BackgroundSynthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making.We developed Rayyan (http://rayyan.qcri.org), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan’s users and collected feedback through a built-in feature.ResultsPilot testing of Rayyan focused on usability, accuracy against manual methods, and the added value of the prediction feature. The “taster” review (273 records) allowed a quick overview of Rayyan for early comments on usability. The second review (1030 records) required several iterations to identify the previously identified 11 trials. The “suggestions” and “hints,” based on the “prediction model,” appeared as testing progressed beyond five included studies. Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements. The survey respondents reported 40% average time savings when using Rayyan compared to others tools, with 34% of the respondents reporting more than 50% time savings. In addition, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews to be the two most important features of Rayyan.As of November 2016, Rayyan users exceed 2000 from over 60 countries conducting hundreds of reviews totaling more than 1.6M citations. Feedback from users, obtained mostly through the app web site and a recent survey, has highlighted the ease in exploration of searches, the time saved, and simplicity in sharing and comparing include-exclude decisions. The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users.ConclusionsRayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.

We tackle the problem of automatically filtering studies while preparing Systematic Reviews (SRs) which normally entails manually inspecting thousands of studies to identify the few to be included. The problem is modeled as an imbalanced data classification task where the cost of misclassifying the minority class is higher than the cost of misclassifying the majority class. This work introduces a novel method for representing systematic reviews based not only on lexical features, but also utilizing word clustering and citation features. This novel representation is shown to outperform previously used features in representing systematic reviews, regardless of the classifier. Our work utilizes a random forest classifier with the novel features to accurately predict included studies with high recall. The parameters of the random forest are automatically configured using heuristics methods thus allowing us to provide a product that is usable in real scenarios. Experiments on a dataset containing 15 systematic reviews that were prepared by health care professionals show that our approach can achieve high recall while helping the SR author save time.Work done while all authors were at QCRI.

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