The most critical, costly, and time sensitive aspect of any eDiscovery matter is typically the review phase. Especially on large scale reviews, teams of contract attorneys need to be staffed, trained on review protocols, and quickly deployed to begin combing through potentially relevant document populations. The shift to remote managed review staffing post-pandemic has vastly expanded and elevated the pool of seasoned contract attorneys, while simultaneously accelerating the need to leverage technology for QC, monitoring and reporting purposes.
Coding decision overturn reporting has long been a powerful tool in most review manager’s arsenals to spot patterns, measure reviewer coding accuracy, and address work product inconsistencies on an individual reviewer level. Review team member work product has become even more important when employing Technology Assisted Review , where an individual’s coding call mistakes can quickly be magnified and reflected across an entire population with machine learning.
Overturn reporting can be a tedious, manual process, often performed on a weekly basis to avoid excessive professional service charges. Unfortunately, by the time coding issues are spotted and addressed with overturn reporting, it’s often too late. Reviewers need to be replaced or costly resources need to be brought on for additional QC ahead of production.
Vision AutoTracker provides real-time insights into team performance and enables data-informed staffing decisions. By using information collected within Relativity and surfacing it for evaluation, AutoTracker builds a team management function directly into the workspace to automate the process of tracking and reporting reviewer overturns.
Review Managers can use the tool as part of their talent acquisition process to identify skilled reviewers based on quantifiable metrics. The tool also aids in automating QC feedback to the Managed Review team by showing them a detailed list of overturned documents along with the reason for the overturn.
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