Fact Sheet

Faster and More Accurate Systematic Reviews

With more than 37.2 million citations in the Embase database and 850,000 new citations in Medline alone in 2018, systematic review professionals are struggling to incorporate the tsunami-like volume of literature into their timely reviews.

The manual way of doing systematic reviews often consists of sending emails, editing spreadsheets, attaching documents, and waiting for replies – all highly error prone. And when you factor in large projects and living reviews that need to be periodically updated, the task becomes even more daunting. For many, updates to reviews may require a significant amount of extra work under tight deadlines, potentially leading to more errors.

In addition, administrative coordination of managing review teams across multiple projects is time consuming, especially for global teams. The increasing number of remote collaborators and growing number of reviews to complete each year, make it difficult to produce work efficiently and cost-effectively. This undermines the quality, transparency, and reproducibility of systematic reviews, and detracts from time better spent conducting research, analysis, and disseminating evidence to stakeholders. In short, more hiring, more literature, and more training equates to higher costs.

“DistillerSR is easy to use, saves time, and is far superior to any other product we have used for screening.”
Renee Wilson - John Hopkins University Evidence-Based Research Center

Improved Review Quality and Speed through AI

AI-Enabled Systematic Reviews

DistillerSR brings together AI and intelligent workflows that automate the management of the review process to produce transparent, audit-ready, and compliant literature reviews faster and more cost effectively. For systematic review professionals working for government or non-governmental organizations, DistillerSR’s AI-enabled screening with error checking and deduplication accelerates the screening of large volumes of literature. A recent article in BMC’s Medical Research Methodology stated that DistillerSR’s AI reduced article screening burdens by as much as five person weeks on a single project. Some DistillerSR customers have also realized AI-enabled cuts to screening loads by as much as 90%.

Powered by natural language processing, DistillerSR’s AI learns from reviewer screening behavior to automatically re-order references by likelihood of relevance or inclusion. Custom classifiers are also available to identify and tag relevant references, which further enriches reviews and helps reviewers find what they need faster.

The quality of systematic reviews is a cornerstone of trusted evidence-based medicine. DistillerSR’s AI enhances the quality of reviews by identifying records that may have been incorrectly excluded. Reviewers can also use an AI simulation to identify records that may have been incorrectly included – all of which optimize the totality of the literature search and the accuracy of its screening.

Improved Review Quality and Speed through AI

Intelligent Workflows

Collaboration is also easy with DistillerSR. Remote teams can work on projects simultaneously with a comprehensive audit trail and version control to keep track of all changes in the review.

User and project metrics help teams easily monitor review progress and responses in real-time, while a cross-project dashboard lets users easily see and access all the work they have left to do. With DistillerSR’s optimized workflow, your process becomes more efficient at all levels of the review. Remote teams can collaborate easily with full transparency and reduced chances of human error compared to traditional “spreadsheet” methods.

Improved Review Quality and Speed through AI

Automated Reference Management

Supporting the growing volume of new scientific literature, DistillerSR has auto-alert literature import services for its LitConnect module through OvidConnect, EBSCOConnect, and OpenConnect. Reviewers configure auto-alerts with their referred data providers and automatically import literature into specific DistillerSR projects – allowing for the widest search for literature while reducing the time associated with conducting them.

For organizations using e-libraries, such as those for academic institutions and corporations, Custom OpenURL allows source literature and full text to be accessed directly during reviews. The central management of full text articles eliminates the need to import articles and reduces copyright compliance issues.

Along with the existing direct access to PubMed, PubMed Central, Article Galaxy, RightFind, and DOI.org, DistillerSR’s Custom OpenURL connectivity provides the widest range of reference sources in the industry. Once imported, the references can be deduplicated and automatically assigned to appropriate reviewers on your team. Reviewers then get notifications on new references to screen. Rather than conducting a large update and redoing your analysis, DistillerSR helps you continuously and automatically monitor for new literature throughout the review.

With DistillerSR’s optimized workflow, your systematic review process becomes more efficient at all levels. Continuous automatic updates keep reviews up-to-date and minimize the chances of finding “surprise” new references before publishing. Remote teams can collaborate easily with full transparency and can reduce chances of human error compared to traditional “spreadsheet” methods. To top it all off, AI can be implemented pragmatically to assist reviewers and complete reference screening and data extraction faster.

“Moving from individual, informal tracking to DistillerSR has saved us innumerable hours and costs. It gives us the tools and control we need to ensure thorough screening by analysts and clinicians at various locations.”
Nancy Sullivan - Research Analyst

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