Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
BMC Med Inform Decis Mak. 2012 May 30;12:47. doi: 10.1186/1472-6947-12-47.
Clinical trials are the primary mechanism for advancing clinical care and evidenced-based practice, yet challenges with the recruitment of participants for such trials are widely recognized as a major barrier to these types of studies. Data warehouses (DW) store large amounts of heterogenous clinical data that can be used to enhance recruitment practices, but multiple challenges exist when using a data warehouse for such activities, due to the manner of collection, management, integration, analysis, and dissemination of the data. A critical step in leveraging the DW for recruitment purposes is being able to match trial eligibility criteria to discrete and semi-structured data types in the data warehouse, though trial eligibility criteria tend to be written without concern for their computability. We present the multi-modal evaluation of a web-based tool that can be used for pre-screening patients for clinical trial eligibility and assess the ability of this tool to be practically used for clinical research pre-screening and recruitment.
The study used a validation study, usability testing, and a heuristic evaluation to evaluate and characterize the operational characteristics of the software as well as human factors affecting its use.
Clinical trials from the Division of Cardiology and the Department of Family Medicine were used for this multi-modal evaluation, which included a validation study, usability study, and a heuristic evaluation. From the results of the validation study, the software demonstrated a positive predictive value (PPV) of 54.12% and 0.7%, respectively, and a negative predictive value (NPV) of 73.3% and 87.5%, respectively, for two types of clinical trials. Heuristic principles concerning error prevention and documentation were characterized as the major usability issues during the heuristic evaluation.
This software is intended to provide an initial list of eligible patients to a clinical study coordinators, which provides a starting point for further eligibility screening by the coordinator. Because this software has a high "rule in" ability, meaning that it is able to remove patients who are not eligible for the study, the use of an automated tool built to leverage an existing enterprise DW can be beneficial to determining eligibility and facilitating clinical trial recruitment through pre-screening. While the results of this study are promising, further refinement and study of this and related approaches to automated eligibility screening, including comparison to other approaches and stakeholder perceptions, are needed and future studies are planned to address these needs.
临床试验是推进临床护理和循证实践的主要机制,但广泛认识到参与者招募方面的挑战是此类研究的主要障碍。数据仓库 (DW) 存储大量异构临床数据,可用于增强招募实践,但由于数据的收集、管理、集成、分析和传播方式,使用数据仓库进行此类活动存在多个挑战。利用 DW 进行招募的关键步骤是能够将试验资格标准与数据仓库中的离散和半结构化数据类型匹配,尽管试验资格标准的编写往往不考虑其可计算性。我们展示了一种基于网络的工具的多模式评估,该工具可用于对临床试验资格进行初步筛选,并评估该工具在临床研究预筛选和招募中的实际使用能力。
该研究使用验证研究、可用性测试和启发式评估来评估和描述软件的操作特性以及影响其使用的人为因素。
该多模式评估使用了来自心脏病学系和家庭医学系的临床试验,包括验证研究、可用性研究和启发式评估。从验证研究的结果来看,该软件对两种类型的临床试验分别显示出阳性预测值 (PPV) 为 54.12%和 0.7%,阴性预测值 (NPV) 为 73.3%和 87.5%。启发式评估中,与错误预防和文档记录相关的启发式原则被认为是主要的可用性问题。
该软件旨在为临床研究协调员提供一份初步的合格患者名单,为协调员进一步进行资格筛选提供起点。由于该软件具有高“规则内”能力,即能够排除不符合研究条件的患者,因此利用现有的企业 DW 构建的自动化工具进行资格确定和通过预筛选促进临床试验招募是有益的。虽然这项研究的结果很有希望,但需要进一步改进和研究这种和其他自动化资格筛选方法,包括与其他方法和利益相关者的看法进行比较,并计划进行未来的研究来满足这些需求。