Fact Sheet

Intelligent Workflows and AI Accelerate Literature Reviews

Tasked with finding quality evidence quickly, health economics and outcomes research (HEOR) professionals are increasingly simplifying and automating their literature reviews, which have historically been managed by clunky processes and manual error-prone spreadsheets. For many HEOR organizations and departments, this increases the time to complete reviews and manage overall costs.

In addition, managing global review teams and the administration of multiple literature reviews can be time-consuming with workflows and processes that are cumbersome to run and replicate. Missed deadlines, faulty analysis, and omitted references are just a few problems that quickly surface without a standard platform to manage and coordinate literature reviews globally.

This is where DistillerSR comes in. DistillerSR brings together AI and intelligent workflows that can automate and simplify the management of researchers’ systematic collection of secondary data to produce transparent, audit-ready, and compliant literature reviews faster and more cost effectively. By automating much of this administrative work, you gain the confidence in the quality, efficiency, and cost-effectiveness of your literature reviews.

“Previous methods were cumbersome and timeconsuming. DistillerSR helped us grow our business and become more competitive. It made us more confident in our accuracy, and it made our quality control simpler. There would be a mutiny on our team if we decided to use something else.”
Kimberly Ruiz, Associate Director, Xcenda

Improved Review Quality and Speed through AI

Improved Review Quality and Speed through AI

DistillerSR uses powerful natural language processing to help reviewers find relevant records faster. DistillerSR’s AI runs in the background and continuously re-orders records based on the likelihood of relevance. This means that reviewers typically find all relevant references after reviewing only a small fraction of the total volume of literature. In short, DistillerSR’s AI tells you when you can stop screening.

Customizable AI classifiers, meanwhile, can be integrated into your workflow to tag and identify relevant references to further enrich literature reviews and help you find articles faster. As a result, you can simultaneously allocate review resources to other tasks while further screening occurs. DistillerSR’s automatic AI-enabled screening with error checking and deduplication accelerates the screening of literature references. 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%.

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

Improved Review Quality and Speed through AI

Intelligent Workflows Manage Global Teams

Many HEOR-based literature reviews have a lot of protocol changes. Very often, this results in re-reviewing, re-searching and re-checking references when protocols change. DistillerSR provides customers with a highly configurable, reusable workflow and project configurations. This saves countless hours from recreating manual review processes. Project and form templates, meanwhile, can be cloned and used numerous times, establishing standard review protocols corporate-wide.

HEOR teams can also use DistillerSR to work on the same project simultaneously, while team leaders monitor the progress of the review and reviewer responses in real-time. Cross-project dashboards, moreover, enable reviewers to easily see and access all the work they have left to do.

With intelligent and automated management of your literature process, you can feel confident about review quality with a transparent process that tracks all project and account activity.

Improved Review Quality and Speed through AI

Automated Literature Updates

Many global HEOR teams “follow the sun” to complete reviews around the clock. DistillerSR has auto-alert literature import services for its LitConnect module through OvidConnect, EBSCOConnect, and OpenConnect. Reviewers configure auto-alerts with their preferred data providers and automatically import literature into specific DistillerSR projects – allowing for the widest literature search while reducing the time associated with conducting them.

For organizations using e-libraries, such as those for corporations, Custom OpenURL allows source literature and full text to be accessed directly. 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. 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 new literature throughout the review.

With DistillerSR, HEOR professionals can confidently set up and monitor their literature review projects. In an industry where speed and accuracy are equally important, HEOR teams require tools to conduct reviews efficiently without error. Through DistillerSR’s AI and intelligent workflow automation, you save hours on manual administrative tasks and can focus more completely on your reviews’ evidence.

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Triangle HEOR HTA Vertical