Intelligent Sensor System for Early Illness Alerts in Senior Housing

慢性疾病管理是当今美国面临的最大的卫生保健问题。2005年，近1 / 2的美国人（1.33亿）至少患有一种慢性病，21%的人口患有多种慢性病。这些数字将在未来30年稳步增长。慢性病尤其影响老年人，他们的病情恶化会导致健康状况的急剧变化和下降、住院治疗、复杂的治疗干预和高昂的费用。疾病的早期识别和早期治疗不仅是慢性病或急性病加重后改善健康状况、迅速康复的关键，也是降低老年人发病率和死亡率、控制卫生保健费用的关键。我们建议以我们最近完成的试点干预研究（NINR R21, Rantz， PI）为基础。在那项研究中，我们为我们的智能传感器系统（不是测量传统生命体征、体重、脉搏血氧仪、血糖的远程医疗）开发了警报，并使用它前瞻性地测量老年人的功能能力，实际检测到慢性疾病或急性疾病的变化，平均比通常的评估方法或疾病自我报告早10天到2周。廉价的传感器嵌入在环境中，因此受试者不必“使用”或“佩戴”任何设备。R21的目的是：1)根据传感器数据开发警报，通知卫生保健提供者疾病或功能下降的早期迹象，以便他们进一步评估和干预早期治疗；2)进一步

DESCRIPTION: Chronic disease management is the biggest health care problem facing the United States today. In 2005, nearly 1 in 2 Americans (133 million) had at least one chronic condition, and 21% of the population had multiple chronic conditions. These numbers will steadily increase over the next 30 years. Chronic diseases especially affect older adults in whom exacerbations result in dramatic changes and decline in health status, hospitalization, complex treatment interventions, and high cost. Early illness recognition and early treatment is not only a key to improving health status with rapid recovery after an exacerbation of a chronic illness or acute illness, but also a key to reducing morbidity and mortality in older adults and controlling costs of health care. We propose to build on our recently completed pilot intervention study (NINR R21, Rantz, PI). In that study, we developed alerts for our intelligent sensor system (not telehealth that measures traditional vital signs, weight, pulse oximetry, blood sugar) and used it prospectively to measure functional ability in older adults and actually detected changes in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness. Inexpensive sensors are embedded in the environment, so subjects do not "have to use" or "wear" any devices. The R21 was to: 1) develop alerts based on the sensor data to notify health care providers of early signs of illness or functional decline so they could further evaluate and intervene with early treatment; 2) further develop and refine a web-based interface to display the sensor data to health care providers; and 3) determine the sample size for an intervention study that would measure the clinical effectiveness and cost-effectiveness of using the sensor system with alerts in elder housing. Now, we propose to conduct a prospective intervention study to measure the clinical effectiveness and cost effectiveness of using sensor data to detect early signs of illness or functional decline in older adults compared to usual health assessment. A larger sample of older adults will be recruited; they live in a different independent housing than where the pilot study was conducted. While preparing the staff in the different housing setting to work with alerts from our intelligent sensor system, we will adjust, if necessary, the algorithms for automated alerts or the web-based interface for health care providers. Following the prospective study, we will develop and refine ways of providing sensor information to older adults and informal caregivers to help them directly better manage changes in health status. Our intelligent sensor system enables early detection of illness or functional decline, the key to successful less invasive, time consuming, and expensive interventions. Helping older adults remain healthier, active, and control their chronic illnesses with early detection of changes in health status and early intervention by health care providers, can result in millions of people remaining independent as they age, avoiding or reducing debilitating and costly hospital stays, and for many, avoiding or delaying nursing home care. This application will be of interest to both NINR and NIA.