Applied clinical informatics · 2026
Large-Scale Implementation of Ambient Artificial Intelligence Documentation and Its Effects on Electronic Health Record Efficiency and Clinician Well-Being.
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Svetly Andrew, Razzouk Elie, McLean Mary, Frye Kenneth, Little Andrew, Massaro Lana, Quailey Cheyenne, Svetly Conner, Patel Aatish, Montiel Jesus
Аннотация
ABSTRACT: BACKGROUND: Ambient clinical documentation tools are increasingly used to reduce administrative burden and improve provider experience. However, evidence describing their effects at scale across large, multisite health systems remains limited.
ABSTRACT: OBJECTIVES: The objective of this study is to evaluate the effect of Dragon Ambient eXperience (DAX) Copilot on documentation efficiency, note composition practices, and provider well-being across a multistate health system.
ABSTRACT: METHODS: Due to rolling registration and training, we conducted a retrospective index-date-aligned pre-post analysis of objective electronic health record activity metrics, supplemented by a postimplementation survey. Physicians and advanced practice providers in ambulatory settings between June 2024 and November 2025 were included. Epic Signal data were analyzed for each provider over the 8 months before and after DAX activation. Metrics included active time in notes per appointment, proportion of note content originating from manual typing, copy/paste, or conventional voice recognition, and average note length. Surveys were administered 45 days postactivation to assess satisfaction, burnout, intent to remain with the organization, and work-life balance.
ABSTRACT: RESULTS: A total of 210 providers met the inclusion criteria, defined as generating ≥25% of total note content using ambient voice technology and having at least 16 months of Signal Data (8 months pre- and 8 months postintervention). Compared with the preimplementation period, active note time decreased significantly (5.54-4.09 min per appointment; 26.2% reduction; p < 0.001). Despite a +28.8% increase in note length, manual documentation behaviors declined substantially, including typing (-51.7%), copy/paste (-42.7%), and conventional voice recognition (-67.8%). Survey responses (n = 233; 26.4% response rate) indicated improvements across domains, with most respondents reporting reduced burnout, increased job satisfaction, improved work-life balance, and a strong preference to continue using DAX.
ABSTRACT: CONCLUSION: Deployment of ambient artificial intelligence documentation technology at scale was associated with improvements in documentation efficiency, substantial reductions in manual input, and improved provider well-being, supporting its potential to meaningfully alleviate documentation burden.
ABSTRACT: OBJECTIVES: The objective of this study is to evaluate the effect of Dragon Ambient eXperience (DAX) Copilot on documentation efficiency, note composition practices, and provider well-being across a multistate health system.
ABSTRACT: METHODS: Due to rolling registration and training, we conducted a retrospective index-date-aligned pre-post analysis of objective electronic health record activity metrics, supplemented by a postimplementation survey. Physicians and advanced practice providers in ambulatory settings between June 2024 and November 2025 were included. Epic Signal data were analyzed for each provider over the 8 months before and after DAX activation. Metrics included active time in notes per appointment, proportion of note content originating from manual typing, copy/paste, or conventional voice recognition, and average note length. Surveys were administered 45 days postactivation to assess satisfaction, burnout, intent to remain with the organization, and work-life balance.
ABSTRACT: RESULTS: A total of 210 providers met the inclusion criteria, defined as generating ≥25% of total note content using ambient voice technology and having at least 16 months of Signal Data (8 months pre- and 8 months postintervention). Compared with the preimplementation period, active note time decreased significantly (5.54-4.09 min per appointment; 26.2% reduction; p < 0.001). Despite a +28.8% increase in note length, manual documentation behaviors declined substantially, including typing (-51.7%), copy/paste (-42.7%), and conventional voice recognition (-67.8%). Survey responses (n = 233; 26.4% response rate) indicated improvements across domains, with most respondents reporting reduced burnout, increased job satisfaction, improved work-life balance, and a strong preference to continue using DAX.
ABSTRACT: CONCLUSION: Deployment of ambient artificial intelligence documentation technology at scale was associated with improvements in documentation efficiency, substantial reductions in manual input, and improved provider well-being, supporting its potential to meaningfully alleviate documentation burden.