Evaluating ambient artificial intelligence documentation: effects on work efficiency, documentation burden, and patient-centered care
Yawen Guo, Jiayuan Wang, Di Hu, Steven Tam, Charles Gilman, Emilie Chow, Danielle Perret, Deepti Pandita, Kai Zheng
Abstract
BACKGROUND AND SIGNIFICANCE: Ambient listening tools powered by generative artificial intelligence (GenAI) offer real-time, scribe-like support that reduce documentation burden and may help alleviate burnout. This study assesses physician-perceived benefits and challenges of ambient AI implementation through surveys and evaluates its effectiveness in clinical workflows using automatically recorded electronic health record (EHR) time-efficiency metrics. METHOD AND MATERIALS: A quality improvement pilot has been underway at UCI Health since December 2023. Epic EHR Signal metrics were analyzed to assess changes in note length, documentation time, and same-day encounter closure rates. Matched pre- and post-implementation surveys evaluated physician-perceived changes in documentation burden, clinical efficiency, and care quality. We also examined open-ended survey responses using thematic analysis to supplement quantitative findings. RESULTS: Analysis on EHR usage data from 167 physicians showed significant reductions in note-writing time, despite an increase in note length. Survey responses (n = 65) also indicated statistically significant improvements across multiple domains. Physicians reported reduced cognitive demand (P = .031) and documentation effort (P = .014), alongside perceptions of enhanced clinical efficiency, patient-centered care, and EHR system usability. Thematic analysis confirmed these quantitative findings and identified opportunities for improvement, including specialty-specific customization and expanded AI functionality. DISCUSSION: Ambient AI tools demonstrated improved documentation efficiency, perceived care quality, and reduced cognitive workload. These benefits suggest potential to alleviate key burdens in clinical documentation. CONCLUSION: Future development should prioritize customization for specialty-specific and individual physician needs, ensure the reliability and accuracy of AI-generated content, and integrate ethical and legal considerations to facilitate safe and scalable implementation in patient-centered care contexts.