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Published in

University of Illinois at Chicago Library, Journal of Biomedical Discovery and Collaboration, (6), p. 48-52, 2011

DOI: 10.5210/disco.v6i0.3581

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Bias Associated with Mining Electronic Health Records

Journal article published in 2011 by George Hripcsak, Charles Knirsch, Li Zhou, Adam Wilcox, Genevieve B. Melton ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Preprint: policy unclear
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Postprint: archiving allowed
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Published version: policy unknown
Data provided by SHERPA/RoMEO

Abstract

Large‐scale electronic health record research introduces biases compared to traditional manually curated retrospective research. We used data from a community‐acquired pneumonia study for which we had a gold standard to illustrate such biases. The challenges include data inaccuracy, incompleteness, and complexity, and they can produce in distorted results. We found that a naïve approach approximated the gold standard, but errors on a minority of cases shifted mortality substantially. Manual review revealed errors in both selecting and characterizing the cohort, and narrowing the cohort improved the result. Nevertheless, a significantly narrowed cohort might contain its own biases that would be difficult to estimate.