From Conversation to Chart: An Analysis of Clinician Edits to Ambient AI Draft Notes

Abstract

Structured Abstract Objective Ambient artificial intelligence (AI) tools are increasingly adopted in clinical practices. This study investigated whether and how clinicians edit AI-generated drafts and the linguistic differences between AI drafts and clinician-finalized notes. Materials and Methods This retrospective study analyzed real-world data from ambulatory clinics at a large academic health system spanning two vendor deployments. We quantified clinicians’ editing behavior using the Myers diff algorithm to compare AI drafts and final documentation. We then applied statistical and linguistic analysis to study factors associated with the frequency/intensity of editing across note sections, turnaround time, clinician characteristics, and encounter types. Results Across 23,760 notes that included one or more ambient AI sections, 84.4% were edited by clinicians before signing off. While rates of unedited notes differed across note sections and care settings, the dominant source of variation was individual clinician practice style rather than specialty-level norms. Notes signed after 24 hours had lower overall edit intensity. The final versions showed small but statistically significant linguistic changes and exhibited slightly higher lexical diversity and modest changes in readability. Editing is most intensive in the assessment and plan section, and varies across specialties. Conclusion and Discussion A majority of AI-drafted clinical notes were edited by clinicians, although the editing rate varies across note sections, medical specialties, and individual clinicians. Future research is needed to further analyze this editing behavior to inform improvement in AI-assisted clinical documentation to achieve better documentation quality, efficiency, and clinician satisfaction.

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