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Identifying Bias at Scale in Clinical Notes Using Large Language Models.

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Summary
This summary is machine-generated.

Generative Pretrained Transformer (GPT)-4 accurately detects and revises biased language in clinical notes. This AI approach identifies modifiable factors contributing to documentation bias, potentially reducing healthcare disparities.

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Area of Science:

  • Clinical Informatics
  • Natural Language Processing
  • Healthcare Disparities

Background:

  • Biased language in clinical documentation can perpetuate healthcare disparities.
  • Identifying and mitigating such bias is crucial for equitable patient care.

Purpose of the Study:

  • To assess Generative Pretrained Transformer (GPT)-4's ability to detect and revise biased language in emergency department (ED) notes.
  • To identify factors associated with biased documentation in clinical notes.

Main Methods:

  • Random sampling of 50,000 ED notes and 500 MIMIC-IV discharge notes.
  • GPT-4 flagged four types of bias: discrediting, stigmatizing/labeling, judgmental, and stereotyping.
  • Human reviewers verified GPT-4 detections; multivariable logistic regression analyzed bias associations.

Main Results:

  • GPT-4 demonstrated high sensitivity (97.6%) and specificity (85.7%) in detecting bias compared to human review.
  • Biased language was present in 6.5% of ED notes and 7.4% of MIMIC-IV notes.
  • Frequent healthcare utilization, substance use presentations, and overnight shifts were associated with increased bias; GPT-4 revisions were highly rated by physicians.

Conclusions:

  • GPT-4 effectively detects and suggests revisions for biased language in clinical notes.
  • The AI tool identifies modifiable contributors to documentation bias.
  • This technology holds promise for mitigating bias and reducing healthcare disparities.