Abstract
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Documentation burden significantly impacts nursing workload and well-being, with nurses spending an estimated 20-40% of their time on documentation. Ambient AI technologies offer potential to reduce documentation time by mapping real-time nurse-patient conversations to structured EHR data entries with human-in-the-loop verification.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>This protocol describes a pragmatic, EHR-embedded randomized controlled trial evaluating the effectiveness of an Ambient AI tool in reducing nursing documentation time across three inpatient medical/surgical units. The study employs a closed-cohort, stepped-wedge, unit-randomized design, integrating the intervention into routine clinical workflows. The primary outcome is documentation time per shift hour, derived from EHR audit logs. Secondary outcomes include documentation burden, professional well-being, and perceived usability.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>The trial is being implemented within a shared governance model that integrates executive oversight, operational feasibility, and research rigor. Multidisciplinary workgroups coordinate technical integration, user experience, and analytics, ensuring alignment between operational priorities and pragmatic trial objectives. Early implementation has highlighted the importance of adapting training and analytic strategies to address differential intervention exposure, as well as the need for rapid operational responses to late-emerging technical issues.</jats:p> </jats:sec> <jats:sec> <jats:title>Discussion</jats:title> <jats:p>This protocol demonstrates the feasibility of embedding a randomized pragmatic trial within a health system-led operational deployment of Ambient AI for inpatient nursing documentation. The approach highlights the necessity of adapting existing outpatient provider-focused AI implementation strategies for inpatient nursing, emphasizing the unique nature of different nursing care environments. Recruitment challenges and the integration of research with operational workflows are discussed as key considerations for future pragmatic AI trials in nursing.</jats:p> </jats:sec> <jats:sec> <jats:title>Funding</jats:title> <jats:p>The protocol development was funded by University of Wisconsin Hospitals and Clinics (UW Health) and UW Institute for Clinical and Translational Research Learning Health System</jats:p> <jats:p> <jats:bold>ClinicalTrials.gov Identifier:</jats:bold> NCT07456241V4: 2026-05-27 <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://clinicaltrials.gov/study/NCT07456241">https://clinicaltrials.gov/study/NCT07456241</jats:ext-link> </jats:p> </jats:sec>