{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T17:30:15Z","timestamp":1783186215458,"version":"3.54.6"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"Abstract<\/jats:title>\n \n Objective<\/jats:title>\n This perspective explores how ambient artificial intelligence (AI) scribes could support documentation and quality improvement (QI) of structured, team-based provider-to-provider communication in acute care settings.<\/jats:p>\n <\/jats:sec>\n \n Background<\/jats:title>\n In acute care settings, team-based discussions such as multidisciplinary rounds and handoffs are essential to the delivery of safe care. These discussions rely on standardized frameworks (eg, IPASS, checklists) to ensure consistent information transfer and shared understanding. Despite their importance, these verbal discussions are often incompletely documented or left undocumented in the electronic health record, leading to gaps in clinical narrative, difficulty in QI evaluation, and lost opportunities for organizational learning.<\/jats:p>\n <\/jats:sec>\n \n Approach<\/jats:title>\n We outline how ambient AI scribes could enhance documentation of team-based communication in daily rounding and handoff discussions. We examine key sociotechnical challenges, including workflow integration, multiprovider consent, surveillance concerns, and vendor collaboration. We describe our experience with proof-of-concept demonstrations as an early feasibility signal.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Ambient AI scribes are a promising tool for capturing structured team communication. Their use should be explored for its potential to improve documentation, support clinician well-being, and enable data-driven approaches to QI and communication fidelity assessments. Effective implementation requires workflow adaptations incorporating scribe output verification, transparent governance, and trust-building efforts to ensure clinician acceptance.<\/jats:p>\n <\/jats:sec>\n \n Discussion<\/jats:title>\n Ambient AI scribes represent a novel frontier in documentation of structured team discussions in acute care settings, with the potential to strengthen communication reliability and systems learning of these vital conversations. Future research should evaluate their impact on patient safety, workforce well-being, and patient outcomes in acute care settings.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf166","type":"journal-article","created":{"date-parts":[[2025,9,14]],"date-time":"2025-09-14T11:39:56Z","timestamp":1757849996000},"page":"1785-1789","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing Structured Team Communication in Acute Care Settings with Ambient AI Scribes"],"prefix":"10.1093","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5083-516X","authenticated-orcid":false,"given":"Laleh","family":"Jalilian","sequence":"first","affiliation":[{"name":"Department of Anesthesiology and Perioperative Medicine, UCLA David Geffen School of Medicine , Los Angeles, CA 90095,","place":["United 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