Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer.

Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer.

<h4>Purpose</h4>Implicit, unconscious biases in medicine are personal attitudes about race, ethnicity, gender, and other characteristics that may lead to discriminatory patterns of care. However, there is no consensus on whether implicit bias represents a true predictor of differential c...

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Main Authors: Ashwin Ramaswamy, Michael Hung, Joe Pelt, Parsa Iranmahboub, Lina P Calderon, Ian S Scherr, Gerald Wang, David Green, Neal Patel, Timothy D McClure, Christopher Barbieri, Jim C Hu, Charlotta Lindvall, Douglas S Scherr
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2024-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0314989
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https://doi.org/10.1371/journal.pone.0314989

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