athenaOne 的原生环境式 AI 如何每月为每位临床医生节省两个工作日
How athenaOne’s Native Ambient AI Saves Two Workdays Monthly Per Clinician
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athenahealth 宣布在其 athenaOne 网络中广泛部署 AI 原生功能，将原生环境式功能扩展至超过170,000名医生和临床医生。
核心创新 athenaAmbient 直接内置于 athenaOne 中，无需额外费用，可捕捉患者就诊过程，生成笔记草稿、建议诊断、医嘱以及护理缺口提醒。
自2025年末以来的网络结果显示，每次患者就诊在病历准备和文档记录方面减少了近六分钟，从而每月为临床医生节省超过两个工作日。
早期采用者报告称，当日完成病历的比例最高提升了30%，显著减少了文档积压。
产品开发由 EHR AI CoLab（athenaInstitute 内部的联合创新论坛）推动，并结合了数千名 alpha/beta 临床医生和超过70个用户群体的测试。
目前正在测试的后续功能包括基于问题的摘要、时间线病历视图、AI 原生临床收件箱以及基于价值的医疗保健分析助手。
原生环境式就诊架构
athenahealth 宣布其 athenaOne 平台中的新 AI 原生功能正式全面可用，将嵌入式环境式文档记录、决策支持和工作流自动化扩展至超过170,000名临床医生。这些功能直接构建于核心 EHR 中，且无需额外订阅费用，此次发布对传统的点式解决方案抄录工具构成挑战；后者需要外部附加组件、单独登录或按用户每月收取许可费。
此次部署的核心是 athenaAmbient，这是一款环境式文档记录工具，可通过桌面设备和移动设备捕捉患者与临床医生之间的对话。该系统原生运行于病历中，可自动起草临床笔记、推断潜在诊断、推荐医嘱类型，并实时识别护理缺口。
嵌入式工作流直接应对行政疲劳和病历记录积压：
行政恢复：每次患者就诊在病历准备和文档记录方面节省近六分钟，使每位临床医生每月能够收回超过两个完整工作日。
病历记录速度：早期采用者的当日病历完成率最高提升30%。
零成本纳入：作为 athenaOne 订阅的核心功能提供，免去了第三方软件的额外开销。
联合创新与 Alpha 流程
为验证模型输出和工作流适配性，athenahealth 启动了 EHR AI CoLab——其 athenaInstitute 研究中心内的联合创新论坛，由临床医生和超级用户组成。该团队与来自70多个用户群体的数千名 alpha/beta 测试者协作，在广泛发布前改进新工具。
目前处于 alpha 测试阶段的其他 AI 原生工具包括：
基于问题的摘要：围绕特定诊断汇总相关病历数据，以减少查看操作。
AI 原生临床收件箱：呈现上下文相关的洞察并建议后续步骤，以简化任务管理。
基于价值的医疗保健分析助手：跟踪质量指标表现和人群健康目标，以支持基于价值的合同合规性。
“这些 AI 功能正在帮助我减少花在文档记录上的时间，并有更多时间全身心陪伴患者，”科罗拉多州 DTC Family Medicine 的所有者 Lynn Joffe 医生说。“我能够带着适当的背景信息走进诊室，持续专注于眼前的患者，并在同一天完成更多病历记录。”
What You Should Know athenahealth announced the broad deployment of AI-native capabilities across its athenaOne network, extending native ambient features to over 170,000 physicians and clinicians.
The core innovation, athenaAmbient, is built directly into athenaOne at no extra charge, capturing patient encounters to generate draft notes, suggested diagnoses, orders, and care-gap alerts.
Network results since late 2025 show a reduction of nearly six minutes per patient encounter in chart preparation and documentation—saving clinicians more than two workdays per month.
Early adopters report an increase of up to 30% in same-day chart completion rates, significantly reducing documentation backlogs.
Product development is driven by the EHR AI CoLab (a co-innovation forum within athenaInstitute) alongside testing involving thousands of alpha/beta clinicians and over 70 user groups.
Upcoming capabilities currently in testing include problem-based summaries, a timeline chart view, an AI-native clinical inbox, and a value-based care analytics assistant.
Native Ambient Encounter Architecture athenahealth has announced the general availability of new AI-native capabilities across its athenaOne platform, extending embedded ambient documentation, decision support, and workflow automation to more than 170,000 clinicians. Built directly into the core EHR at no additional subscription cost, the rollout challenges traditional point-solution scribes that require external add-ons, separate logins, or per-user monthly licensing fees.
At the center of the deployment is athenaAmbient, an ambient documentation tool that captures patient-clinician conversations across desktop and mobile devices. Operating natively within the chart, the system automatically drafts clinical notes, infers potential diagnoses, recommends order types, and identifies care gaps in real time.
The embedded workflow directly addresses administrative fatigue and charting backlogs:
Administrative Recovery:
Saves nearly six minutes per patient encounter on chart preparation and documentation, yielding more than two full workdays recovered per clinician each month.
Charting Velocity:
Increases same-day chart completion by up to 30% among early adopters.
Zero-Cost Inclusion:
Delivered as a core feature of the athenaOne subscription, bypassing third-party software overhead.
Co-Innovation and Alpha Pipeline
To validate model outputs and workflow fit, athenahealth launched the EHR AI CoLab—a co-innovation forum within its athenaInstitute research hub comprising clinicians and superusers. The group works alongside thousands of alpha/beta testers across 70+ user groups to refine new tools before broad release.
Additional AI-native tools currently in alpha testing include:
Problem-Based Summaries:
Aggregates relevant chart data around specific diagnoses to reduce review clicks.
AI-Native Clinical Inbox:
Surfaces contextual insights and suggests next steps to streamline task management.
Value-Based Care Analytics Assistant:
Tracks quality measure performance and population health targets to support value-based contract compliance.
“These AI capabilities are helping me spend less time on documentation and more time fully present with my patients,” said Dr. Lynn Joffe, owner of DTC Family Medicine in Colorado. “I’m able to walk into the exam room with the right context, stay engaged with the patient in front of me, and complete more of my charts the same day.” Reader Interactions