Wyatt Matthew C, Hendrickson R Curtis, Ames Michael, Bondy Jessica, Ranauro Paul, English Thomas M, Bobitt Keith, Davidson Arthur, Houston Thomas K, Embi Peter J, Berner Eta S
Biomedical Informatics, Center for Clinical and Translational Science, University of Alabama at Birmingham, Suit 175 Sparks Building, 1720 7th Avenue South, Birmingham, AL 35233, United States.
Biomedical Informatics, Center for Clinical and Translational Science, University of Alabama at Birmingham, Suit 175 Sparks Building, 1720 7th Avenue South, Birmingham, AL 35233, United States.
J Biomed Inform. 2014 Dec;52:65-71. doi: 10.1016/j.jbi.2013.11.009. Epub 2013 Dec 4.
Cross-institutional data sharing for cohort discovery is critical to enabling future research. While particularly useful in rare diseases, the ability to target enrollment and to determine if an institution has a sufficient number of patients is valuable in all research, particularly in the initiation of projects and collaborations. An optimal technology solution would work with any source database with minimal resource investment for deployment and would meet all necessary security and confidentiality requirements of participating organizations. We describe a platform-neutral reference implementation to meet these requirements: the Federated Aggregate Cohort Estimator (FACE). FACE was developed and implemented through a collaboration of The University of Alabama at Birmingham (UAB), The Ohio State University (OSU), the University of Massachusetts Medical School (UMMS), and the Denver Health and Hospital Authority (DHHA) a clinical affiliate of the Colorado Clinical and Translational Sciences Institute. The reference implementation of FACE federated diverse SQL data sources and an i2b2 instance to estimate combined research subject availability from three institutions. It used easily-deployed virtual machines and addressed privacy and security concerns for data sharing.
跨机构数据共享以发现队列对于推动未来研究至关重要。虽然在罕见病研究中特别有用,但确定招募对象以及判断一个机构是否有足够数量患者的能力在所有研究中都很有价值,尤其是在项目启动和合作方面。一个最佳的技术解决方案应能与任何源数据库协同工作,在部署时资源投入最少,并能满足参与组织的所有必要安全和保密要求。我们描述了一个满足这些要求的平台中立参考实现:联合聚合队列估计器(FACE)。FACE是通过阿拉巴马大学伯明翰分校(UAB)、俄亥俄州立大学(OSU)、马萨诸塞大学医学院(UMMS)以及丹佛健康与医院管理局(DHHA,科罗拉多临床与转化科学研究所的临床附属机构)的合作开发并实施的。FACE的参考实现联合了多种SQL数据源和一个i2b2实例,以估计来自三个机构的联合研究对象可用性。它使用易于部署的虚拟机,并解决了数据共享中的隐私和安全问题。