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How Data Analytics Can Help Identify Trends in Mass Tort Claims

How Data Analytics Can Help Identify Trends in Mass Tort Claims

Data science techniques enable us to tell powerful stories regarding mass tort claimants. But given the proliferation of data everywhere, quality over quantity rules in mass tort cases and claims adjudication. Data scientists must collect and use the right data and employ the right tools to visualize and interpolate them to reveal actionable insights.

We can use numbers to quantify elements of a mass tort case, enabling attorneys to speak more objectively about the facts of the case. Humans don’t like uncertainty and therefore, turn to data to provide more concrete context—to help us tell what is true, not what we want to say.

Applying data analytics to the mass tort field helps predict:

  • Valuation– How much will the settlement be worth if we win the lawsuit?
  • Estimation– what is the probability that we will win a particular case and that a claimant will get paid?

For example, in our work at Verus, data analysis is helping clients forecast which cases are likely to come to be compensated and when, as well as how to predict future cash flows.

Identifying trends, mining data to guide mass tort attorneys

When investigating data for trends in mass torts, we must:

  1. Slice, segment, and graph data in multiple ways to identify trends (is something happening with X medical device, among Y population or in Z region or both Y and Z?)
  2. Determine if the trends are real (using statistical methods to verify trends and to possibly drill down further, identify missing or inaccurate data, and verify what’s really happening)
  3. Create a visualization of the trends in formats that easily illustrate the findings (chart, map or graph) to depict what’s really going on and communicate it to our clients

Some areas in which data analytics is helping us identify trends are exposure geography, claimant demography, and—in a completely different realm— the effectiveness of law firm marketing.

For example, our statistical models used to predict asbestos filings, show a recent rise in lung cancer filings, while claim approvals are not declining. Data analytics applied to this trend will shed light on the reasons behind it—or pose additional questions that will require further data collection and study. Analytics may reveal:

  • That demographics are shifting to an older population or to a group that is responding more to personal injury marketing but without higher prevalence of disease or use of the product at issue.
  • That the underlying exposures are changing in some identifiable way with this claimant population (e.g., secondary claims from background exposures vs. primary work site exposure).
  • Reasons why claim geography is changing – early exposures cases were mostly in the Northeast and Rust Belt, but now we are seeing more in the Southeast and southern Midwest. Is this an industry trend due to relocation of plants/work sites?
  • Are law firms spending a higher percentage of their dollars in Southeast?

For any of these data insights, a deeper exploration of the numbers across criteria will help determine which trends are real and perhaps explain why.

At Verus, a lot of our data work is built around building predictive models and valuation models; examining epidemiological models along with the current inventory of claims to better predict current and future liabilities. By diving into analytics and presenting it visually, we can help our clients answer important questions about an emerging mass tort and support strategic decision making.

Data in and of itself are static—but understanding how to analyze and visualize those data is where the trends in mass tort litigation become actionable insights for our clients and their practices.

Director of Analytics

Mark works with Verus’ clients on analytics-based projects that range from research, to concept, implementation and success measurement. Mark’s extensive list of engagements include the design of analytical dashboards, the development of forecasting platforms … MORE

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