Project 1: Primary Care

摘要 支持技术的服务(TESS)，它使用基于Web的移动应用程序，这些应用程序由 高强度的教练或护理管理，已经显示出巨大的潜力，具有大量的随机性 对照试验始终显示出疗效。然而，实现这些的多次尝试都得到了验证 对基于价值的大型医疗系统的干预失败了。这些失败主要有两个原因。 首先，现实世界中的患者根本不使用在研究环境中开发的工具。 其次，TESS没有被设计成适合普通医学实践的工作流程。这项研究 项目将使用全面的以用户为中心的设计方法来吸引患者、护理经理(CMS) 和医生参与TES的设计-包括技术、CM服务协议和实施 计划-可以在家庭医学诊所的协作护理计划中成功部署。整体而言 TES将旨在支持现有的协作护理模式，促进正在进行的 患者的抑郁评估以及与开处方的医生的沟通。设计创新 该研究项目的重点将是设计一款简单、可用、有用、适合 人们生活的方方面面。我们将利用我们中心正在进行的个人感知方面的研究工作，这些研究使用 被动地从手机上收集数据，实时识别与抑郁相关的行为。到目前为止， 我们已经创建了算法，可以可靠地识别GPS移动模式、身体活动、访问过的位置、睡眠 模式，以及电话交流模式，所有这些都跟踪与抑郁有关的行为。这个项目 将设计患者界面，将这些感知信息以轻松的方式呈现给患者 可以理解，并推动人们增加积极的活动，减少导致抑郁的行为，以及 探索行为和情绪之间的关系。我们将使用行为激活框架来 干预方案的设计。TES还将包括一个CM仪表板，可用于查看 患者应用程序的使用，以及允许CM为 患者并与医生就药物治疗需求进行沟通。它的有效性和 工商业污水附加费的实施将在4家初级保健诊所开展的整群随机试验中进行评估 该项目将是第一个将新兴的个人感知功能整合到干预应用程序中的项目。 由此产生的TES有可能成为第一个可供真实世界的患者使用、适合临床工作流程的TES， 并可在全科医学协同护理项目中成功实施。

Abstract Technology-enabled services (TESs), which use web-based and mobile applications supported by low- intensity coaching or care-management, have shown great potential, with a large number of randomized controlled trials consistently demonstrating efficacy. However, the many attempts to implement these validated interventions into large value-based care systems have failed. There are two broad reasons for these failures. First, patients in real world settings simply do not use the tools that were developed in research settings. Second, TESs have not been designed to fit into the workflows in general medicine practices. This research project will use a comprehensive user centered design approach to engage patients, care managers (CMs) and physicians in the design of a TES—comprised of technologies, CM service protocol, and implementation plan—that can be successfully deployed in a collaborative care program in family medicine clinics. The overall TES will be designed to support the existing collaborative care model, facilitating the acquisition of ongoing depression assessments from patients and communication with prescribing physicians. The design innovation focus of this research project will be to design a patient app that is simple, usable, useful, and fits into the fabric of people’s lives. We will harness ongoing research efforts in our Center in personal sensing, which use passively collected data from mobile phones to identify behaviors relevant to depression in real time. To date, we have created algorithms that reliably identify GPS mobility patterns, physical activity, locations visited, sleep patterns, and in-phone communication patterns, all of which track behaviors related to depression. This project will design patient interfaces that can represent this sensed information to patients in ways that are easily understandable, and that nudge people to increase positive activities, decrease depressogenic behaviors, and explore the relationship between behaviors and mood. We will use a behavior activation framework for the design of the intervention protocol. The TES will also include a CM dashboard that provides visibility into patient app use, as well as communication tools that allow the CM to provide low intensity support to the patient and communicate with the physician around pharmacotherapy needs. The effectiveness and implementation of the TES will be evaluated in a roll out cluster randomized trial across 4 primary care clinics This project will be the first to integrate the emerging capabilities of personal sensing into intervention apps. The resulting TES has the potential to be the first that is usable by real world patients, fits into clinic workflows, and can be successfully implemented in a general medicine collaborative care program.