Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina 27705, USA.
J Am Med Inform Assoc. 2012 Jun;19(e1):e68-75. doi: 10.1136/amiajnl-2011-000115. Epub 2011 Sep 23.
Failure to reach research subject recruitment goals is a significant impediment to the success of many clinical trials. Implementation of health-information technology has allowed retrospective analysis of data for cohort identification and recruitment, but few institutions have also leveraged real-time streams to support such activities.
Duke Medicine has deployed a hybrid solution, The Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN), that combines both retrospective warehouse data and clinical events contained in prospective Health Level 7 (HL7) messages to immediately alert study personnel of potential recruits as they become eligible.
DISCERN analyzes more than 500000 messages daily in service of 12 projects. Users may receive results via email, text pages, or on-demand reports. Preliminary results suggest DISCERN's unique ability to reason over both retrospective and real-time data increases study enrollment rates while reducing the time required to complete recruitment-related tasks. The authors have introduced a preconfigured DISCERN function as a self-service feature for users.
The DISCERN framework is adoptable primarily by organizations using both HL7 message streams and a data warehouse. More efficient recruitment may exacerbate competition for research subjects, and investigators uncomfortable with new technology may find themselves at a competitive disadvantage in recruitment.
DISCERN's hybrid framework for identifying real-time clinical events housed in HL7 messages complements the traditional approach of using retrospective warehoused data. DISCERN is helpful in instances when the required clinical data may not be loaded into the warehouse and thus must be captured contemporaneously during patient care. Use of an open-source tool supports generalizability to other institutions at minimal cost.
未能达到研究对象招募目标是许多临床试验成功的重大障碍。健康信息技术的实施允许对队列识别和招募数据进行回顾性分析，但很少有机构还利用实时流来支持此类活动。
杜克大学医学中心部署了一种混合解决方案，即杜克综合研究对象队列和入组研究网络（DISCERN），该方案结合了回顾性仓库数据和包含在前瞻性 HL7 消息中的临床事件，以便在研究人员符合条件时立即提醒他们潜在的招募对象。
DISCERN 每天分析超过 500000 条消息，为 12 个项目提供服务。用户可以通过电子邮件、文本页面或按需报告接收结果。初步结果表明，DISCERN 独特的能力可以对回顾性和实时数据进行推理，从而提高研究入组率，同时减少完成与入组相关任务所需的时间。作者已经引入了一个预配置的 DISCERN 功能作为用户的自助服务功能。
DISCERN 框架主要可被同时使用 HL7 消息流和数据仓库的组织采用。更有效的招募可能会加剧对研究对象的竞争，而对新技术感到不舒服的研究人员可能会在招募方面处于竞争劣势。
DISCERN 用于识别 HL7 消息中实时临床事件的混合框架补充了使用回顾性仓库数据的传统方法。当所需的临床数据未加载到仓库中，因此必须在患者护理期间实时捕获时，DISCERN 非常有用。使用开源工具可以以最低的成本支持在其他机构的推广。
