7 ediscovery tips for mass tort litigation

Mass tort series // Part 2 of 4

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Mass tort and MDL cases are a different category of litigation. Not just bigger than standard commercial cases, but structurally different in ways that compound at every phase. The data volumes are larger. The timelines are longer. The coordination demands span firms, jurisdictions, and years. And the cost of getting ediscovery wrong — whether that's missing key documents, running over budget, or building a review structure that doesn't scale — follows the matter for its entire life.

Most ediscovery platforms were built for single-matter litigation. The workflows, the pricing models, the review tools — they work reasonably well when you're managing one case with one team. Mass tort breaks all of those assumptions. Here's how to approach ediscovery when the scale is fundamentally different.

Tip 1: Negotiate ESI protocols with proportionality in mind

In standard litigation, ESI protocol negotiations are often a formality. In mass tort and MDL cases, they're one of the most consequential ediscovery decisions — and the proportionality principle should be at the center of them.

The core idea is straightforward: the scope of ediscovery should be proportional to the needs of the case, weighing the importance of the issues, the amount in controversy, and the burden and cost of the discovery requested. In mass tort, where unchecked scope can generate terabytes of marginally relevant data and add years to a timeline, that principle has real teeth. Agreeing on reasonable scope at the outset — limiting discovery to what's genuinely necessary to litigate the core issues — keeps costs manageable and timelines realistic, while preventing the most important evidence from getting buried under everything else.

Both sides have an interest in this, even if their negotiating positions differ. Plaintiffs typically want broad defendant discovery to surface corporate knowledge and establish liability patterns across the MDL. Defendants typically push for narrow plaintiff-side discovery to limit exposure. But neither side benefits from a discovery process so sprawling that it consumes the litigation itself. A well-negotiated ESI protocol — one that defines custodians, date ranges, search terms, and production formats with specificity — gives both sides a manageable framework and reduces the likelihood of costly disputes down the road.

In MDL cases specifically, this negotiation happens within a procedural structure that adds complexity. Courts typically issue standing orders establishing baseline ediscovery requirements before the parties begin negotiating, and common benefit orders govern how discovery costs are shared across the plaintiff side. Understanding those orders, and the MDL-specific procedural landscape around them, is part of getting the ESI protocol right.

The Nextpoint services team can help firms develop and negotiate ESI protocols tailored to their matter — including MDL standing order compliance, proportionality analysis, and scope negotiations that hold up through the full life of the case.

Tip 2: Run early data assessment before review begins

Understanding the data before review starts is the single most effective thing a litigation team can do to keep mass tort ediscovery under control. Early data assessment, or EDA, means evaluating the shape, volume, and composition of your dataset at intake — before reviewers touch a single document — so you can make strategic decisions rather than reactive ones.

In practice, EDA answers questions like: How much data is there, really? What portion is likely relevant? Where are the date clusters? Which custodians are driving volume? Is a significant portion of the produced dataset duplicative of materials you already have from a prior related matter?

That last question is particularly important in mass tort. Defendant productions in related MDL matters often overlap substantially — the same corporate documents, regulatory filings, and internal communications often show up again in a new matter. Without EDA, firms pay to review documents they've effectively already reviewed.

Nextpoint's Discovery Analytics dashboard gives teams an interactive view of their data at intake: document counts broken down by criteria like file type, custodian, email author, and date range, all clickable to return the underlying documents. Rather than importing a dataset and diving in, teams can use Analytics to orient quickly, understand the composition of what they have, and make culling decisions before review begins.

Teams can also run search terms directly against the dataset to evaluate scope and negotiate ediscovery terms with opposing counsel from an informed position. For matters where the data volume warrants it, the Nextpoint services team can lead a full EDA engagement, applying advanced deduplication and overlap analysis to identify what's actually new and meaningful versus repeat materials or extra noise.

Tip 3: Collect and review in phases

In mass tort litigation, trying to collect and review everything at once is a path to budget overruns and missed deadlines. A phased approach — prioritizing the highest-impact materials first and expanding scope as the case develops — is both more manageable and more defensible.

Phase one typically focuses on the core corporate documents most likely to establish liability: internal communications about the product at issue, regulatory submissions, adverse event reports, and executive-level correspondence during the relevant period. This is the material that will drive early motion practice, bellwether trial strategy, and expert preparation. Getting it reviewed and organized first gives the litigation team something to work from while the broader review continues.

Subsequent phases can expand to additional custodians, broader date ranges, or plaintiff-side medical records as the theory of the case develops and the most important evidence from phase one shapes what to look for next.

Nextpoint's Analytics dashboard supports phased review as well as EDA. Teams can filter the dataset by custodian, file type, date range, or other metadata criteria to build targeted review sets rather than opening the full collection to the entire team at once. For firms running large distributed review teams, review sets can be assigned to specific individual reviewers, so each reviewer only sees their assigned documents — a meaningful organizational advantage when the team spans multiple firms and jurisdictions.

Tip 4: Build your review platform around the case

How you structure your ediscovery platform matters as much as which platform you use. In mass tort, where the review team may span dozens of attorneys across multiple firms and the case will evolve over years, setting up your review environment thoughtfully at the start pays dividends throughout.

That means thinking through the specific issues in your case and building your tagging and coding structure to reflect them — not just relevance and privilege, but the substantive issues that will drive case strategy. Develop tags for corporate knowledge, causation evidence, and regulatory conduct. Create custom codes for the key issues your bellwether trial theory depends on. Build folder structures that organize documents by plaintiff, by product phase, or by issue cluster, depending on what the case requires.

Nextpoint's review platform supports custom tags, codes, folders, and categories, along with review sets that can be assigned to specific team members. The flexibility matters in mass tort because the standard review template rarely fits. A talc case and an opioid case have different issue structures, different key custodians, and different theories of liability. The platform should reflect that, not impose a generic structure on top of it.

Tip 5: Build for data reuse from day one

Mass tort ediscovery data is not a single-use asset. The same medical literature, corporate documents, and expert materials that appear in one matter will surface again in related cases — sometimes years later, sometimes across dozens of individual plaintiff matters within the same MDL. Firms that treat each matter as a fresh start pay to collect, process, and review the same materials repeatedly.

The more effective approach is building your ediscovery database structure with reuse in mind from the start. That means maintaining master repositories of recurring materials — medical literature libraries, defendant document productions, core regulatory filings — that can be referenced across matters rather than re-imported each time. It also means thinking about how your tagging and coding structure from this matter might carry forward: issue tags that apply across related cases, custodian profiles that inform the next deposition, exhibit sets that become the foundation for the next bellwether.

This connects directly to how you organize spin-off databases for new matters within an ongoing MDL. Rather than standing up a new database from scratch when a tag-along action joins the litigation, firms with well-structured master repositories can bring new matters online with the core materials already in place.

Read part 1 of our mass tort blog series on data reuse →

Tip 6: Protect sensitive plaintiff data

Mass tort litigation handles some of the most sensitive personal information in legal practice. Medical records, prescription histories, injury documentation — in a matter with thousands of plaintiffs, the volume of sensitive data is substantial, and the jurisdictional variation in privacy requirements adds another layer of complexity. HIPAA governs medical records, but state privacy laws vary, and international plaintiffs may bring additional compliance requirements.

The protective order negotiated at the MDL level typically establishes confidentiality designations for sensitive plaintiff data, but the security of the platform holding that data matters independently of the legal framework around it. Firms should verify that their ediscovery platform meets official auditing standards, understands the data handling requirements specific to mass tort, and can demonstrate how sensitive materials are protected at rest and in transit.

Nextpoint is SOC 2 compliant and built for the security requirements of complex litigation. The Nextpoint services team can also advise on compliance requirements across jurisdictions and help firms navigate the data handling obligations specific to their matter.

One additional note on AI tools: the widespread adoption of AI for document review introduces a data privacy consideration that mass tort practices should address directly. General-purpose AI tools accessed through consumer interfaces should not be used with client data. Any AI applied to mass tort ediscovery should come from vendors with explicit data handling agreements and a clear understanding of legal data security requirements.

Tip 7: Plan for predictable costs from the start

eDiscovery costs in mass tort cases have a way of compounding quietly. Per-gigabyte pricing on data hosting means that every document you retain, every production you receive, and every related matter you add to the platform increases your monthly bill. Over a multi-year MDL with rolling plaintiff additions and multiple related matters, that adds up fast — and creates economic pressure to delete data that may still be needed for appeals, new bellwether plaintiffs, or tag-along actions joining the litigation later.

Choosing a platform like Nextpoint with unlimited data hosting removes that dynamic entirely. The cost of ediscovery becomes predictable from day one: a flat rate per user, regardless of how much data you add or how long the matter runs. That predictability matters for budgeting, client billing, and making rational decisions about what to retain. When data retention is free, firms keep what they should keep rather than deleting what they can't afford to store.

For mass tort practices managing multiple related matters simultaneously, the difference between per-gigabyte and per-user pricing offers more than just cost savings — it delivers a structural advantage as well. The pricing model shapes how you think about data, how you build your repositories, and ultimately how much institutional knowledge you carry forward from one matter to the next.

See how Nextpoint supports mass tort litigation

Mass tort ediscovery requires deliberate strategy to overcome its inherent logistical challenges. The decisions made at intake, from how you scope discovery to how you structure your review platform to how you price and retain your data, shape the entire trajectory of a matter that may run for years.

Getting those decisions right is easier when the underlying platform was built for this kind of work: unlimited data, flexible review tools, analytics that give you visibility before review begins, and a services team that can step in when the complexity demands it. That's what Nextpoint brings to mass tort litigation.

Frequently asked questions about mass tort ediscovery

What makes ediscovery in mass tort litigation different from standard cases? Mass tort ediscovery operates at a fundamentally different scale and under a different procedural structure. Cases are consolidated before a single federal judge through the MDL process, with discovery coordinated centrally by a Plaintiff Steering Committee rather than managed independently by each plaintiff firm. Data volumes are significantly larger, timelines stretch across years, and the coordination demands across lead counsel, liaison counsel, local counsel, and multiple firms add layers of complexity that standard ediscovery workflows aren't built for.

What is early data assessment (EDA) in mass tort litigation? Early data assessment is the process of evaluating the composition, volume, and structure of a dataset before document review begins. In mass tort cases, EDA helps litigation teams understand what they actually have — document counts by custodian, file type, and date range — so they can make strategic culling and prioritization decisions before reviewers engage. It also surfaces overlap between newly produced materials and datasets from prior related matters, which is common in MDL litigation and can significantly reduce redundant review.

What ESI protocol considerations are specific to MDL cases? MDL courts typically issue standing orders establishing baseline ediscovery requirements before party negotiations begin. Beyond those orders, ESI protocol negotiations in MDL cases must account for proportionality — agreeing on a discovery scope that's manageable for both sides and focused on what's genuinely necessary to litigate the core issues. Other MDL-specific considerations include the treatment of documents produced across multiple related matters, the handling of plaintiff fact sheets, deduplication standards for defendant productions, and cost-sharing under common benefit orders.

How should mass tort firms structure their document review platform? Review platforms in mass tort cases should be structured around the specific issues of the matter, not a generic template. That means building custom tags and codes for the substantive issues driving case strategy, folder structures organized by plaintiff, product phase, or issue cluster, and review sets assigned to specific team members so that large distributed review teams work from organized, targeted subsets rather than the full collection. The structure set up at the beginning of the matter shapes how efficiently the team can retrieve and use that work product as the case develops.

How can mass tort firms control ediscovery costs over multi-year matters? The most effective cost controls in mass tort ediscovery combine early data assessment to reduce the volume going into review, phased collection and review to prioritize high-value materials, deduplication across related matters to avoid reviewing the same documents repeatedly, and a platform with per-user rather than per-gigabyte pricing. Per-gigabyte pricing creates ongoing hosting costs that compound over multi-year matters and create economic pressure to delete data that may still be needed for appeals, new bellwether plaintiffs, or related cases. Platforms like Nextpoint, with unlimited data hosting at a flat per-user rate, remove that constraint and make long-term data retention economically viable.