Case Study

DistillerSR’s labels and filters automate conditional workflows and build succinct reports.

Managing Large Citation Volumes

Prior to using labelling and filtering with DistillerSR, Chris Waters-Banker and her team were dealing with a large amount of evidence to produce Global Value Dossiers for clients. With as many as 40,000 citations, the screening process was arduous and time intensive. She refers to this period as being “label naïve.”

Today, Waters-Banker’s and the team at Medlior utilize DistillerSR’s filters and labels throughout their reviews, and their new “label enlightened” approach has helped save considerable time and effort in their systematic literature review (SLR) process.

“Filters provide the opportunity to manipulate references throughout the workflow, such as grouping and directing citations to specified forms for review. When filtering and labelling are used together, DistillerSR provides us with the ability to strategize our screening processes, refine as needed, and maintain a robust and uncompromised systematic approach to our reviews,” said Waters-Banker.

For example, although a researcher might be more interested in randomized controlled trials (RCTs), they may need to capture common clinical practices found in observational studies. However, these studies often come with a larger volume of evidence and can be of questionable quality. With DistillerSR’s labelling and filtering, Waters-Banker’s team can apply labels to “observational” studies and group them together. The increased volume can be presented to the client, who can determine whether to proceed further based on available budget. If they do not choose to move forward, the identified publications can be excluded by bulk with no additional work required.

Saving Time and Resources

Waters-Banker utilizes labels from the beginning of her projects. “By applying labels during the import process, you can upload multiple sets of reference files without the need for creating separate projects,” she said.

This is particularly helpful in cases where there are more than one research question and/or if the search strategy/methodology is different. This best practice saves time and effort for later stages of the review as well. Labels allow you to track citations through each level of screening and create filters directing citations to their appropriate SLR screening forms.

Filters are then used to assign batches of references to reviewers with subject matter expertise. DistillerSR also enables project managers to monitor the screening process closely to better allocate support and resources where needed.

Reducing Errors for Trusted Evidence

Medlior’s team is charged with providing the most up-to-date and accurate evidence to healthcare decision makers. The Medlior team is working with a high volume of data across multiple reviews at any given time. Errors and “the dreaded missed citations,” according to Waters-Banker, have high consequences.

“When dealing with large volumes of SLRs, filtering can improve the ability to cross-check citations early in the screening process. This mitigates the risk of missing citations and ultimately providing a better night’s sleep,” she said.

Embedded Best Practice Methodologies

DistillerSR not only helps Waters-Banker and her team refine their process with robust, repeatable methods, but it also helps improve the speed and accuracy with which they can complete SLRs.

“From a methodological perspective, the filtering feature in DistillerSR provides a robust and flexible approach, which allows for broader research questions to be answered in a systematic and reproducible way. From a consultancy perspective, the filtering feature in DistillerSR has increased our efficiency and resource utilization when paired with labelling.”

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