How to Solve Your Top Systematic Review Challenges

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Systematic reviews are complex, and research teams often struggle with managing all the moving parts efficiently. If you work in the evidence-based research field, chances are you’ll face these common challenges.

Logistics and Coordination

Systematic reviews require a great deal of coordination, communication, and collaboration - especially when research teams include remote contributors and thousands (or tens of thousands) of references.

“Our number one challenge was managing all the moving parts to get things done efficiently. Being able to track who has been doing what on a project can be really hard to capture.”

Caroline Patton, The American Society for Radiation Oncology (ASTRO)

Human Error

The old adage, “you’re only human” is true - humans make mistakes. Systematic reviews, however, must be extremely accurate and reliable. Mistakes like duplicate references, transcription errors, and accidental inclusions/exclusions can skew—or worse, invalidate—the results of a systematic review.

“The ‘human factor’ can be a huge challenge in screening abstracts.”

Renee Wilson, Research Associate, Evidence-based Practice Center at Johns Hopkins University

Lack of Time

The most common response to “What is your biggest literature review challenge” in our Survey on Literature Reviews was the amount of time it takes to complete systematic review tasks such as search, screening, and data extraction. The more time a review takes to complete, the more resources it consumes (and the faster it becomes out of date).

As the saying goes, “Ain’t nobody got time for all that!”

“Given the time and cost associated with the review process today, we need more efficient ways to conduct systematic reviews.”

Dr. Annette O’Connor, Professor of Epidemiology, College of Veterinary Medicine at Iowa State University

Searching For Solutions

For years, researchers have employed everything from spreadsheets to proprietary internal tools in an effort to automate and standardize their review processes. While helpful, these tools almost always fell short of delivering a comprehensive solution.

Spreadsheets, in particular, are difficult to manage when dealing with remote collaborators and don’t keep a record of changes, which makes detecting and addressing errors difficult. Internal tools require a constant allocation of resources for development and maintenance, which could be used more effectively elsewhere.

Technology to the Rescue

In just a few steps, a comprehensive literature review software solution helps review teams produce high quality, accurate work in a much more timely fashion. 

Web-based, multi-user tools allow team members to access projects anytime, anywhere, and monitor progress in real-time. Users can easily identify reviewer/data conflicts or show time spent on tasks by the review participants.

References can be uploaded into the software with simple drag-and-drop functionality, and reusable form libraries make building intelligent protocols faster and more efficient.

Literature review software offers handy features like built-in data validation and multiple choice questions, which can dramatically reduce the potential for errors compared to when data is entered manually.

By automating many of the manual processes involved in screening, data extraction, risk of bias assessments, and reporting, literature review software addresses the biggest challenges faced by systematic review teams today.

Better data, fewer errors, and dramatic increases in efficiency - it’s easy to see why the evidence-based research community is embracing literature review software in their work.



Susan Sykes
About the Author

Susan Sykes

Susan Sykes works hard to create and deliver content that the evidence-based research community will find interesting and useful. She is an experienced writer and marketing professional who loves finding ways to communicate in-depth, technical information so that it engages the reader. In her down time, she can often be found plotting her next home decor project.

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