A Novel Playbook for Pragmatic Trial Operations to Monitor and Evaluate Ambient Artificial Intelligence in Clinical Practice.

Background: Ambient artificial intelligence (AI) offers the potential to reduce documentation burden and improve efficiency through clinical note generation. Widespread adoption, however, remains limited due to challenges in electronic health record (EHR) integration, coding compliance, and real-world evaluation. This study introduces a framework and protocols to design, monitor, and deploy ambient AI within routine care. Methods: We launched an implementation phase to build technical workflows, establish governance, and inform a pragmatic randomized trial. A bi-directional governance model linked operations and research through multidisciplinary workgroups that incorporated the Systems Engineering Initiative for Patient Safety (SEIPS) framework. Integration into the EHR used Fast Healthcare Interoperability Resources (FHIR), and a real-time dashboard tracked utilization and documentation accuracy. To monitor drift, a difference-in-differences analysis was applied to three process metrics: time in notes, work outside of work, and utilization. Audits of ICD-10 compliance were performed using an internally-developed large language model (LLM), whose validity was assessed via correlation with certified professional coders. Results: Ambient AI utilization, measured as the proportion of eligible clinical notes completed using the system, had a weighted median of 65.4% (IQR, 50.6% to 84.0%). Iterative improvement cycles targeted task-specific adoption. A brief workflow issue related to a note template change initially reduced ICD-10 documentation accuracy from 79% (95% CI: 72%-86%) to 35% (95% CI: 28%-42%); accuracy returned to baseline after note template redesign and user training. The internally developed LLM coder achieved a strong correlation with professional coders (Pearson's r = 0.97). The trial enrolled 66 providers across 8 specialties, powered at 90% for the primary outcome on provider well-being. Conclusions: We provide a publicly-available framework and protocols to help safely implement ambient AI in healthcare. Innovations include an embedded pragmatic trial design, human factors engineering, compliance-driven feedback loops, and real-time monitoring to support deployment, ensuring fidelity before initiation of the clinical trial. ClinicalTrialsgov ID: NCT06517082.