The Impact of Predictive Analytics on Personal Injury Firms

Predictive analytics technology is revolutionizing the way law firms handle client matters. Personal injury firms that collect fees from settlements stand to realize the most conclusive and quantifiable benefits.

The new generation of personal injury predictive analytics software empowers legal teams to make better decisions, operate more efficiently, and improve case outcomes. There is a strong argument to consider this technology as part of your firm’s AI adoption strategy.

Learn more about the advantages, use cases, and potential risks of this AI-powered technology below. We'll also introduce other AI-based innovations law firms are adopting, to drive business and case-related results.

What Is Predictive Analytics for Law Firms?

Predictive analytics is a method of forecasting outcomes that uses machine learning and algorithms to identify relationships between data points. Meteorologists use this process to forecast weather. AI-powered analysis of prior weather data reveals patterns, which are then applied to current circumstances to create forecasts for tomorrow or next week.  

In the same vein, predictive analytics tools allow legal teams to forecast case outcomes under different scenarios. The source data includes court records, statutes, judicial decisions, and client and case details. The resulting predictions can help lawyers allocate resources, set client expectations, identify workable case strategies, and improve case outcomes. 

How Does Predictive Analytics Impact Personal Injury Law Firms?

Personal injury firms that use predictive analytics effectively can be more targeted in the cases they accept and the strategies they use to ensure winning results. 

The technology’s high-level benefits include improved efficiency and happier clients. These outcomes result from better decision-making, more thorough research, and more precise resource allocation, along with streamlined processes and enhanced client service

Helps Firms Make Better Decisions 

AI-powered case predictions can help legal teams quickly understand likely outcomes for personal injury cases based on datasets too complex for humans to parse. Lawyers can use those predictions to inform various case-related decisions, including:

• Should we take this case? 
• Should we settle or litigate? 
• Will this motion prevail?

It is also worth noting that insurance companies are likely using predictive AI to understand their position on a case. For law firms, then, the adoption of legal predictive analytics may help them remain competitive.

Better Skip Tracing

Locating individuals via public information relies heavily on data processing. The underlying identification data is housed across many databases, often in forms that are not easily comparable. One source might have a single name field, for example, while another could have separate fields for first name and last name.

AI-powered systems can harmonize data discrepancies faster and more accurately than less sophisticated skip-tracing tools. This efficiency expedites the search for missing witnesses and defendants.

Enhanced Case Allocation

Law firms can use predictive data to estimate a case's fee potential. Understanding the fee potential of a personal injury matter, in turn, allows the team to staff the matter appropriately.

Streamlined Settlement Processes

Personal injury predictive analytics also streamline the settlement process. With an estimate of a likely settlement amount, legal teams can begin negotiations at a more realistic value. There will be less posturing involved, especially if both sides have estimated the final number before negotiating.

Enhanced Client Service and Satisfaction

AI-powered personal injury law tools can efficiently identify case risks and estimate outcomes. Legal teams can use that information to prep their clients, encourage realistic expectations, and establish trust. Transparent communication throughout the case minimizes surprises, which increases the likelihood that the final result will align with client expectations. 

Ultimately, routinely achieving results that meet client expectations strengthens the firm's reputation and long-term business performance.

Questions that Predictive Analytics Can Answer in Cases 

The predictive power of personal injury lawyer software can overhaul the way law firms approach client matters. While the exact capabilities depend on the application being used, legal teams can potentially ask and answer several questions that will shape the case strategy. Let's review some prompts to consider using with a premium, full-featured predictive data analytics tool.

“What Is the Likelihood of Winning This Case Based on Similar Historical Cases?” 

Your teams are already asking this question today, but the old way of answering it is manual and reliant on experience. Lawyers will do some research and check in with colleagues who have handled similar cases, for example.

An AI-powered analysis can take a wider view by considering a broader spectrum of case data—far more information than a human could analyze in a realistic timeframe.

“How Does the Severity of the Injury and Other Case Details Influence the Likelihood of Success?” 

The best legal data analytics applications should be able to analyze different scenarios quickly. Lawyers can use that functionality to assess the influence of specific details, such as injury severity, on the probable outcomes.

“Which Cases Should We Prioritize Based on the Potential Financial Return?” 

With a reliable process for predicting probable case outcomes, legal teams can make disciplined decisions about which cases to pursue and prioritize. This can greatly increase overall firm efficiency as the team prioritizes matters with the highest potential financial return.

 “How Should We Allocate our Legal Team Resources To Maximize Efficiency and Profitability?” 

Resource allocation is a challenge in any service business. Client demand for services can rise and fall while the firm strives to keep its headcount as steady as possible. A robust predictive analytics tool can help manage that balance.

To plan for efficient use of the firm's headcount, you might evaluate the firm's caseload and aggregate revenue potential, identify the active cases with the highest profit potential, and estimate the man-hours required to resolve open cases.

“What Are the Potential Risks and Challenges Associated With Taking on This Case?” 

By tapping into public case data for similar matters, a premium legal predictive analytics application should be able to identify risks and challenges. The firm's research team can also answer this question, but the analysis will take longer and potentially overlook relevant information.

“How Should We Communicate Potential Case Outcomes and Risks To Manage Client Expectations Effectively?”

Personal injury predictive analytics applications can identify probable case outcomes and risks that may arise. As importantly, these applications can document the conclusions with sources. Once your team reviews and verifies the source data, you will have research to share with clients that supports your opinion on the case. If the predictive tool also has generative capabilities, you could have it draft a client-friendly summary of the primary issues and potential outcomes.

Written summaries supported by research help ensure clients understand how their case is likely to progress.

“What Legal Arguments and Strategies Have Historically Been Successful in Cases Similar to This One?”

This is another question the firm's research team may already be tackling, albeit manually. Personal injury law tools with the right functionality can compile a list of successful arguments and strategies relevant to a case in minutes rather than hours.

“How Should We Approach Litigation Based on Predictive Models to Maximize Our Chances of Success?” 

Because predictive software works quickly, multiple litigation strategies can be researched and evaluated for one case. Manual research can be prohibitively inefficient.

“What Are the Projected Costs and Potential Revenues Associated with Handling This Case?”

Legal predictive analytics applications that have access to the firm's data can evaluate prior cases to project costs. The cost projection can then be compared to projected revenue, which would be calculated from a probable settlement range and the firm's fee percentage.

“Are There Emerging Trends or Patterns in Personal Injury Law That Could Affect the Outcome of This Case?”

This question is challenging to answer with manual research because it involves a search for unknown, but relevant factors. An AI application is better suited to answer broader, exploratory questions like this one for two reasons. One, AI can identify hidden connections that humans may overlook. Two, AI can process information very quickly. 

Even if the application identifies trends or patterns that are not entirely applicable, the analysis only takes a few minutes. The team member overseeing the process can easily reword the query to generate a more relevant response.

Concerns for Using Predictive Analytics in Legal Cases 

Predictive data analytics for law firms can unlock efficiencies, improved decision-making, better case outcomes, and more satisfied clients—but there are downsides. Lawyers must be aware of AI's shortcomings and the potential for ethical violations. 

Three areas of concern to manage are data quality, information accuracy, and client confidentiality.

Data Quality and Bias

AI-powered applications are only as good as the data that trains them and the data used to answer queries. If the source data is incomplete or biased, the application's output will also be incomplete or biased.

For that reason, legal teams must know how their predictive tools source data and how complete and current that data is. Any output should be carefully reviewed for bias before it is used to make case decisions or communicate with the client.

Interpretation and Information Accuracy

Predictive AI uses statistical analysis to forecast outcomes, but it does not predict the future with certainty. The accuracy of any predictions is heavily dependent on the prompt, the data analyzed, and the quality of the application. 

An AI-powered forecast, then, is not a final product. It is a starting point for legal researchers to verify and validate the information, assumptions, and interpretations that are informing the prediction.

Data Privacy and Client Confidentiality

Personal injury predictive analytics applications require case details to forecast outcomes. Sharing client details could be an ethical violation, depending on how the application manages and stores data. 

Before sharing any case-specific details with an application, read and understand the software's privacy and data handling policies. Verify that the application does not store or use query data for training or other purposes.

Other AI Use Cases for Personal Injury Law Firms 

Outside of predictive analytics, there are several other ways law firms can leverage AI and machine learning to create efficiencies and improve outcomes. Generative AI, which can create text, images, video, and audio, is rapidly being incorporated into several categories of legal software tools. You may want to consider some of these tools as part of your firm's AI adoption strategy.

1. Brief drafting software streamlines the drafting of appellate briefs, memoranda of law, and other complex legal documents.
2. Calendaring and docketing applications use rules to generate calendar notations for statutory deadlines and other case milestones automatically.
3. Contract drafting and review software suggests contract revisions based on approved contracts stored in its database.
4. Contract lifecycle management tools streamline the creation, negotiation, and management of contracts.
5. Law practice management (LPM) applications provide a framework for organizing and managing case files, tracking time, invoicing, accepting online payments, tracking leads, scheduling appointments, assigning tasks, and more. Premier LPM systems also have document automation features. These can include reusable templates for common documents such as personal injury demand letters. 

Standalone legal generative AI assistants can review documents, summarize cases or research, answer general questions, and more. Some of these applications can also draft documents, such as an AI medical chronology or an AI demand letter.

LPM CASEpeer incorporates many of the features noted above. Designed for personal injury practices, CASEpeer enhances efficiency by combining AI functionality plus expense tracking, case management, practice management, client experience, and reporting features into one comprehensive application.

CASEpeer's legal AI tools include automated text editing and document summaries. The CASEpeer text editor highlights suggestions within documents to change the style and tone, organize, and summarize. Team members can quickly review and accept or decline suggestions to improve a document in minutes. 

CASEpeer document summaries also create measurable efficiencies for legal teams. The feature recaps key concepts from any text in a few clicks, freeing up your team's time for higher-value tasks. 

To learn more about how CASEpeer can recover lost time in your personal injury practice, schedule a demo today.