Real-World Adoption, Use, and Governance of an LLM-Based Electronic Health Record Search and Synthesis Platform
We evaluated the longitudinal adoption and use of Scout, an electronic health record-integrated large language model platform that provides cited search and synthesis of patient records, during staged deployment across a multi-hospital academic health system. Over 11 months, 2,635 users completed 80,465 tasks. 1,768 clinical users initiated use within the first 29 days of system-wide launch, which included a required training. Daily initiation increased 21.2-fold at launch, from 2.9 to 61.0 users per day. Of the 495 users who initiated use during the launch week, 310 did so in the first two days. Use spanned more than 20 recorded specialty categories, five care-practitioner roles, and 46,485 distinct prompts involving retrieval, synthesis, extraction, evaluation, and drafting, with 752 tasks saved for reuse. Among care practitioners who completed the required course, 77.9% went on to use Scout, with a median of 1.7 days after course completion. The highest-intensity user quintile accounted for 59.0% of tasks and 61.9% of tokens. Exploratory prompt analysis showed use across retrieval, synthesis, extraction, evaluation, and note-drafting. These findings suggest that rapid, broad adoption requires clinician-led task design and evaluation, traceable governance proportional to reach, centralized capacity planning and user support, and training integrated with access.