An Evaluation of Epic EHR AI Outpatient Chart Summarization

The primary aim of this study is to evaluate the impact of an EHR developed generative AI outpatient chart summarization tool on self-reported physician-task load score (PTL), comparing the tool to a control group. Exploratory outcomes include EHR-derived time metrics (Caboodle and Signal), Professional fulfillment Index (PFI), usability (SUS), provider satisfaction and productivity, and patient experience item results from CG-CAHPS. We will also evaluate whether AI literacy modifies adoption and effect of the tool using the short-form Meta AI Literacy Scale (MAILS). On an exploratory basis, we will also perform adjustments based on provider specialty, access to an ambient-listening AI scribe, panel complexity, provider age group, provider sex, and time-varying effects by month over the study period. Enrolled participants are randomized to one of two groups. Randomization will be stratified by whether the participant has an active AI scribe license, and covariate-constrained randomization will be performed within strata to improve balance on baseline PTL (NASA-TLX-adapted score) and a modified baseline chart review time (Caboodle-derived). Due to the nature of the intervention, participants cannot be blinded to group assignment. The primary purpose of the initiative is to improve quality, efficiency, and business operations at University of California, Los Angeles (UCLA) Health and will inform the operational implementation of the tool across all providers within the UCLA Health System. Nevertheless, the UCLA study team plans to rigorously examine and publish the impact of this intervention across the health system, which is why the study team pre-registered the initiative.