Curcin Vasa, Lim Choi Keung Sarah N, Danger Roxana, Rossiter James, Zhao Lei, Arvanitis Theodoros N
Department of Computing, Imperial College London, UK.
Stud Health Technol Inform. 2013;192:1223.
Detailed insight into the recruitment parameters of a clinical trial is crucial to interpretation of its results, and reasons for its success or failure. Such recruitment is increasingly done through specialized software tools, sometimes linked to Electronic Health Record (EHR) systems, enabling automated capture of audit logs. However, in the absence of shared semantic models underpinning these logs, gathered data remains insular and opaque. We propose a standardized syntactical representation to capture the provenance of the recruitment task, and ground it in CRIM, a variant of the established PCROM information model for research in primary care. The method has been successfully prototyped in the EU FP7 TRANSFoRm project, where the recruitment eligibility query module has been integrated with a provenance capture infrastructure, resulting in the full reproducibility of the study design process.
深入了解临床试验的招募参数对于解释其结果以及成败原因至关重要。如今,此类招募越来越多地通过专门的软件工具来完成,这些工具有时与电子健康记录(EHR)系统相连,能够自动捕获审计日志。然而,由于缺乏支撑这些日志的共享语义模型,所收集的数据仍然孤立且不透明。我们提出一种标准化的句法表示法来捕获招募任务的出处,并将其建立在CRIM之上,CRIM是用于初级保健研究的既定PCROM信息模型的一个变体。该方法已在欧盟第七框架计划TRANSFoRm项目中成功实现了原型,在该项目中,招募资格查询模块已与出处捕获基础设施集成,从而实现了研究设计过程的完全可重复性。