What They Needed
A multinational pharmaceutical company sought to substantially decrease their overall ediscovery spend, as well as improve the speed and accuracy of their privilege review. The company was interested in finding ways to reuse prior work product from past legal matters to reduce review costs.
The company process for identifying potentially privileged documents lacked accuracy, resulting in extensive attorney review of privilege “false positives.” Over 90% of documents flagged for privileged review were ultimately determined to be not privileged. Additionally, the company’s privilege screen, which consisted of a list of search terms and attorney names, was not identifying many privileged documents.
The company also sought to improve the cross-matter consistency around how the documents were reviewed. They were particularly interested in reducing risk associated with the inadvertent production of sensitive and privileged company data. To solve these challenges, the company was looking to create efficiencies at scale.
How We Did It
To reduce cost while increasing review consistency, the Lighthouse analytics team deployed Prism. Prism is a proprietary big data analytics technology that uses AI to aggregate and analyze document data and previous attorney work product from prior legal matters. Prism allows companies to repurpose past work product, including privilege coding, to reduce review time and cost, as well as to improve coding consistency. The Lighthouse team proposed a proof of concept for the company, highlighting how Prism could help them achieve their goals.
Key data from 22 of the company’s past legal matters were ingested into Prism. This data included duplicate hash values, metadata, document text, production information, and attorney responsiveness and privilege/ redaction coding. Once entered, Prism’s algorithms ‘learned’ from this data to customize its recommendations. The Lighthouse team then applied Prism’s learnings to a separate large review matter to identify possible efficiencies.