Assessing the effectiveness of a digital platform to support the mental health of healthcare workers in the response and recovery phases of COVID-19

新型冠状病毒（COVID-19）从根本上改变了医疗保健的提供，并对 个人和社区的心理健康。医疗保健对心理健康的影响 在大流行病的各个阶段，工作人员特别紧张。这种影响 预计对所有人，特别是对受不成比例影响的任职人数不足的人来说， 少数民族和女性。迫切需要开发和测试可扩展的数字方法， 可以识别，解决和支持医疗保健工作者（HCW）的福祉随着时间的推移和超越 流行病HCW很忙碌，而且与获得心理健康支持有关的耻辱。我们寻求 利用数字平台，方便人们获取以福祉为重点的资源，并与 心理健康提供者通过真实的时间支持，使用分步护理模式。急性期 在大流行期间，我们的团队开发并在整个Penn Medicine卫生系统中实施了Penn Cobalt。 Cobalt是一个基于网络和短信的平台，允许HCW完成上下文调查， 远程进行基于证据的评估然后，个人被引导到量身定制的福祉 资源和适当的个人或团体心理保健。在短短7个月内， Cobalt拥有18，300名独立用户，并确定了111名报告自我伤害想法的HCW， 立即将这些人与心理健康提供者联系起来，以获得支持和评估。虽然钴 一直保持高参与度，并提高了卫生系统识别高风险个体的能力， 需要有动机的用户。个人必须“拉”照顾他们，并克服多重障碍， 从确定对心理健康支持的需求，到预约和参加预约。 我们 寻求评估主动参与个人的“推动式”护理模式（Cobalt+）是否可以 改善个人心理健康，对访问的满意度，减少影响劳动力的因素 生产力（如缺勤）。具体目标是通过随机对照试验， 与钴（常规治疗）相比，钴+对HCW抑郁/焦虑（主要结局）的有效性。 次要结果将包括福祉、对获得护理的满意度和工作措施 生产力将探索人种和性别的异质性治疗效应。此外，我们将努力 更好地了解获得精神卫生保健的看法和钴的有效性， 通过半结构化定性访谈，在HCW中进行钴。总的来说，该项目旨在解决 显著差距，并加强保健妇女获得和接受以心理健康和福祉为重点的资源， 在乎我们的目的是，这将使一个积极的文化的福祉和心理健康的支持， 在大流行的多个阶段，我们的医疗保健工作人员。我们的方法评估现有的数字 这些模型可以及时执行，并迅速扩大规模，供其他卫生系统使用。

The novel coronavirus (COVID-19) has radically changed the delivery of healthcare and significantly impacted the mental health of individuals and communities. The effect upon the mental health of the healthcare workforce has been particularly strained throughout the various phases of the pandemic. This impact is predicted to be chronic and pervasive for all and particularly for disproportionately affected underrepresented minority and female populations. There is a critical need to develop and test scalable digital approaches which can identify, address and support the well-being of healthcare workers (HCW) over time and beyond the pandemic. HCWs are busy and there is stigma associated with accessing mental health support. We seek to leverage digital platforms to facilitate ease of access to well-being focused resources and direct connection to mental health providers through real time support using a stepped model of care. During the acute phase of the pandemic, our team developed and implemented Penn Cobalt across the Penn Medicine health system. Cobalt is a web and text-message based platform which allows HCWs to complete contextual surveys and evidence-based assessments remotely on their own time. Individuals are then directed to tailored well-being resources and appropriate individual or group mental healthcare. Across Penn Medicine, and in just 7 months, Cobalt has had 18,300 unique users and has identified 111 HCWs reporting thoughts of self-harm and immediately connected those individuals with a mental health provider for support and evaluation. While Cobalt has had sustained high engagement and increased the health system's ability to identify high-risk individuals, it requires a motivated user. Individuals must “pull” care toward them and overcome multiple barriers ranging from identifying the need for mental health support, to making an appointment, and attending the appointment. We seek to evaluate whether a “pushed” model of care (Cobalt+), which proactively engages individuals, can improve individual mental health, satisfaction with access, and decrease factors which affect workforce productivity (e.g. absenteeism). The specific aims will evaluate, through a randomized controlled trial, the effectiveness of Cobalt+ on HCW depression/anxiety (primary outcome) compared with Cobalt (usual care). Secondary outcomes will include well-being, satisfaction with access to care, and measures of work productivity. Heterogeneous treatment effects will be explored for race and gender. Additionally, we will seek to better understand perceptions of access to mental health care and the effectiveness of Cobalt compared with Cobalt among HCWs through semi-structured qualitative interviews. Overall, this project seeks to address a significant gap and enhance HCW access and receipt of mental health and well-being focused resources and care. Our intent is that this will enable a proactive culture of well-being and mental health support for the healthcare workforce during the multiple phases of the pandemic. Our approach evaluates existing digital models which can be executed in a timely fashion and rapidly scaled for use across other health systems.