AI Tool to Reduce Clinician Documentation Burden

This study looks at whether a clinician-facing artificial intelligence (AI) tool can help outpatient doctors spend less time reviewing medical records and documenting care. The tool creates brief summaries of existing medical record information to support routine clinical work. This study examines how use of this tool affects clinicians' workload, time spent in the electronic health record, and overall experience with documentation. The goal is to better understand whether AI documentation support tools can improve efficiency and reduce burden for clinicians in outpatient specialty practice. Eligible UNC outpatient specialists may be invited to complete surveys and, if they qualify, are randomly assigned either to receive access to the tool (Evidently) at the beginning of the study period or to continue their usual workflow for eight weeks before receiving access. The study does not recruit patients and does not change medical care for patients.

Inclusion Criteria: Participants must meet all of the following criteria: * Attending outpatient specialists affiliated with UNCFP or UNCMG. * At least two half-days (≥3.5 hours per session) of scheduled outpatient clinic per week. * Primary specialty in an outpatient, patient-facing specialty practice within UNCFP or UNCMG. Anesthesiologists will be enrolled under the same protocol but analyzed separately due to distinct workflow characteristics. Exclusion Criteria: Clinicians meeting any of the following criteria will be excluded: * Trainees (e.g., residents or fellows). * Advanced practice providers (APPs). * Attendings whose outpatient workflows are predominantly supervisory or shared with trainees or APPs, defined as clinicians who self-report that 25% or more of outpatient clinic sessions are conducted jointly with trainees or APPs. * Clinicians who participated in a prior Evidently pilot study. * Clinicians with planned time away exceeding one week during the study period. Exclusion criteria will be assessed using self-report in the baseline survey, supplemented by administrative records where available (e.g., prior pilot participation or clinic volume). Clinicians meeting any exclusion criterion will not be randomized.