Jalilian Laleh, Lukac Paul, Lane-Fall Meghan
Department of Anesthesiology and Perioperative Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, United States.
Department of Pediatrics, UCLA David Geffen School of Medicine, UCLA Health Information Technology, UCLA Health, Los Angeles, CA 90095, United States.
J Am Med Inform Assoc. 2025 Sep 26. doi: 10.1093/jamia/ocaf166.
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.
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.
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.
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.
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.
本观点探讨环境人工智能(AI)抄写员如何支持急性护理环境中基于团队的结构化提供者间沟通的文档记录和质量改进(QI)。
在急性护理环境中,多学科查房和交接班等基于团队的讨论对于提供安全护理至关重要。这些讨论依赖标准化框架(如IPASS、检查表)来确保一致的信息传递和共同理解。尽管其很重要,但这些口头讨论在电子健康记录中往往记录不完整或未被记录,导致临床叙述出现空白,QI评估困难,以及组织学习机会丧失。
我们概述了环境AI抄写员如何在日常查房和交接班讨论中增强基于团队沟通的文档记录。我们研究了关键的社会技术挑战,包括工作流程整合、多提供者同意、监督问题和供应商合作。我们描述了作为早期可行性信号的概念验证演示的经验。
环境AI抄写员是捕捉结构化团队沟通的有前途的工具。应探索其使用潜力,以改善文档记录、支持临床医生的健康状况,并实现数据驱动的QI和沟通保真度评估方法。有效实施需要对工作流程进行调整,包括抄写员输出验证、透明治理和建立信任的努力,以确保临床医生接受。
环境AI抄写员代表了急性护理环境中结构化团队讨论文档记录的一个新前沿,有可能加强这些重要对话的沟通可靠性和系统学习。未来的研究应评估其对急性护理环境中患者安全、劳动力健康和患者结局的影响。