Decentralized clinical trials (DCTs) have the potential to increase pace and reach of recruitment as well as to improve sample representation, compared to traditional in-person clinical trials. However, concerns linger regarding data integrity in DCTs due to threats of fraud and sampling bias.
The purpose of this report is to describe two tools that we have developed and successfully implemented to combat these threats. Cheatblocker and QuotaConfig are two external modules that we have made publicly available within the REDCap data capture system to target fraud and sampling bias, respectively. We describe the modules, present two case examples in which we used the modules successfully, and discuss the potential impact of tools such as these on data integrity in DCTs. We situate this discussion within the broader landscape of translational science wherein we strive to improve research rigor and efficiency to maximize public health benefit.
