By now, legal teams facing discovery are aware of many of the common technology and technology-enabled workflows used to increase the efficiency of document review on a single matter. But as data volumes grow and legal budgets shrink, legal teams must begin to think beyond a “matter-by-matter” approach. They must start applying technology more innovatively to create efficiencies across matters to minimize the burden of repeatedly reviewing the same documents again and again.
Fortunately, many common technology-enabled review workflows (e.g., technology assisted review (TAR), advanced search guided by linguistic experts, and AI-powered review analytics) can help teams apply work product and insights from past matters to current and future matters. This not only saves time but also increases consistency and lowers the risk of inadvertent disclosures and cumbersome clawbacks.
The opportunity to reduce repeated review is quite large, both because the problem is rampant and the technology that can help solve it is underutilized. A 2022 survey by the ABA showed that “predictive coding” is the least common application of eDiscovery software, used by only one in five law firms. In fact, 73% of respondents said they don’t know what predictive coding is (we explain it below). As document review continues to grow in complexity, and budget and other constraints apply pressure from other directions, more organizations should consider taking advantage of everything that technology has to offer.
Repeated review is a large and familiar burden
Repeated review is baked into the status quo. Matters spanning multiple jurisdictions, civil litigations tied to government investigations, and matters involving the same or related IP are just a few examples in which the same documents could come up for review multiple times.
Instead of looking across matters holistically, legal teams often feel obligated to roll up their sleeves, lower their heads, and review the same documents all over again—even when relevancy overlaps and for categories of information that remain relatively static across matters (privilege, trade secret, personally identifiable information (PII), etc.).
This has obvious consequences for time and cost. The time invested on reviewing documents for privilege in a current matter, for example, becomes time saved on future matters involving those same documents.
Risk is a factor as well. A document classified as privileged, or that contains PII or another sensitive category, in one matter should be classified the same way in the next one. But without a record of past matters, attorneys start over from scratch each time, which opens the door to inconsistency. And while it’s certainly possible to undo the mistake of producing sensitive documents, it can be quite time-consuming and expensive.
Rejecting the status quo
While the burden and risks associated with repeated review are felt every day, few legal teams and professionals are searching for a solution. Those willing to look beyond the status quo, however, will see that repeated review isn’t actually necessary, at least not to the degree that it’s done today. We also find that the keys to reducing repeated review lie in technology that many teams already use or have access to.
Reusing work product from TAR and CAL workflows
TAR 1.0, TAR 2.0, and Continuous Active Learning (CAL) workflows use machine learning technology to search and classify documents based on human input and their own ability to learn and recognize patterns. This is called predictive coding and it’s most often used to prioritize responsive documents for human review.
The parameters for responsiveness change with the topics of each matter, so it’s not always possible to reuse those classifications on other matters. TAR and CAL tools can also be effective at making classifications around privilege, PII, and junk documents, which are not redefined from matter to matter. If a document was junk last time (say company logos attached to emails, blank attachments, etc.) it’s going to be junk this time too. Therefore, reusing these classifications made by technology on one matter can save legal teams even more time in the future.
Refining review with linguistic experts
Linguistic experts add an extra layer of nuance to document review technology that makes them more precise and effective at classification. They develop complex criteria, based on intricate rules of syntax and language, to search and identify documents in a more targeted way than TAR and CAL tools.
They can also help reduce repeated review by conducting bespoke searches informed by past matters. This process is more hands-on than using TAR and CAL tools; human linguists take lessons learned from one matter and incorporate them into their work on a related matter. It’s also more refined, so it can help in ways that TAR and CAL tools can’t.
Litigation related to off-label drug use offers a good example. A company might have multiple matters tied to different drugs, making relevance unique for each matter. In this scenario, linguistics experts can identify linguistic markers that show how sales reps communicated with healthcare providers within that company. Then when the next off-label document review project begins, documents with those identifiers can be segregated for faster review. In this way, work from linguistic experts in one matter can help improve efficiency and minimize first-level review work on new matters.
Apply learnings across matters using AI
Review tools built on AI can reduce repeated review by classifying documents based on how they were classified before. AI tools can act as a “central mind” across matters, using past decisions on company data to make highly precise classifications on new matters. The more matters the AI is used on, the more precise its classifications become. The beauty here is that it applies to any amount of overlap across matters. The AI will recognize any documents that it has reviewed previously and will resurface their past classifications.
Some AI tools can even retain the decision on past documents and associate it with a unique hash tag, so that it can tell reviewers how the same or similar documents were coded in previous matters—without the concern of over-retaining documents from past matters.
Curious to challenge your status quo?
TAR, AI, and other solutions can be invaluable parts of a legal team’s effort to curb repeated review — but they’re not the only part. In fact, the most important factor is a team’s mindset. It takes forethought and commitment to depart from the status quo, especially when it involves unfamiliar tools or strategies.
The benefits can be profound, and the road to achieving them may be more accessible than you think.
Find tips for starting small, as well as more information about how and why to address the burden of repeated review, in our deep dive on the subject.
About the Author
Sarah is an eDiscovery Evangelist and Proposal Content Strategist at Lighthouse. Before coming to Lighthouse, she worked for a decade as a practicing attorney at a global law firm, specializing in eDiscovery counseling and case management, data privacy, and information governance. At Lighthouse, she happily utilizes her eDiscovery expertise to help our clients understand and leverage the ever-changing world of legal technology and data governance. She is a problem solver and a collaborator and welcomes any chance to discuss customer pain points in eDiscovery. Sarah earned her B.A. in English from Penn State University and her J.D. from Delaware Law School.