SCH: INT: AURA - Connecting Audio and Radio Sensing Systems to lmprove Care at Home

从医院到家庭的有效护理过渡对于确保高风险患者的最佳护理至关重要 并减少加重医疗保健系统负担的可避免的再入院的成本。一种常见的做法 在技术辅助护理过渡是通过网络培训和指导病人和家庭护理人员 以及用于症状报告、生命体征监测和提供反馈的基于移动的电话的系统。 尽管有效，但这些系统具有三个主要限制：（a）患者或护理者必须主动地 测量数据并将其输入系统--这是一个容易出错、主观且经常被遗忘的过程;（B）Web或 基于移动设备的交互可能很麻烦，要求也很高，比如输入数据、了解健康状况 状态，或获取和响应警报需要键入并单击一系列表单/Web （c）系统通过消息和通知提供的反馈往往是无效的， 因未得到人们注意为了克服这些限制，本项目采用数据驱动的方法来开发一个 在家庭环境中启用语音、感知环境、治疗后自我护理系统。所提出的系统 将（a）使用先进的基于WiFi无线电信号的人在家中监测患者的特定活动， 活动识别算法;（B）定制亚马逊等语音助手的自然语言响应 Echo/Alexa基于患者的位置、活动和健康历史;以及（c）自动化数据输入， 报告以减轻患者或护理人员的负担。该系统将部署在40个结肠直肠和膀胱 癌症患者的家，以评估可用性，可行性和初步的好处。 该项目的总体目标与NLM 2017-2027年的十年战略计划保持一致。 通过自动收集、链接、管理和建模语音和WiFi无线电传感器数据， 该项目将创建一个相互关联的数字生物标志物生态系统，解释，影响和预测 护理过渡期间的健康状况。通过使用低成本的语音助手（不到50美元）和无处不在的 家庭WiFi，它最大限度地传播和参与数据驱动的健康大众。 为了促进未来数据就绪劳动力的发展，三位博士学生将在此工作 多学科项目，一个新的mHealth课程将开发，开放的科学实践将 在项目的开发、部署和传播阶段应用。

An effective care transition from hospital to home is crucial to ensure optimal care for high-risk patients and to reduce the cost of avoidable readmissions that burdens the healthcare system. A common practice in technology-assisted care transition is to train and guide patients and family caregivers through the web and mobile phone-based systems for symptom reporting, vital signs monitoring, and providing feedback. Although effective, these systems have three major limitations: (a) the patient or caregiver has to actively measure and enter data into the system-which is error-prone, subjective, and often forgotten; (b) web or mobile-based interactions can be cumbersome and demanding-tasks like entering data, knowing health status, or getting and responding to an alert require typing and clicking through a series of forms/web pages; and (c) feedback from the system through messages and notifications is often ineffective and unnoticed. To overcome these limitations, this project employs a data-driven approach to develop a voice-enabled, context-aware, post-treatment self-care system in home settings. The proposed system will (a) monitor specific activities of a patient at home using advanced WiFi radio signal-based human activity recognition algorithms; (b) tailor natural language responses of voice assistants like Amazon Echo/Alexa based on the patient's location, activity, and health history; and (c) automate data-entry and reporting to reduce patient or caregiver burden. The system will be deployed in 40 colorectal and bladder cancer patients' homes for assessing usability, feasibility, and preliminary magnitude of benefits. The overarching goals of this project are aligned with the NLM's 10-year strategic plan for 2017-2027. Through automated collection, linking, curation, and modeling of voice and WiFi radio sensor data, this project will create an interconnected ecosystem of digital biomarkers that explain, influence, and predict health outcomes during care transition. By using low-cost voice assistants (less than $50) and ubiquitous home WiFi, it maximizes the dissemination and engagement of data-powered health to mass population. To foster the development of a data-ready workforce for the future, three Ph.D. students will work in this multidisciplinary project, a new mHealth course will be developed, and open science practices will be applied during the development, deployment, and dissemination phases of the project.