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

SCH：INT：AURA - 连接音频和无线电传感系统以改善家庭护理

• 批准号: 9928253
• 负责人: Shahriar Nirjon
• 金额: $ 20.08万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-10 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9928253
• 关键词: Adverse event; Aftercare; Algorithms; Awareness; Biological Markers; Cancer Patient; Caregiver Burden; Caregivers; Caring; Collection; Colorectal Cancer; Data; Development; Ensure; Family Caregiver; Feedback; Fostering; Future; Goals; Health; Health Care Costs; Health Status; Healthcare Systems; Home environment; Hospitals; Human Activities; Instruction; Internet; Link; Location; Malignant neoplasm of urinary bladder; Measures; Modeling; Monitor; Notification; Outcome; Patients; Phase; Population; Principal Investigator; Radio; Recording of previous events; Reporting; Self Care; Self Management; Series; Signal Transduction; Social Impacts; Strategic Planning; Symptoms; System; Technology; Training; Voice; Work; base; care systems; cost; digital ecosystem; doctoral student; high risk; hospital readmission; mHealth; multidisciplinary; natural language; oncology; open data; patient population; personalized health care; programs; response; sensor; web page

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.

从医院到家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 并降低可避免再入院的成本，使医疗保健系统负担负担。一种常见的做法 在技​​术辅助护理过渡中是通过网络培训和指导患者和家庭护理人员 以及基于手机的症状报告，生命体征监测和提供反馈的系统。 尽管有效，但这些系统具有三个主要局限性：（a）患者或护理人员必须积极地 测量并将数据输入到系统中 - 容易出错，主观且经常被遗忘； （b）网络或 基于移动的互动可能很麻烦，并且要求完成数据，知道健康 状态，或获得和响应警报需要键入并单击一系列表格/Web 页； （c）通过消息和通知的系统反馈通常无效，并且 未被注意。为了克服这些限制，该项目采用数据驱动的方法来开发 在家庭设置中，支持语音，上下文感知，处理后的自我护理系统。提出的系统 （a）使用高级WiFi无线电信号的人在家监视患者的特定活动 活动识别算法； （b）量身定制像亚马逊这样语音助手的自然语言反应 根据患者的位置，活动和健康病史的回声/Alexa； （c）自动数据输入和 报告以减轻患者或护理人员负担。该系统将部署在40个结直肠和膀胱中 癌症患者的房屋评估可用性，可行性和福利初步幅度。 该项目的总体目标与NLM 2017 - 2027年的10年战略计划保持一致。 通过自动集合，链接，策展和语音和WiFi无线电传感器数据的建模，此 项目将创建一个互连的数字生物标志物生态系统，以解释，影响和预测 护理过渡期间的健康结果。通过使用低成本的语音助手（不到$ 50）和无处不在 家庭wifi，它最大程度地提高了数据驱动健康对大众人口的传播和参与。 为了促进未来数据就绪的劳动力的发展，三位博士学位。学生将在此工作 多学科项目，将开发新的MHealth课程，开放科学实践将是 在项目的开发，部署和传播阶段应用。