We explore the growing wave of class action lawsuits hitting the telehealth industry—and why legal counsel, especially those advising telemedicine platforms or navigating pharma partnerships, must pay attention now.

Here’s the core issue: many ad tech stacks are leaking protected health information (PHI) through tracking pixels and cookies. These tools—common in e-commerce—are transmitting sensitive data to third parties like Meta, potentially without proper consent. That’s not a glitch; it’s a design feature—and it may violate HIPAA, the FTC Act, CCPA, and state consumer protection laws.

Key Legal Risks:

• Tracking pixels may result in unauthorized PHI disclosures.
  
  
• “Anonymized” data isn’t safe—reidentification is easier than ever.
  
  
• Partnering with pharma introduces a regulatory maze: HIPAA, FTC, FDA promotional rules, and anti-kickback statutes may all apply—and risks compound, they don’t cancel out.
  
  
• A simple ad campaign could result in kickback concerns, promotional violations, or privacy breaches.
  
  

What Legal Counsel Should Do:

1. Audit your tech stack—what tools are collecting what data, and where does it go?
   
   
2. Revisit consent practices—is the user aware of what’s being shared?
   
   
3. Separate clinical from commercial functions—these are legal firewalls.
   
   
4. Update your data sharing contracts—HIPAA, indemnification, audit rights, reuse clauses.
   
   
5. Retrain marketing teams—even one tag can trigger legal liability.
   
   

Real-World Consequences:

• OCR penalties, FTC consent decrees, and civil suits for emotional distress and privacy violations.
  
  
• Reputational damage, loss of investor confidence, and pharma partners pulling back.
  
  

At The Kulkarni Law Firm, we help telehealth platforms and FDA-regulated companies identify and mitigate these risks before they explode. From privacy reviews to marketing compliance and risk assessments, we’re here to help.

Ask yourself:

• Are you using ad pixels?
  
  
• Do your legal and marketing teams actually talk?
  
  
• Are you ready to explain, under subpoena, who got patient data—and why?
  
  

If this episode made you rethink your stack—or your strategy—share it with your team.

Get in touch with us if you have any questions.

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In this episode of the KLF Deep Dive, Darshan Kulkarni and Edye Edens engage in a practical discussion around one of the most debated issues in clinical research: Should clinical trial sites be paid differently based on who they are, where they’re located, or what they bring to the table?

The conversation challenges the idea of flat-rate, standardized budgets for all sites and explores the concept of Fair Market Value (FMV) from both sponsor and site perspectives. Edye and Darshan break down how location, site type (academic vs. independent), operational capacity, and even patient population size can significantly affect the true cost of running a clinical trial. They also address common misconceptions—like whether having more patients always justifies higher pay—and emphasize the importance of effort tracking and cost documentation as tools for sites to advocate for reasonable compensation.

The two also examine the wide variability in how sites are funded, including differences driven by grants, institutional policies, and pass-through rules. They explore why attempts to equalize payments across the board—though well-intentioned—may backfire, creating unintended inequities that hurt both high-performing sites and trial timelines.

Darshan draws a parallel to J. Paul Getty’s famous quote, “Just a little bit more,” noting that many sites, regardless of how much they’re paid, still want more—while sponsors often feel they're already paying generously. This leads to a larger issue: the lack of transparency and shared understanding around how FMV is calculated. Sites are often expected to justify every line item with data, while sponsors rarely share the assumptions and benchmarks they use in return.

This episode explores key questions like:

• What does fair really look like in site budgeting?
  
  
• Should sponsors be more transparent about their FMV calculations?
  
  
• How do we balance standardization with the real-world variability in site operations?
  
  

The discussion sets the stage for a follow-up episode exploring whether sponsors should disclose their FMV methodologies—and how that could impact trust and negotiation dynamics across the clinical trial ecosystem.

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In this episode of KLF Deep Dive, Darshan Kulkarni explores the growing urgency for in-house counsel to develop AI compliance programs as artificial intelligence becomes embedded in drug discovery, clinical decision-making, patient engagement, and beyond.

Darshan emphasizes that AI can create significant legal risk—even without breaking the law—if companies fail to address issues of transparency, validation, privacy, and governance. As regulators like the FDA and FTC tighten their expectations, companies must proactively implement structured, cross-functional AI compliance programs.

Key Topics Covered:

• AI System Mapping
  Start by identifying all AI systems—internally developed or third-party. Understand who owns them, what data they use, and how they function. Create a living inventory that evolves with your organization.
• Validation & Explainability
  Ensure that your models are transparent, repeatable, and auditable. Document how decisions are made and build mechanisms to detect deviations. Explainability is no longer optional—regulators and litigators expect it.
• Privacy & Governance
  Align your AI systems with HIPAA, GDPR, and state privacy laws. Update privacy notices to disclose AI use and profiling. Legal and privacy teams must collaborate closely with AI developers.
• Monitoring & Decommissioning
  All systems fail or become outdated. Put in place processes to log errors, recalibrate models, and decommission AI tools without disrupting patient care.
• Contracting & Vendor Management
  Negotiate contracts that clearly define data rights, IP ownership, use limitations, and audit rights. Tie these terms back to your insurance coverage and risk allocation.
• Risk Assessment
  Use risk registers to evaluate AI systems for potential misuse, bias, or patient harm. Prioritize mitigation efforts and build policies based on real-world use, not theoretical frameworks.
• Culture & Training
  AI compliance isn’t a document—it’s a system. Cross-functional teams (legal, medical, IT, marketing) must be trained regularly. Appoint internal champions to maintain risk maps and trigger policy updates.

Conclusion:

If your organization doesn’t know who governs each AI system—or if your contracts don’t cover AI-specific risks—you’re already behind. Now is the time to build an adaptive, defensible AI compliance program that scales with your innovation.

Kulkarni Law Firm helps pharma and health tech companies translate AI risk into operational clarity. Subscribe to KLF Deep Dive for more weekly insights at the intersection of legal risk and life science innovation.

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