Unité Inserm U, IFR, Faculté de Médecine, University of Rennes, France.
Int J Med Inform. 2011 Jun;80(6):371-88. doi: 10.1016/j.ijmedinf.2011.02.003. Epub 2011 Apr 2.
(i) To review contributions and limitations of decision support systems for automatic recruitment of patients to clinical trials (Clinical Trial Recruitment Support Systems, CTRSS). (ii) To characterize the important features of this domain, the main classes of approach that have been used, and their advantages and disadvantages. (iii) To assess the effectiveness and potential of such systems in improving trial recruitment rates.
A systematic MESH keyword-based search of Pubmed, Embase, and Scholar Google for relevant CTRSS publications from January 1st 1998 to August 31st 2009 yielded 73 references, from which 33 relevant papers describing 28 distinct studies were chosen for review, based on their report of a novel decision support system for trial recruitment which reused already available patient data.
The reviewed papers were classified using a modified version of an existing taxonomy for clinical decision support systems, using 10 axes relevant to the trial recruitment domain.
It proved possible and useful to characterize CTRSS on a relatively small number of dimensions and a number of clear trends emerge from the study. Only nine papers reported a useful evaluation of the effectiveness of the system in terms of trial pre-inclusion or enrolment rate. While all the systems reviewed re-use structured and coded patient data none attempts the more difficult task of using unstructured patient notes to pre-screen for trial inclusion. Few studies address acceptance of systems by clinicians, or integration into clinical workflow, and there is little evidence of use of interoperability standards.
System design, scope, and assessment methodology vary significantly between papers, making it difficult to establish the impact of different approaches on recruitment rate. It is clear, however, that the pre-screening phase of trial recruitment is the most effective part of the process to address with CTRSS, that clinical workflow integration and clinician acceptance are critical for this class of decision support, and that the current trends in this field are towards generalization and scalability.
（i）综述自动招募临床试验患者的决策支持系统（临床试验招募支持系统，CTRSS）的贡献和局限性。（ii）描述该领域的重要特征、已使用的主要方法类别，及其优缺点。（iii）评估此类系统提高试验招募率的效果和潜力。
1998 年 1 月 1 日至 2009 年 8 月 31 日，通过系统地基于 MESH 关键词在 Pubmed、Embase 和 Scholar Google 上搜索，共获得 73 篇 CTRSS 相关文献，其中 33 篇相关论文描述了 28 项不同的研究，入选标准为报告了一种新颖的临床试验招募决策支持系统，该系统重复使用了已有的患者数据。
根据 10 个与试验招募领域相关的轴，对所审查的论文进行了一种改良版的现有临床决策支持系统分类法。
对 CTRSS 进行相对少量维度的特征描述是可行且有用的，并且从研究中出现了一些明显的趋势。只有 9 篇论文报告了对系统在试验预纳入或入组率方面的有效性进行了有用的评估。尽管所有被审查的系统都重复使用了结构化和编码的患者数据，但没有一个系统试图更困难的任务，即使用非结构化的患者记录来预先筛选试验纳入。很少有研究涉及系统被临床医生接受的情况，或融入临床工作流程的情况，并且几乎没有使用互操作性标准的证据。
各论文之间的系统设计、范围和评估方法差异很大，使得难以确定不同方法对招募率的影响。然而，很明显，试验招募的预筛选阶段是最适合使用 CTRSS 来解决的过程部分，临床工作流程的整合和临床医生的接受程度对于这类决策支持是至关重要的，而该领域的当前趋势是朝着推广和扩展的方向发展。
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