Association of ambient artificial intelligence scribe availability with patient instruction documentation and readability in ambulatory care

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ID: 333429
2026
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Abstract
Abstract Objective This retrospective cohort study evaluated the effect of an artificial intelligence (AI) scribe on patient instructions documentation in ambulatory internal medicine settings. Materials and Methods We analyzed 391 061 adult encounters at a single medical center from December 2023 to July 2025. A difference-in-differences analysis estimated the relationship of AI scribe availability with patient instructions documentation frequency. Mixed-effects logistic regression estimated the association between encounter-level AI scribe utilization and documentation, adjusted for patient, clinician, and encounter characteristics. Length and readability were compared across instructions created by AI alone, clinicians assisted by AI, and clinicians alone. Character-level edit distances between AI drafts and final documentation were calculated. Results AI scribe availability was associated with a 10.5 percentage-point increase in patient instructions documentation frequency (P = .018), and encounter-level AI use was associated with higher odds of documentation (aOR 4.32; 95% CI, 4.14-4.50). AI instructions were longer than clinician-authored content (median 1994 vs 482 characters) and scored higher on 3 text-complexity indices (all P < .001). Clinician editing of AI content increased length without meaningfully reducing complexity. AI drafts and final documentation differed substantially, with a median character-level edit distance of 39.7%. Discussion AI scribe availability was associated with increased patient instructions documentation. However, AI-generated instructions were longer and scored as more linguistically complex than clinician-authored instructions, and clinician editing did not close this gap. Conclusion Ambient AI scribes can improve availability but may increase complexity of patient instructions. Research into downstream effects including patient comprehension and satisfaction is warranted.
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Authors Kevin X. Shi, Megan Creasman, Gabriela Schmajuk, Aris Oates, Maria E. Byron, Augusto García-Agúndez, Jinoos Yazdany
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Journal Journal of the American Medical Informatics Association : JAMIA
Year 2026
DOI
10.1093/jamia/ocag177
URL
https://doi.org/10.1093/jamia/ocag177
Keywords Keywords not found

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