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Potential for Algorithmic Bias in Clinical Decision Instrument Development.

Jed Keenan Obra1,2, Chandan Singh3, Kenshata Watkins4

  • 1University of California, Berkeley, Berkeley, CA, USA. jedkeenan.obra@ucsf.edu.

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|December 10, 2025
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Summary
This summary is machine-generated.

Clinical decision instruments (CDIs) can introduce bias despite aiming to reduce healthcare disparities. This systematic review found skewed demographics, geographic representation, and variable choices in CDI development, potentially perpetuating inequality.

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Area of Science:

  • Health Informatics
  • Medical Ethics
  • Health Equity

Background:

  • Clinical decision instruments (CDIs) aim to standardize care and reduce disparities.
  • However, standardization may inadvertently perpetuate bias and inequality in healthcare.
  • Potential sources of bias in CDI development require systematic investigation.

Purpose of the Study:

  • To quantitatively characterize potential sources of bias in the development of Clinical Decision Instruments (CDIs).
  • To systematically review 690 CDIs for evidence of bias in their development process.

Main Methods:

  • Quantitative systematic review of 690 Clinical Decision Instruments (CDIs).
  • Analysis focused on four potential sources of bias: participant demographics, investigator team geography, predictor variable selection, and outcome definitions.

Main Results:

  • Evidence of potential algorithmic bias was found in CDI development.
  • Participant demographics were skewed (e.g., 73% White, 55% male).
  • Investigator teams showed geographic skew (52% North America, 31% Europe).
  • CDIs utilized potentially biased predictor variables (e.g., 1.9% used Race and Ethnicity).
  • Outcome definitions, particularly those involving follow-up (26%), may introduce socioeconomic bias.

Conclusions:

  • CDIs, while intended to improve care, may contain inherent biases.
  • Factors such as skewed demographics, geographic representation, and variable selection contribute to potential bias.
  • Recommendations include considering these factors during CDI development and transparently communicating them to clinicians.