Adams Meredith C B, Hudson Cody, Chen Wanchi, Hurley Robert W, Topaloglu Umit
Department of Anesthesiology, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, United States.
Department of Artificial Intelligence, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, United States.
JAMIA Open. 2025 May 24;8(3):ooaf036. doi: 10.1093/jamiaopen/ooaf036. eCollection 2025 Jun.
The main goal is to develop an automated process for connecting Research Electronic Data Capture (REDCap) instances in a clinical trial network to allow for deidentified transfer of research surveys to cloud computing data commons for discovery.
To automate the process of consolidating data from remote clinical trial sites into 1 dataset at the coordinating/storage site, we developed a Hypertext Preprocessor script that operates in tandem with a server-side scheduling system (eg, Cron) to set up practical data extraction schedules for each remote site.
The REDCap Application Programming Interface (API) Connection provides a novel implementation for automated synchronization between multiple REDCap instances across a distributed clinical trial network, enabling secure and efficient data transfer between study sites and coordination centers. Additionally, the protocol checker allows for automated reporting on conforming to planned data library protocols.
Working from a shared and accepted core library of REDCap surveys was critical to the success of this implementation. This model also facilitates Institutional Review Board (IRB) approvals because the coordinating center can designate which surveys and data elements to be transferred. Hence, protected health information can be transformed or withheld depending on the permission given by the IRB at the coordinating center level. For the NIH HEAL clinical trial networks, this unified data collection works toward the goal of creating a deidentified dataset for transfer to a Gen3 data commons.
We established several simple and research-relevant tools, REDCAP API Connection and REDCAP Protocol Check, to support the emerging needs of clinical trial networks with increased data harmonization complexity.
主要目标是开发一种自动化流程,用于连接临床试验网络中的研究电子数据采集(REDCap)实例,以便将去识别化的研究调查问卷传输到云计算数据共享平台以供发现。
为了将来自远程临床试验站点的数据整合到协调/存储站点的一个数据集中的过程自动化,我们开发了一个超文本预处理器脚本,该脚本与服务器端调度系统(如Cron)协同运行,为每个远程站点设置实际的数据提取计划。
REDCap应用程序编程接口(API)连接为跨分布式临床试验网络的多个REDCap实例之间的自动同步提供了一种新颖的实现方式,实现了研究站点和协调中心之间安全高效的数据传输。此外,协议检查器允许自动报告是否符合计划的数据库协议。
基于共享且被接受的REDCap调查问卷核心库开展工作对于该实施的成功至关重要。这种模式也便于机构审查委员会(IRB)的批准,因为协调中心可以指定要传输哪些调查问卷和数据元素。因此,可以根据协调中心层面IRB给予的许可对受保护的健康信息进行转换或保留。对于美国国立卫生研究院(NIH)的HEAL临床试验网络,这种统一的数据收集朝着创建一个去识别化数据集以传输到Gen3数据共享平台的目标努力。
我们建立了几个简单且与研究相关的工具,即REDCAP API连接和REDCAP协议检查,以支持临床试验网络日益增长的数据协调复杂性的新需求。