Understanding Stigmatizing Language in Clinical Documentation: A Paired Comparison of Ambient AI Drafts and Clinician Finalized Notes
For healthcare organizations using ambient AI documentation tools, this study reveals that clinician editing can inadvertently increase stigmatizing language in clinical notes.
Ambient AI drafts contain stigmatizing language in 21.4% of sections, but clinician editing increases this to 24.0%, indicating that editing introduces more stigmatizing terms than it removes.
Ambient artificial intelligence (AI) documentation tools are increasingly deployed to reduce clinician documentation burden, but their implications for biased language in clinical notes remain unclear. We conducted a large-scale comparison analysis of AI drafts and corresponding clinician finalized notes to quantify stigmatizing language changes pre- and post-editing. Using a lexicon-based natural language processing (NLP) pipeline, we measured (1) the prevalence of stigmatizing language in AI drafts, (2) the prevalence and term composition in final notes, and (3) the frequency of removal or introduction of stigmatizing terms. Across 66,297 paired note sections, 21.4% of AI draft sections contained at least one stigmatizing language mention, rising to 24.0% in clinician finalized versions. Introductions occurred more often than removals, suggesting clinician editing can be a net source of stigmatizing language entering the EHR with using Ambient AI.