Implicit bias in ICU electronic health record data: measurement frequencies and missing data rates of clinical variables.

Journal: BMC medical informatics and decision making
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Systematic disparities in data collection within electronic health records (EHRs), defined as non-random patterns in the measurement and recording of clinical variables across demographic groups, can be reflective of underlying implicit bias and may affect patient outcome. Identifying and mitigating these biases is critical for ensuring equitable healthcare. This study aims to develop an analytical framework for measurement patterns, defined as the combination of measurement frequency (how often variables are collected) and missing data rates (the frequency of missing recordings), evaluate the association between them and demographic factors, and assess their impact on in-hospital mortality prediction.

Authors

  • Junming Shi
    Division of Biostatistics, University of California Berkeley, Berkeley, CA, USA.
  • Alan E Hubbard
  • Nicholas Fong
    Department of Anesthesia and Perioperative Care, Zuckerberg San Francisco General Hospital and Trauma Center, 1001 Potrero Avenue, CA94110, San Francisco, CA, USA.
  • Romain Pirracchio

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