The breadth and depth of data available to mass tort attorneys can provide valuable insights to guide decision making about their cases. Data science can play an important role even before litigation begins, for example in identifying potential claimant populations—and it continues to be an invaluable tool throughout the discovery process, through trial, settlement negotiation, and the eventual distribution of settlement proceeds.

Using Data to Reach Those Most Likely Impacted by a Mass Tort

Analyzing location, gender and age data sets can help mass tort lawyers determine where to concentrate outreach efforts in order to garner the attention of more injured parties and potentially increase the number of claimants who would possibly be eligible for compensation for their injuries.

For example, data science can improve the effectiveness of outreach programs by identifying the best channels—digital platforms, direct mail or traditional media ad buys—to reach those most likely to have claims. This can be done by leveraging data sets to 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 to target marketing and messaging through the best channels is likely to bring forth a larger and potentially more eligible group of claimants.

A great example of targeted outreach relates to the 3M Combat Arms earplugs litigation. Using publicly available information to identify states such as Florida and Texas with high concentrations of veterans, law firms were able to target their promotional activities to those regions specifically, and thus generate more leads per outreach dollar. Additionally, since this product was used within a specific timeframe by military personnel, it is possible to identify age cohorts that comprise the majority of potential claimants and funnel outreach data to the media channels, publications, and online platforms most likely to be used by this group.

Looking ahead on the mass tort horizon is the Juul e-cigarette litigation, in which data is once again guiding marketing efforts. Given that the injured parties are likely to be younger digital natives and "cord cutters," traditional outreach involving television commercials is far less effective; rather, more outreach is being conducted on social media, and even within the social media sphere, more money is being spent on platforms that appeal to younger Juul users.

Using Data to Identify Actionable Claims

Data can help mass tort firms both identify potential claimant populations that may have been left out of a mass tort, as well as more easily differentiate among potential claimants who respond to outreach programs. The goal is to identify those respondents who have the highest likelihood of being able to prove injury as early in the process as possible.

For example, it is widely known that asbestos causes mesothelioma and other forms of cancer, but asbestos in talcum powder has affected a very different demographic than those exposed to asbestos contained in products used in commercial and industrial settings. A thorough data analysis that looks at demographic and other environmental factors can identify and classify claimants appropriately as new and specific inclusion criteria emerge for different cases.

In another example, data analytics helped sort claimants in lawsuits brought against the makers of the diabetes medications Avandia and Actos. Plaintiffs in the Avandia lawsuits alleged that it had caused cardiac injuries, stroke and death. Plaintiffs in the Actos suits claimed that Actos caused them bladder cancer. However, there was confusion among some Actos users who thought they had taken Avandia. Analyzing prescription data helped law firms determine who took which drug and, therefore, which manufacturer to bring claims against. This avoided unnecessary expense that would have resulted from bringing suit against the wrong manufacturer, as well as the potential negative media attention of having "frivolous" lawsuits dismissed en masse.

Using Existing Data to Identify Additional Claims without Advertising

Data mining can help identify new causes of action for past and present clients of a law firm from existing records and making connections to other data sources. Using an earlier example, if a firm was hired by a particular client to file a suit against the maker of Avandia, the client's medical records for that claim may contain information about other prescription medications taken by that individual. Even if the firm did not specifically capture data on these other medications in the course of handling the Avandia claim, data mining techniques can extract this information from records and flag other drugs that may also have resulted in adverse events. Mining existing client records in this manner can leverage past outreach spending to identify new cases at much lower cost.

For example, some claimants who first retained legal counsel in the transvaginal mesh litigation were found to have hernia mesh in addition to or instead of transvaginal mesh. Firms that mined the records first obtained in the transvaginal mesh litigation for that information were able to identify past clients who could also make a claim against hernia mesh manufacturers, when that litigation began, and reach out to them directly without any additional advertising spend.

If you have well-collected data, it is possible to aggregate it and apply advanced analytics to find commonalities within certain populations defined along shared criteria, which may qualify a class or population as part of a new action.

An example of these commonalities was found in the IVC filter litigation where many of the claimants were also on an anti-coagulant medication. By culling that data from the medical records already obtained for claimants in the IVC filter claims, firms were able to identify clients who were potential claimants in the litigation against manufacturers of those drugs.

Using Data to Shape the Litigation

Analytics is also playing a role before cases are heard, with some judges asking for an initial census, which happened in the 3M earplugs case. The judge in the recent Juul litigation has indicated that he will require an initial census as well. The number of potential claims, claimant demographics, injury spread and specific severity can be determined from data sets being collected through the initial census process.

The initial census in the 3M earplugs litigation collects basic information about the claims (branch of the military in which he/she served, when the person used the Combat Arms earplugs and the nature of the injury if the earplugs were used). The collected data will be analyzed and used by the court in managing the litigation.

Using Data to Optimize Settlement Values

Having the relevant information about a claimant population is of critical importance in structuring settlements to ensure that those who have been injured are compensated fairly. In addition, by examining the structure and values of prior settlements, law firms can gain insights that enable them to better support enhanced settlement values in current litigation.

By examining the information available on a firm's current client claims, a thorough analysis can identify which claimants are not getting optimal settlement values from a proposed settlement structure and how those values might potentially be improved. Note that machine learning and clustering applications can identify claimant groupings much faster than before, and relying upon a regression analysis, firms can structure settlement matrices more optimally than previously possible. However, as helpful as machine learning and advanced analytics are in framing those settlements, humans are still required to step in and adjudicate how those dollars should be allocated.

Using Data to Streamline Internal Processes

Moving internally from claimants to a mass tort law firm's own operation, data analytics enhance process improvement, generate reports on real-time data, and help attorneys review and improve the accuracy of data and potential of cases for recovery.

One recommended process improvement is to use data from prior litigations to determine the optimal workflow for new matters. For instance, attorneys may be able to establish initial screening criteria to determine if claimants are likely to qualify for a particular litigation, potentially saving review costs and medical record expenses. Making those determinations as early as possible improves the ratio of successful outcomes to a law firm's costs.

Performance metrics can help law firms manage their staff by evaluating effectiveness of time spent and identifying areas for improvement.

It is crucial that the quality of existing data be reliable and complete, and that enough quality data was collected to begin with in order to build a reliable claimant database or create a more efficient process. Data analysis may end up revealing gaps in data quality, which subsequently drives process improvement and can shed light on where and how the firm can improve its data collection and operations. Stepping back and assessing the firm's overall processes, as well as using the data that arises from that assessment, will help the law firm identify opportunities for process improvement that could have long-term, positive effects on case management, reduce expenses, and can lead to a higher level of success for both the firm and the firm's clients.

Closing

Data analytics enables a law firm to identify potential claimants and possible additional claims, where efficiencies can be implemented, and how to structure and evaluate potential settlements. It can also help the law firm assess which of its processes have been effective and which have not. In short, harnessing the power of data analytics empowers law firms with the ability to increase the number of qualifying clients, claimant compensability and operational efficiency, while reducing administrative costs.

Deb Zonies is V.P. of Mass Tort Services and General Counsel, at Verus, LLC, a litigation support services firm based in Princeton. Robert Koehl is the Analytics Manager at Verus, LLC.