Want a better approach to mass tort case management? If you do, then Elizabeth Guthrie of Nextpoint has some tips for you here!

The article titled (wait for it!) Stop reinventing the wheel: A better approach to mass tort case management, available here) discusses how mass tort litigation is, by nature, repetitive work. A firm handling a new opioid case today will often face the same medical literature, expert witnesses, and opposing counsel they encountered in opioid litigation last year. The documents change, the plaintiffs change, but the underlying infrastructure of the case looks remarkably familiar.

Yet most firms build that infrastructure from scratch every time.

In an era where mass tort practices are managing hundreds of cases simultaneously across multiple jurisdictions — and where most platforms charge by the gigabyte — that redundancy becomes inefficient and expensive.

Gaining a competitive edge in mass tort doesn’t necessarily require more resources. Treating every closed matter as an asset is an invaluable strategy.

That starts with building master repositories: centralized libraries of materials that appear repeatedly across cases. Medical literature is the obvious starting point. In pharmaceutical mass tort, the same clinical studies, FDA correspondence, and scientific literature surface again and again across individual plaintiff matters. Housing these in a single searchable repository, rather than re-collecting and re-hosting it for each new case, means your team spends time analyzing documents rather than hunting for them.

I can certainly attest to the value of master repositories when managing multiple cases involving the same issues, witnesses and documents again and again.

So, what is the pricing reason why most firms don’t build master repositories on platforms and how does Nextpoint overcome that pricing concern? Find out here, it’s only one click. The info is in their blog master repository! 😉

So, what do you think? How do you manage discovery for mass tort cases? Please share any comments you might have or if you’d like to know more about a particular topic.

Image created using ChatGPT, using the term “robot lawyer disappointed about carving a wheel from stone when it sees another robot lawyer riding a bicycle”.

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