Ambient Documentation Systems in Emergency Medicine: A Scoping Review of Clinical Precision, Patient Experience, Throughput, and Quality

This scoping review maps the existing evidence on ambient documentation systems, with an emphasis on emergency medicine applications. It identifies key concepts and knowledge gaps while examining implications for documentation precision, patient experience, clinician well-being, throughput and efficiency, algorithmic equity, safety and governance, and financial and quality outcomes. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The protocol was prospectively registered on the Open Science Framework (OSF). PubMed, MEDLINE (via Ovid), Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Association for Computing Machinery (ACM) Digital Library were searched from January 2015 through March 2026, with final searches conducted on 21 March 2026. English-language peer-reviewed studies, systematic reviews, policy analyses, and expert commentaries addressing ambient documentation systems, medical scribes, documentation burden, automatic speech recognition (ASR) bias, or clinical recording consent in healthcare settings were included. In total, 27 sources met the inclusion criteria. Ambulatory evidence, including two randomized controlled trials and a multicenter pre-post study across six health systems, showed reductions in documentation time, cognitive load, and clinician burnout. Growing emergency department (ED)-specific evidence, with three studies examining ambient artificial intelligence (AI) adoption, documentation time, and note quality in ED settings, suggests that ambient systems may shift rather than eliminate documentation effort, particularly in emergency settings characterized by interruptions, multitasking, and evolving clinical narratives. Computer science literature has identified significant racial and dialect-based disparities in the ASR systems that underpin ambient documentation, with higher word error rates for Black speakers than for White speakers. Key thematic areas include scalability advantages over human scribes, documentation precision, patient consent in high-acuity settings, algorithmic equity, and research gaps in ED-specific outcomes. Ambient documentation systems offer a scalable approach to addressing documentation burden in emergency medicine. The ambulatory evidence base shows consistent benefits in reducing electronic health record time, cognitive load, and clinician burnout, while ED-specific studies support the feasibility of ambient AI, with adoption concentrated in lower-acuity, non-interpreted encounters and ongoing questions about performance in high-acuity, complex settings. Based on these identified themes, we propose a seven-domain framework for evaluating ambient documentation systems in emergency medicine, encompassing documentation precision, patient experience, clinician well-being, throughput and efficiency, algorithmic equity, safety and governance, and financial and quality outcomes. Equitable deployment requires attention to disparities in ASR across racial, ethnic, and linguistic groups, with ongoing monitoring and bias auditing as core governance components. Future research should focus on ED-specific outcomes, including documentation accuracy and patient safety, long-term safety monitoring, patient-centered consent processes, financial and quality implications, and strategies to ensure that ambient documentation benefits all patient and clinician populations equitably.