Ambient Artificial Intelligence Scribe Use and Objective Documentation Effort Among Emergency Physicians

Ambient artificial intelligence (AI) scribes ("AI scribes") are purported to reduce emergency department (ED) documentation burden, but ED evidence is largely single-site, uncontrolled, or self-reported, with exposure inferred rather than measured. We evaluated AI scribe use among attending emergency physicians across a multisite network, with exposure and outcomes read directly from the electronic health record.

Across 20 EDs (3 academic and 17 community), AI scribe use was flagged per note from the patient consent statement within the ED note. Analyses were restricted to attending physicians and, to isolate the physician's own documentation, excluded teaching encounters and attestation notes identified from standardized attestation text. Endpoints, computed over the mature-adoption window (December 2025 onward, when Epic audit-log data are retained), were active time in the note (primary), note length, note-start time, completion lag, and 72-hour ED return. Within-physician models (physician and calendar-month fixed effects, clustered by physician) compared each adopter's AI-scribed notes with their own unassisted notes, adjusting only for pre-exposure case mix.

Among 275 attending physicians, 31% of encounters were AI-scribed. Physicians spent less active time in AI-scribed notes (median 10.0 versus 12.5 min-2.4 minutes after adjustment), started them about 15 minutes later (median 31 versus 17 minutes after first seeing the patient, and produced notes about 14% longer [median 5,220 versus 4,468 characters; within-physician +629 characters]). Note completion was not delayed (-6 minutes), and 72-hour ED return was not increased (-0.8 percentage point; 95% confidence interval -1.5 to -0.1).

Among emergency physicians, AI scribe use was associated with less active time in the note, deferred note-start, and longer notes, without delaying completion or increasing short-term returns, with effect sizes consistent with ambulatory randomized data.