BarbriSFCourseDetails
  • videocam Live Webinar with Live Q&A
  • calendar_month March 16, 2026 @ 1:00 PM E.T.
  • signal_cellular_alt Intermediate
  • card_travel Personal Injury and Med Mal
  • schedule 90 minutes

MDL Case Management and Meritless Claims: Novel Application of New Rule 16.1 and AI Tools

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$197.00
$167.45
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Discount expires 2/19/26

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About the Course

Introduction

This CLE webinar will review the practical application of new Federal Rule of Civil Procedure 16.1 in recent multi-district litigation (MDL) to distinguish and dismiss meritless claims as well as to better manage discovery. The speakers will address using predictive analytics and artificial intelligence (AI) for converting the massive amounts of data generated in MDL cases into actionable intelligence that can be used to forecast settlement ranges, model individual damages, predict future injuries and damages, and create a roadmap to settlement or litigation strategy.

Description

Rule 16.1 provides a supplemental procedural framework to manage MDL cases, which make up 50% to 70% of all federal lawsuits—as of Jan. 5, 2026, 197,965 individual cases pending in 158 MDLs. Because several key procedures in Rule 16.1 are discretionary, some questioned its usefulness. That skepticism has been allayed by the court's approach in In re: Depo-Provera Products Liability Litigation, (No. 3:25-mdl-3140 (N.D. Fla.)), which offers an early case study in best practices under Rule 16.1 and how to get them implemented.

In Depo-Provera, the plaintiffs have been required to submit early proof of product use and injury with supporting documents. An MDL data administrator deploys a properly trained and supervised AI-enabled platform to review this data and tag potential deficiencies so that claims can be corrected or dismissed. From there, both plaintiffs and defendants can use their own tools to summarize the massive documentation generated by these cases; spot strengths and weaknesses, patterns, or inconsistencies; inform discovery and case strategy; identify best cases for bellwether trials; and more. No longer is this type of analysis only available for the few. 

The increased use of computerized analysis by way of machine learning or agentic AI means that the parties need to anticipate the use of these tools and how to protect work product and confidential information in the subject case as well as in future cases.  

Listen as this esteemed panel offers insight on best practices for managing and using the enormous amounts of data generated in MDL litigation to assess risk and structure strategy to achieve desired outcomes at trial.

Presented By

Daniel T. Campbell
Partner
Crowell & Moring LLP

Mr. Campbell has over 25 years of experience representing clients in their biggest litigations and guiding clients through their most significant disputes, working with corporations and organizations facing product liability, commercial, or contractual disputes throughout the United States. In product matters, he serves as national coordinating counsel, strategic counsel, and lead trial counsel for companies of all sizes on product liability, warranty, and other personal injury and property damage claims. Mr. Campbell's tort and product liability experience spans various industries, including automotive, medical device, railroad, and aviation, and his clients include product manufacturers, distributors and retailers. He litigates complex insurance coverage disputes, as well as complex contract, commercial litigation and arbitration matters for product manufacturers, service providers, and government contractors.

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Emily Tucker
Counsel
Crowell & Moring LLP

Ms. Tucker represents clients in a wide range of product liability matters, including in multidistrict litigation and class actions. Her work encompasses pharmaceutical and medical device matters impacting all members of the pharmaceutical supply chain, as well as counseling automotive and rail industry clients through litigation and related appeals. Ms. Tucker regularly applies her scientific background to her legal work, finding that her technical acumen provides a valuable perspective for matters and research involving technology, biology, and chemistry. She maintains an active pro bono practice, with a focus on landlord-tenant disputes.

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Credit Information

  • This 90-minute webinar is eligible in most states for 1.5 CLE credits.


  • Live Online


    On Demand

Date + Time

  • event

    Monday, March 16, 2026

  • schedule

    1:00 PM E.T.

I. Overview of Rule 16.1 and its relationship to Rule 26

II. Best practices case study: In re: Depo-Provera Products Liability Litigation

III. Use of different types of AI on data generated in MDL cases 

The panel will review these and other important questions:

  • What types of data are most readily available and helpful from initial case intake or plaintiff censuses?
  • How can data analysis be used to create more targeted discovery?
  • What is the interaction between Rules 16.1 and 26?
  • Can AI prompts be protected as work product?