based in EMR data.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major Importance

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Funding

Dr. Jonsson Funk, Dr. Conover, and Ms. Young report grant support from the NIH National Institute on Aging (R01 AG056479), NIH National Heart Lung and Blood Institute (R01 HL118255), and NIH National Center for Advancing Translational Sciences (UL1 TR001111).

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  1. Jessica C. Young and Mitchell M. Conover contributed equally to this work.

Authors and Affiliations

  1. Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 104B Market Street, CB #7521, Chapel Hill, NC, 27599, USA

    Jessica C. Young, Mitchell M. Conover & Michele Jonsson Funk

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  1. Jessica C. Young
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  2. Mitchell M. Conover
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  3. Michele Jonsson Funk
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Correspondence to Michele Jonsson Funk.

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This article is part of the Topical Collection on Pharmacoepidemiology

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Young, J.C., Conover, M.M. & Jonsson Funk, M. Measurement Error and Misclassification in Electronic Medical Records: Methods to Mitigate Bias. Curr Epidemiol Rep 5, 343–356 (2018). https://doi.org/10.1007/s40471-018-0164-x

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