What Do Clinicians Edit in Ambient AI-Drafted Clinical Documentation? A Qualitative Content Analysis

Abstract

Objective Ambient artificial intelligence (AI) documentation is increasingly used to draft clinical notes from patient-provider conversations, but how clinicians revise and finalize these drafts is not well understood. This qualitative content analysis study characterizes real-world edits to AI-generated drafts and identifies opportunities for improvement of AI design and the implementation process. Materials and Methods Eight coders analyzed clinical documentation generated by ambient AI from 200 clinical encounters. We developed an inductive coding framework with 11 codes across three categories: clinical content, terminology, and language style. Interrater reliability was assessed using Cohen’s kappa. We then applied thematic analysis to synthesize patterns across the coded edits. Results The most frequently edited content pertained to clinical facts including orders (e.g., procedures, lab tests) (40.0%), symptoms (30.3%), medication prescriptions (27.3%), and diagnosis descriptions (25.9%). In comparison, edits related to terminology use (11.6%) and language style (7.2%) were less frequent. The results of our thematic analysis show that most edits can be categorized into one of the following five types: to correct factual errors, to address needs of medical specialty, to express diagnostic certainties, to convert patient expressions into objective assessments recorded in medical terms, and to reorganize or condense content. Conclusion and Discussion Clinicians routinely revise ambient AI drafts to improve accuracy and clinical specificity. Future work on AI development and clinical implementation should emphasize specialty customization and support personalized documentation practices, alongside clinician education that promotes robust and consistent review routines to ensure documentation quality.

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