Applying Data Analytics to Mass Torts | Part 1: Creating a Larger Claimant Pool

by | Dec 10, 2019

Data science is being applied across many industries and professional fields, including the law. In the case of mass torts, data provide valuable insights that guide mass tort attorneys’ decision-making in ways that build practices and streamline workflow.

In this first of a two-part article, we’ll look at how data analytics can help mass tort firms increase their claimant pool and maximize /outreach dollars to do so.

Identifying Actionable Claims

Data analysis can help mass tort firms identify potential claimant populations that may have been overlooked, and more easily differentiate among potential claimants who respond to outreach programs. It also delivers efficiencies by identifying respondents with the highest likelihood of being able to prove injury as early in the process as possible. Two examples are victims of asbestos related cancers and people suffering adverse events related to the diabetes medications Avandia and Actos.

  • While it is widely known that asbestos causes mesothelioma and other forms of cancer, asbestos contamination in talcum powder has affected a very different demographic than people exposed to asbestos in commercial and industrial settings. A thorough data analysis of demographic, environmental and other factors can identify and classify claimants appropriately as new and specific inclusion criteria emerge for an exposed population that was previously not contemplated.
  • Plaintiffs in the Avandia and Actos lawsuits each had very different medical injury claims. However, some Actos users thought they had taken Avandia. By analyzing prescription data, law firms determined who took which drug, clarified the manufacturer against which claims should be brought, and avoided unnecessary expense and the potential negative media attention of having a block of “frivolous” lawsuits dismissed.

Improving Outreach Efforts and Spend

Analyzing data sets can help mass tort lawyers potentially increase the number of claimants who could possibly be eligible for compensation for their injuries. Datasets can identify the media consumption and communication habits of the affected population based on age, sex, location, occupation, purchasing habits, and many other characteristics. Using such data helps determine where to concentrate outreach efforts and the best channels for reaching more injured parties—and bring forth a larger group of eligible claimants. For example:

  • 3M Combat Arms earplugs litigation – Using publicly available information to identify states with high concentrations of veterans, law firms were able to target their promotional activities to those regions and generate more leads per outreach dollar. Further, data mining can identify age cohorts that comprise the majority of potential claimants to refine selection of the media channels most likely to be used by this group.
  • Juul e-cigarette litigation – Data is guiding marketing efforts to concentrate outreach and money spent on social media platforms that appeal to younger Juul users, rather than traditional media outreach channels

Using Existing Data to Identify Additional Claims

Data mining can help identify new causes of action for a law firm’s past and present clients from existing records and make connections to other data sources. Mining existing client records can also leverage past outreach spending to identify new cases at much lower cost.

  • In the transvaginal mesh litigation, some claimants who first retained legal counsel were found to have hernia mesh in addition to or instead of transvaginal mesh. Mining the records first obtained in the transvaginal mesh litigation enabled firms to a) identify past clients who formed a new claimant pool—those who could also make a claim against hernia mesh manufacturers when that litigation began; and b) reach out to them directly without any additional advertising spend.
  • Aggregating and applying advanced analytics to well-collected data can reveal commonalities within certain populations along shared criteria, which may qualify a class or population as part of a new action. This happened in the IVC filter litigation, in which many of the claimants were also on an anti-coagulant medication. Again, culling that data from client medical records already obtained enabled firms to identify those who were potential claimants in the litigation against the anti-coagulant drug manufacturers.

Read Part 2 of this blog post

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