  Electronic health records (EHRs) serve as an essential data source for the
envisioned artificial intelligence (AI)-driven transformation in healthcare.
However, clinician biases reflected in EHR notes can lead to AI models
inheriting and amplifying these biases, perpetuating health disparities. This
study investigates the impact of stigmatizing language (SL) in EHR notes on
mortality prediction using a Transformer-based deep learning model and
explainable AI (XAI) techniques. Our findings demonstrate that SL written by
clinicians adversely affects AI performance, particularly so for black
patients, highlighting SL as a source of racial disparity in AI model
development. To explore an operationally efficient way to mitigate SL's impact,
we investigate patterns in the generation of SL through a clinicians'
collaborative network, identifying central clinicians as having a stronger
impact on racial disparity in the AI model. We find that removing SL written by
central clinicians is a more efficient bias reduction strategy than eliminating
all SL in the entire corpus of data. This study provides actionable insights
for responsible AI development and contributes to understanding clinician
behavior and EHR note writing in healthcare.
