The design and implementation of an open-source, data-driven cohort recruitment system: the Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN)
The design and implementation of an open-source, data-driven cohort recruitment system: the Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN)
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DOI：
10.1136/amiajnl-2011-000115
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发表时间：
2012-06-01
影响因子：
6.4
通讯作者：
Horvath, Monica M.
中科院分区：
文献类型：
--
作者：
Ferranti, Jeffrey M.;Gilbert, William;Horvath, Monica M.
目的未能达到研究对象招募目标是许多临床试验成功的重要障碍。健康信息技术的实施允许对队列识别和招募的数据进行回顾性分析，但很少有机构还利用实时流来支持这些活动。设计杜克医学已经部署了一个混合解决方案，即杜克综合受试者队列和招募研究网络（DISCERN），结合了回顾性仓库数据和前瞻性健康等级7（HL7）中包含的临床事件结果DISCERN每天分析12个项目的50多万条信息。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对回顾性和实时数据进行推理的独特能力提高了研究入组率，同时减少了完成招募相关任务所需的时间。作者引入了一个预配置的DISCERN功能作为用户的自助服务功能。限制DISCERN框架主要适用于同时使用HL7消息流和数据仓库的组织。更有效的招聘可能会加剧竞争的研究科目，和调查人员不舒服的新技术可能会发现自己处于竞争劣势，在recruitment.Conclusion DISCERN的混合框架，用于识别实时临床事件的HL7消息的补充，使用回顾性仓库数据的传统方法。当所需的临床数据可能无法加载到仓库中，因此必须在患者护理期间同时捕获时，DISCERN很有帮助。使用开放源码工具有助于以最低成本推广到其他机构。
Objective 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.Design 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.Results DISCERN analyzes more than 500 000 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.Limitations 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.Conclusion 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.