Title: Analyzing How Clinicians Adjust Hedging Language in Ambient AI Records: A Comparison of AI-Generated Drafts and Finalized Notes

Abstract: While ambient artificial intelligence systems produce initial drafts for clinical documentation that providers typically revise before finalizing in electronic health records, the impact of these modifications on hedging language remains poorly understood. This study employed a paired analysis of clinician-edited segments within ambient AI drafts and the corresponding final notes to investigate three key questions: (1) whether such edits influence the frequency of hedging language, (2) if these revisions demonstrate a consistent trend toward increased certainty or uncertainty, and (3) whether variations in hedging frequency and directional shifts vary across different ambient AI vendors and medical specialties.

The analysis of 62,811 paired note sections revealed that clinicians were more likely to introduce hedging terms into text that previously lacked them, rather than removing hedging from text that already contained it. Consequently, the text following edits contained a higher volume of hedging mentions than the original pre-edit drafts. Directionality assessments indicated a statistically significant overall tendency toward heightened uncertainty in edits that replaced existing language. Furthermore, examinations by vendor and clinical specialty uncovered considerable variability in the prevalence of hedging, the changes in hedging mentions from pre- to post-edit, and the directionality of these linguistic shifts.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC