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. 2016 Dec 5;5(1):210.
doi: 10.1186/s13643-016-0384-4.

Rayyan-a web and mobile app for systematic reviews

Affiliations

Rayyan-a web and mobile app for systematic reviews

Mourad Ouzzani et al. Syst Rev. .

Abstract

Background: Synthesis 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.

Results: Pilot 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.

Conclusions: Rayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.

Keywords: Automation; Evidence-based medicine; Systematic reviews.

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Figures

Fig. 1
Fig. 1
Rayyan architecture. Rayyan is a fully cloud-based architecture that uses a cloud platform as a service allowing elastic scaling of resources as we get more users and more requests. Rayyan’s workers are distributed using the load balancer to different app servers (Ruby web workers). These workers are elastic; they auto-scale based on traffic to guarantee minimal response time. For longer jobs or the elastic delayed jobs (the worker bees), such as upload parsing, similarity computation, and label predictions, they are handled through a queuing system. All workers have access to the storage layers: Postgres (for permanent storage), Solr (for indexing and searching), and Memcached (for caching results). Other parts of Rayyan, written in Java, are attachable to the jobs using an Apache Thrift service. Real-time notifications, on job completion or chat messages, for example, are delivered using Pusher, while other transactional information are delivered using the Mailchimp Mandrill service. All system activities are logged by Logentries and later backed up on AWS S3, while live instrumentation and monitoring is done by NewRelic
Fig. 2
Fig. 2
Rayyan dashboard. The dashboard lists all reviews for this user as well as for each review the progress in terms of decisions made and estimated time spent working on the review for all collaborators
Fig. 3
Fig. 3
Rayyan workbench. The workbench shows the different ways users interact with the app
Fig. 4
Fig. 4
Similarity graph. Interacting with citations through the similarity graph
Fig. 5
Fig. 5
Reasons for exclusion. Users can select or add a reason for exclusion and exclude the study at the same time
Fig. 6
Fig. 6
Filtering by exclusion/inclusion decisions by author

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