title: Bias associated with mining electronic health records.
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.
authors.affiliation: Columbia University. hripcsak@columbia.edu.
authors.forename: George
authors.initials: G
authors.lastname: Hripcsak
authors.forename: Charles
authors.initials: C
authors.lastname: Knirsch
authors.forename: Li
authors.initials: L
authors.lastname: Zhou
authors.forename: Adam
authors.initials: A
authors.lastname: Wilcox
authors.forename: Genevieve
authors.initials: G
authors.lastname: Melton