Usability and Accuracy of the SWIFT-ActiveScreener: Preliminary evaluation for use in clinical research

Abstract

AbstractSystematic reviews (SRs) employ standardized methodological processes for synthesizing empirical evidence to answer specific research questions. These processes include rigorous screening phases to determine eligibility of articles against strict inclusion and exclusion criteria. Despite these processes, SRs are a significant undertaking, and this type of research often necessitates extensive human resource requirements, especially when the scope of the review is large. Given the substantial resources and time commitment required, we investigated a way in which the screening process might be accelerated while maintaining high fidelity and adherence to SR processes. More recently, researchers have increasingly turned to artificial intelligence-based (AI) software to expedite the screening process. This paper evaluated the accuracy and usabiity of a novel, machine learning program, Sciome SWIFT-ActiveScreener (ActiveScreener) in a large SR of mental health outcomes following treatment for PTSD. ActiveScreener exceeded the expected 95% accuracy of the program to predict inclusion or exclusion of relevant articles, and was reported to be user friendly by both novice and seasoned screeners. Our results showed that ActiveScreener, when used appropriately, may save considerable time and human resources when performing SR.