In the face of formidable healthcare challenges, such as staffing shortages and rising costs, technology has emerged as a crucial ally in enhancing patient care. UCHealth, Colorado’s largest health system, has pioneered the integration of technology into patient care through its Virtual Health Center (VHC).

面对严峻的医疗保健挑战，如人员短缺和成本上升，技术已成为加强患者护理的关键盟友。科罗拉多州最大的卫生系统UCHealth率先通过其虚拟健康中心（VHC）将技术整合到患者护理中。

In this Comment, we explore UCHealth’s journey in creating a centralized hub that harnesses innovative digital health solutions to address patient care needs across its 12 hospitals, spanning over 600,000 emergency department visits and nearly 150,000 inpatient and observation encounters annually. The VHC has proven to be a transformative force, providing high-quality care at scale, reducing staff burden, and establishing new career pathways in virtual health.

在这篇评论中，我们探讨了UCHealth在创建一个集中中心方面的历程，该中心利用创新的数字健康解决方案来满足其12家医院的患者护理需求，每年覆盖60多万急诊科就诊和近15万住院和观察。VHC已被证明是一支变革力量，可大规模提供高质量护理，减轻员工负担，并在虚拟健康领域建立新的职业途径。

The transformation process involved multiple steps: (a) identifying a need, (b) vetting within health system solutions, (c) searching for industry solutions, and scrutinizing these through meetings with our innovations center, (d) piloting the solution, and (e) sustaining the solution by integrating them within the electronic health record (EHR)..

转型过程涉及多个步骤：（a）确定需求，（b）在卫生系统解决方案中进行审查，（c）寻找行业解决方案，并通过与创新中心的会议进行审查，（d）试点解决方案，以及（e）通过将其整合到电子健康记录（EHR）中来维持解决方案。。

IntroductionUCHealth’s1 executive team, serving approximately 2.7 million patients across Colorado, recognized the need for a strategic approach to overcome challenges related to in-person care availability, especially in smaller facilities. In response, we established the VHC in 2016 (Fig. 1), initially focusing on critical care services in smaller and rural hospitals.

简介Uchealth的1执行团队为科罗拉多州约270万患者提供服务，他们认识到需要采取战略方法来克服与个人护理可用性相关的挑战，特别是在较小的设施中。作为回应，我们于2016年建立了VHC（图1），最初专注于小型和农村医院的重症监护服务。

Over time, it evolved to encompass myriad acute care functionalities, including remote telemetry, real-time predictive modeling, and virtual interventions2. We decided to invest in a centralized infrastructure that could work across multiple hospitals, vet existing technologies, and create new resources to meet our goals.

。我们决定投资建设一个可以跨多家医院工作的集中式基础设施，审查现有技术，并创造新资源以实现我们的目标。

This decision proved prescient during the COVID-19 pandemic, when rapidly increasing need for telemedicine services, unprecedented levels of staff illness, and challenges in expanding the clinical workforce made it essential to provide more support to patients with fewer resources. The VHC serves as a highly adaptable telemedicine platform for delivering multiple programs while achieving economies of scale (Table 1).Fig.

在新型冠状病毒肺炎大流行期间，这一决定被证明是有先见之明的，当时对远程医疗服务的需求迅速增加，员工患病率达到前所未有的水平，以及扩大临床劳动力的挑战，使得必须为资源较少的患者提供更多的支持。VHC是一个适应性很强的远程医疗平台，可以在实现规模经济的同时提供多个项目（表1）。图。

1: VHC.From 2016 a to 2023 b. In this two panel figure, we show the evolution of the Virtual Health Center. In 2016, we started the VHC by creating a business plan for three key hospitals. In 2016 we had <20 virtual visits for the entire month. By 2023, we scaled our virtual capabilities across all 12 hospitals.

1： VHC。从2016年a到2023年b。在这个双面板图中，我们展示了虚拟健康中心的演变。2016年，我们通过为三家重点医院制定商业计划启动了VHC。2016年，我们整个月的虚拟访问不到20次。到2023年，我们在所有12家医院扩展了虚拟能力。

By 2023, we scaled virtual visits to over 70,000 a month.Full size imageTable 1 Examples of how VHC programs achieve economies of scaleFull size tableKey goals and achievementsThe VHC’s mission includes delivering high-quality patient care, alleviating staff burden3, empowering clinicians, creating a virtual health career pathway, and seamlessly implementing new.

到2023年，我们将虚拟访问量扩大到每月超过70000次。全尺寸imageTable 1 VHC计划如何实现规模经济的例子全尺寸表关键目标和成就VHC的使命包括提供高质量的患者护理，减轻员工负担3，赋予临床医生权力，创建虚拟健康职业途径，以及无缝实施新的。

1.

1.

Standardize the identification and subsequent treatment of deteriorating patients with frontline staff and VHC tools

使用一线工作人员和VHC工具标准化恶化患者的识别和后续治疗

2.

2.

Provide timely identification of deterioration with frontline staff and VHC tools

使用一线员工和VHC工具及时识别恶化情况

3.

3.

Improve time from identified deterioration to time to intervention

缩短从确定的恶化到干预的时间

4.

4.

Standardize vital sign monitoring after a rapid response when patient remains on the floor to detect further deterioration

当患者躺在地板上以检测进一步恶化时，快速反应后，标准化生命体征监测

Emergency and ICU trained physicians located in the VHC can remotely place orders such as intravenous fluids, antibiotics, and other essential, time-sensitive interventions necessary to treat sepsis in partnership with the primary bedside clinical team.Several steps were necessary to successfully implement early sepsis detection in patients who were boarding (awaiting a hospital bed after being admitted from the emergency department): (1) VHC staff training to identify sepsis, (2) clinical intelligence applications such as predictive models to identify concerning clinical trends, and (3) change management among in-house clinicians to initiate interventions on patients determined by the VHC algorithms and staff to be at high risk.Elements of the clinical intelligence applications include: (a) a dashboard in the VHC that shows the current status of the four embedded predictive tools (Shock Index, Epic Deterioration Index, Epic Sepsis Prediction Model, and Respiratory Distress Index) and (b) modifications to the EHR to allow for efficient screening of patients across numerous hospitals within the health system.

VHC中接受过紧急和ICU培训的医生可以远程下订单，例如静脉输液，抗生素以及与主要床边临床团队合作治疗败血症所需的其他必要的，对时间敏感的干预措施。（1）VHC工作人员培训以识别败血症，（2）临床情报应用，如预测模型，以确定有关临床趋势，以及（3）内部临床医生的变化管理，以对VHC算法和工作人员确定的高危患者进行干预。临床情报应用的要素包括：（a）VHC中的仪表板，显示四种嵌入式预测工具（休克指数，Epic恶化指数，Epic败血症预测模型和呼吸窘迫指数）的当前状态，以及（b）修改EHR，以便在卫生系统内的多家医院对患者进行有效筛查。

All predictive models were implemented using Epic’s Azure-hosted Nebula module using native Epic querying tools such as Reporting Workbench. There were no interfaces to outside resources. Therefore, components such as FHIR messaging and external standardized terminologies were not required for initial implementation.

所有预测模型都是使用Epic的Azure托管星云模块，使用本地Epic查询工具（如Reporting Workbench）实现的。没有与外部资源的接口。因此，最初的实施不需要FHIR消息传递和外部标准化术语等组件。

None of the predictive models are currently FDA approved. Our models use patient-specific information used in routine clinical practice to facilitate assessments, and these non-device Clinical Decision Support software functions are exempt from FDA approval6. The Epic Sepsis Model and Epic Deterioration index are proprietary algorithms licensed by Epic..

目前没有一种预测模型获得FDA批准。我们的模型使用常规临床实践中使用的患者特定信息来促进评估，这些非设备临床决策支持软件功能不受FDA批准6的限制。Epic败血症模型和Epic恶化指数是Epic许可的专有算法。。

Data availability

数据可用性

All data generated or analyzed during this study are included in this published article and it’s referenced articles.

本研究中生成或分析的所有数据均包含在本文及其参考文章中。

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Download referencesAcknowledgementsThis study was funded by the National Institutes of Health [K76 AG059983; PI Goldberg]. The funder played no role in study design, data collection, analysis and interpretation of the data, or the writing of this manuscript.Author informationAuthors and AffiliationsUniversity of Colorado Anschutz Medical Campus, Department of Emergency Medicine, Aurora, CO, USAElizabeth Goldberg, Bethany Kwan & Richard ZaneUniversity of Colorado Anschutz Medical Campus, Department of Medicine – Cardiology, Aurora, CO, USADave KaoUniversity of Colorado Anschutz Medical Campus, Department of Medicine – Hospital Medicine, Aurora, CO, USAHemali PatelUCHealth Nursing Administration, Aurora, CO, USAAmy HassellAuthorsElizabeth GoldbergView author publicationsYou can also search for this author in.

下载参考文献致谢本研究由美国国立卫生研究院资助[K76 AG059983；PI Goldberg]。资助者在研究设计，数据收集，数据分析和解释或撰写本手稿方面没有发挥任何作用。作者信息作者和附属机构科罗拉多州安舒茨大学医学院急诊医学系，科罗拉多州奥罗拉市，美国伊丽莎白·戈德堡，贝萨尼·关和理查德·扎内大学科罗拉多州安舒茨医学院，医学-心脏病学系，科罗拉多州奥罗拉市，美国科罗拉多州戴夫·考兹大学安舒茨医学院，医学-医院医学系，科罗拉多州奥罗拉市，USAHAMILI PatelUCHealth Nursing Administration，科罗拉多州奥罗拉市，USAMY HassellAuthorsElizabeth GoldbergView作者出版物您也可以在中搜索这位作者。

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PubMed Google ScholarContributionsE.G. wrote the first draft of the manuscript. A.H. and H.P. obtained, analyzed, and interpreted the data. D.K., B.K., R.Z., and E.G. wrote and edited sections of the manuscript. All authors read and approved the final manuscript.Corresponding authorCorrespondence to.

PubMed谷歌学术贡献。G、 写了手稿的初稿。A、 H.和H.P.获得，分析和解释了数据。D、 。所有作者都阅读并批准了最终稿件。对应作者对应。

Elizabeth Goldberg.Ethics declarations

伊丽莎白·戈德堡（ElizabethGoldberg）。道德宣言

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Reprints and permissionsAbout this articleCite this articleGoldberg, E., Kao, D., Kwan, B. et al. UCHealth’s virtual health center: How Colorado’s largest health system creates and integrates technology into patient care.

转载和许可本文引用本文Goldberg，E.，Kao，D.，Kwan，B。等人。UCHealth的虚拟健康中心：科罗拉多州最大的卫生系统如何创建技术并将其整合到患者护理中。

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