Use Mobile Devices and Cloud Computing Platforms for Real-time Data Solutions in

使用移动设备和云计算平台实现实时数据解决方案

• 批准号: 8203500
• 负责人: Edward Lin
• 金额: $ 56.01万
• 依托单位: BAYESSOFT, INC.
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-03-15 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8203500
• 关键词: Adverse event; Architecture; Area; Behavioral; Blinded; Businesses; Caring; Case Report Form; Client; Clinical Research; Clinical Trials Design; Cohort Studies; Comb animal structure; Computer software; Conduct Clinical Trials; Data; Data Analyses; Data Base Management; Data Collection; Data Quality; Data Storage and Retrieval; Databases; Development; Devices; Drug Combinations; Drug Industry; Electronics; Feedback; Glosso-Sterandryl; Goals; Guidelines; Home environment; Industry; Information Systems; Intention; International; Internet; Marketing; Medical; Medical Research; Medicine; Methods; Modeling; Outsourcing; Paper; Patient Monitoring; Patient Self-Report; Patient-Centered Care; Patients; Performance; Pharmacologic Substance; Phase; Physiological; Policies; Procedures; Process; Productivity; Protocols documentation; Questionnaires; Reporting; Research; Research Contracts; Running; Secure; Security; Services; Signal Transduction; Software Tools; Solutions; Speed; Statistical Methods; Surveys; System; Tablets; Technology; Telephone; Testing; Time; Travel; Wireless Technology; base; commercialization; compliance behavior; cost; cost effective; cost effectiveness; data management; design; drug development; electronic data; epidemiology study; experience; flexibility; health care delivery; improved; innovation; medication compliance; meetings; next generation; novel therapeutics; patient oriented; pressure; prevention service; prototype; psychologic; psychosocial; research and development; sensor; statistics; success; translational medicine; wireless network

DESCRIPTION (provided by applicant): The success of drug development and clinical research is heavily dependent on the quality of data, patience compliance, and statistical analysis. Traditional data collection via paper-pencil is labor- intensive and inconvenient, often leading to missing, noisy or invalid entries. There is always a challenge to monitor patients' medication compliance. When conducting analyses, traditional statistical methods lack of flexibility to make faster decisions. Seeing the ubiquitous application of wireless technologies and the quick emergence of Cloud Computing and Bayesian adaptive statistics in the pharmaceutical industry, we propose to develop a system called 'portable-devices based Data Solutions over the Cloud' (pDSC) for real-time data collection, scalable data storage, remote patient monitoring, and Bayesian adaptive clinical trial design. pDSC enables faster, cheaper, and effective clinical studies. Phase I of this project was highly successful. A prototype system was developed and tested by potential users. The business concept was highly acceptable for personalized data collection and analysis using the web-phone architecture. The feasibility of combing body area sensor networks with 3G wireless networks for collecting behavioral, psychosocial, geo-spatial and physiological data was fully verified. The protocols between iPhone and the web applications were developed and the method of high-performance Bayesian computation in the cloud was investigated. This Phase II proposal aims to fully develop the pDSC system to use smart phones and other portable devices for real-time data collection and patient monitoring. pDSC will be developed over the Windows Azure cloud platform with secure and scalable data storage mechanisms and interfaces to access other software tools for Bayesian adaptive design and interim analysis. The technical development will enable us to form a contracted research organization (CRO). It will offer cost-effective data services, fully complying with FDA guidelines, to help academic and pharmaceutical industry clients develop medicines to meet the goal of personalized treatment and patient-centered care. PUBLIC HEALTH RELEVANCE: Quality of data and analysis is crucial for the success of any clinical research. The proposed solution based on wireless and Internet technologies and Bayesian adaptive methods will greatly enlarge the capacity and cost-effectiveness of data solutions in developing pharmaceutical products. It has great potential to meet the needs of next generation clinical research and healthcare delivery featured by personalized medicine, patient-centered service, and prevention-oriented care.

描述（由申请人提供）：药物开发和临床研究的成功在很大程度上取决于数据的质量、耐心、依从性和统计分析。传统的纸笔数据采集是劳动密集型的，而且不方便，经常导致丢失、嘈杂或无效的条目。监测患者的药物依从性总是一个挑战。在进行分析时，传统的统计方法缺乏灵活性，无法更快地做出决策。鉴于无线技术的广泛应用以及云计算和贝叶斯自适应统计在制药行业的迅速兴起，我们建议开发一个名为“基于云的便携式设备数据解决方案”（pDSC）的系统，用于实时数据收集、可扩展数据存储、远程患者监测和贝叶斯自适应临床试验设计。pDSC使更快，更便宜，更有效的临床研究。该项目的第一期非常成功。开发了一个原型系统，并由潜在用户进行了测试。该业务概念对于使用网络电话架构的个性化数据收集和分析是高度可接受的。充分验证了身体区域传感器网络与3G无线网络结合采集行为、心理社会、地理空间和生理数据的可行性。开发了iPhone与web应用程序之间的协议，研究了云环境下高性能贝叶斯计算的方法。该II期提案旨在全面开发pDSC系统，使用智能手机和其他便携式设备进行实时数据收集和患者监测。pDSC将在Windows Azure云平台上开发，具有安全和可扩展的数据存储机制和接口，可以访问其他软件工具，用于贝叶斯自适应设计和中期分析。技术的发展将使我们能够组建一个合同研究组织（CRO）。它将提供具有成本效益的数据服务，完全符合FDA的指导方针，帮助学术和制药行业客户开发药物，以实现个性化治疗和以患者为中心的护理目标。