Ambient Artificial Intelligent Scribes in Oncology: Impact on Clinician Experience and Documentation Burden

Ambient artificial intelligence (AI) scribes, widely implemented to reduce clinician burden and dissatisfaction, are not optimized for oncology. We evaluated this technology at-scale at a high-volume cancer center.

This prospective mixed-methods study used surveys, electronic health record (EHR), and utilization data to assess adoption, burden, and satisfaction with an ambient AI scribe that was implemented from June to September 2025. Descriptive statistics categorized clinicians as adopters (3+ weeks of use), attempted adopters (1-2 weeks), and never-users (0 weeks). Generalized estimating equations assessed the adoption's effect on the likelihood of closing notes on visit days and pajama time. Pre- and postimplementation surveys obtained clinicians' insights into satisfaction and documentation burden.

In all, 190 (19.8%) of 958 clinicians used the tool, most often with new, video, and in-person visits. Adopters (n = 124) and attempted adopters (n = 66) were more commonly physicians from medicine and surgery. Pre- to post-rollout, the likelihood of closing notes on visit days remained unchanged among never-users (70% v 70%) and decreased among attempted adopters (73% v 70%) and adopters (56% v 54%). Pajama time remained unchanged among attempted adopters (50.6 v 50.1 minutes per week) and decreased among never-users (69.5 v 62.2) and adopters (86.9 v 81.4). Clinicians completing both pre- and postrollout surveys (n = 45) showed a decrease in high/excessive note-writing time (78% v 60%; P = .043). Postrollout surveys (n = 77) showed largely positive experiences, with 73% reporting they would recommend the tool to colleagues.

This study of an ambient AI scribe at-scale showed positive satisfaction but mixed burden outcomes among oncology clinicians. Notably, despite enterprise-wide implementation and training, adoption was low and varied by clinician and visit characteristics. Future research should explore adoption barriers and tool optimization to support oncology documentation workflows.