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Gesture Based Medication Adherence Confirmation for Clinical Trials

Gesture Based Medication Adherence Confirmation for Clinical Trials
临床试验中基于手势的药物依从性确认
批准号:
8199012
负责人:
Adam Hanina
金额:
$27.32万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-09-06 至 2013-03-05

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项目成果

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中文摘要
翻译
描述(由申请人提供):Ai Cure Technologies LLC成立于2009年,旨在为移动的设备和其他计算平台开发网络摄像头软件解决方案,以自动化并降低监测患者行为和药物依从性的成本。药物依从性差是医疗保健系统面临的最大挑战之一。每年有超过200万起严重不良事件和大约10万例死亡是由于这个问题。据估计,美国每年药物相关疾病(包括依从性差)的医疗保健总成本为2900亿美元。在临床试验中,慢性病患者的依从性水平从43%到78%不等,临床试验的高成本部分归因于依从性差造成的效率低下。随着临床试验规模越来越大,越来越多的临床试验转移到美国以外的地方,跟踪患者的行为变得越来越困难,FDA监控这些网站的负担也越来越重。传统的监测方法,如药丸计数,患者访谈和血液工作已被证明是不可靠的。事实上,最近一项采用这些传统监测方法的临床试验被证实因药物依从性差而失败。智能泡罩包装和MEMS瓶盖等产品价格昂贵,并且无法确认已服用药物。直接观察治疗是有效的,以确认药物治疗的依从性,但劳动密集型,病人的侵扰,和昂贵的。Ai Cure Technologies将提供一个网络摄像头软件解决方案,供临床试验申办者分发,以自动直接观察药物管理,并提供药物依从性的审计跟踪。该解决方案将为研究界和政策制定者提供可靠的数据,以改善整体健康结果并控制飙升的成本。该解决方案还将作为FDA在药物上市前更好地监管试验的工具。 公共卫生相关性:在临床试验中,参与者是否服用处方药以及服用到何种程度既不清楚,也不能通过现有和过时的方法(如药丸计数或患者访谈)进行可靠的监测。这意味着很难在规定的治疗方案和临床试验过程中对药物疗效或安全性进行准确评估。AiView将提供一个自动确定临床试验患者药物依从性的系统,并允许临床试验管理人员访问这些数据。
英文摘要
DESCRIPTION (provided by applicant): Ai Cure Technologies LLC, was established in 2009 to develop webcam software solutions for mobile devices and other computing platforms that automate and reduce the cost of monitoring patient behavior and medication adherence. Poor medication adherence is one of the healthcare system's greatest challenges. More than two million serious adverse events and about 100,000 deaths occur annually due to this problem. Total US healthcare costs of drug-related morbidity, including poor adherence, are estimated at $290 billion per year. In clinical trials, adherence levels for populations with chronic conditions range from 43% to 78%, the high cost of clinical trials being partly attributable to inefficiencies created by poor adherence. As clinical trials become larger, and more move outside the US, tracking patient behavior becomes more difficult, as does the burden on the FDA to monitor these sites. Traditional monitoring methods such as pill counting, patient interviews, and blood work have proven unreliable. Indeed, a recent clinical trial employing these traditional monitoring methods was confirmed to have failed for poor medication adherence. Products such as smart blister packs and MEMS caps are costly and do not confirm medication has been taken. Direct observation therapy is effective to confirm medication adherence; but is labor-intensive, patient intrusive, and expensive. Ai Cure Technologies will provide a webcam software solution for distribution by clinical trial sponsors to automate direct observation of medication administration and provide an audit trail of medication adherence. The solution will provide reliable data to the research community and policy-makers to improve overall health outcomes and rein in soaring costs. The solution will also act as a tool for the FDA to better regulate trials before drugs come to market. PUBLIC HEALTH RELEVANCE: In clinical trials, whether or not participants take their prescribed medication and to what degree is neither well understood nor reliably monitored through existing and antiquated methods such as pill counting or patient interviews. This means that it is difficult to provide an accurate assessment on drug efficacy or safety within a prescribed regimen and over the course of a clinical trial. AiView will provide a system for automatically determining medication adherence of clinical trial patients, and allow access to this data by clinical trial managers.
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海外基金