Half the picture: Word frequencies reveal racial differences in clinical documentation, but not their causes.

Clinical notes are the best record of a provider's perceptions of their patients, but their use in studying racial bias in clinical documentation has typically been limited to manual evaluation of small datasets. We investigated the use of computational methods to scale these insights to large, heterogeneous clinical text data. We found significant differences in negative emotional tone and language implying social dominance in clinical notes between Black and White patients, but identified multiple contributing factors in addition to potential provider bias, including mis-categorization of some healthcare vocabulary as emotion-related. We further found that notes for Black patients were significantly less likely to mention opioids than for White patients, potentially reflecting both inequitable access to medication and provider bias. Our analysis showed that computational tools have significant potential for studying racial bias in large clinical corpora, and identified key challenges to providing a nuanced analysis of bias in clinical documentation. ©2022 AMIA - All rights reserved.