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

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，耐心的依从性和统计分析。通过纸笔的传统数据收集是劳动密集型和不方便的，通常会导致丢失，嘈杂或无效的条目。监测患者的用药依从性始终是一个挑战。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。鉴于无线技术的普遍应用以及云计算和贝叶斯自适应统计在制药行业的快速出现，我们建议开发一个名为“基于便携式设备的云数据解决方案”（pDSC）的系统，用于实时数据收集，可扩展的数据存储，远程患者监测和贝叶斯自适应临床试验设计。pDSC使临床研究更快、更便宜、更有效。 该项目的第一阶段非常成功。开发了一个原型系统，并由潜在用户进行了测试。对于使用网络电话架构的个性化数据收集和分析，业务概念是高度可接受的。充分验证了将体域传感器网络与3G无线网络相结合用于收集行为、心理、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，研究了云中高性能贝叶斯计算的方法。 第二阶段的建议旨在全面开发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。技术开发将使我们能够成立一个合同研究组织（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，以帮助学术和制药行业的客户开发药物，以满足个性化治疗和以患者为中心的护理的目标。 公共卫生相关性：数据和分析的质量对于任何临床研究的成功至关重要。基于无线和互联网技术和贝叶斯自适应方法的解决方案将大大扩大数据解决方案在开发医药产品的容量和成本效益。它具有巨大的潜力，以满足下一代临床研究和医疗服务的个性化医疗，以病人为中心的服务，以预防为导向的护理的特点的需求。

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.