Podcast: Navigating the New Rules of Healthcare Advertising

What "Privacy Safe" Really Means in Healthcare Marketing

During our conversation, I used an analogy that resonated with both Jodi and Justin: "privacy safe" in healthcare advertising is like "all natural" on food labels—everyone claims it, but few actually deliver on the promise.

Just like you need to read ingredient labels at the grocery store, healthcare marketers need to dig deep behind the tools, vendors, and partners they're using. Many companies are relying on marketing tools that were created before we had even one state privacy law on the books—before California started the wave of comprehensive privacy legislation we're dealing with today.

These legacy tools often rely on modeling, inferences, and predictions about someone's health status, then use that data to determine who should see an ad. That approach is now highly regulated—and increasingly risky from both a legal and reputational standpoint.

Why HIPAA Compliance Doesn't Equal Privacy Safety

One of the most persistent misconceptions I encounter is that "HIPAA compliant" equals "privacy safe." We're well past that world in healthcare advertising. HIPAA is important, but it's no longer sufficient as the sole privacy framework.

HIPAA was designed to regulate covered entities—healthcare providers, health plans, and their business associates. It doesn't apply to most digital advertising activities. It doesn't regulate health inferences. It doesn't cover consumer health information collected outside the covered entity context.

Meanwhile, state privacy laws have filled these gaps with regulations that specifically target the kinds of data practices common in healthcare advertising. These laws regulate:

• Health inferences and predictions about individuals
• The sale or sharing of consumer health data
• Targeted advertising based on sensitive personal information
• Consumer rights that extend far beyond HIPAA's protections

The bottom line: if your compliance strategy starts and ends with HIPAA, you're missing the bigger picture—and exposing your organization to significant regulatory risk.

The Patchwork Challenge: Navigating Multiple State Laws

One of the biggest challenges facing healthcare marketers today is what I call the "patchwork problem"—the reality that we now have over 20 states with comprehensive privacy laws, each with its own definitions, requirements, and enforcement mechanisms.

This creates several practical challenges:

• Definitional variations: States define "health data" and "sensitive information" differently, creating ambiguity about what's regulated where
• Consent requirements: Some states require opt-in consent for health data processing; others allow opt-out mechanisms
• Gray areas around inferences: The boundary between permissible contextual targeting and prohibited health inference varies by jurisdiction
• Enforcement unpredictability: Different attorneys general prioritize different issues and pursue enforcement with varying levels of aggressiveness

During the podcast, we discussed how this complexity makes it nearly impossible to maintain different compliance strategies for different states. The practical solution? Build to the strictest standard and apply it consistently across all markets.

Privacy-Safe Tools and Techniques That Actually Work

Rather than treating privacy as a trade-off with precision, healthcare marketers can start by building privacy-safe experiences and optimizing for business goals from there. Here are the proven methods we discussed:

Contextual Advertising

Instead of targeting individuals based on inferred health conditions, place ads in contexts where your target audience naturally appears. This means advertising on health information sites, in relevant search results, and alongside content related to specific conditions—without making person-level health inferences.

Opted-In Consented Data

Use data collected directly from consumers with clear, informed consent. This includes email lists built through transparent sign-up processes, CRM data from existing customer relationships, and first-party website interactions where users understand what they're agreeing to.

Aggregated Insights

Leverage group-level behavioral patterns rather than individual-level predictions. Instead of identifying specific individuals with health conditions, identify places, content, and contexts where people with those interests congregate online. This shift from person-level targeting to pattern-based insights maintains effectiveness while eliminating inference-based risk.

Vendor Due Diligence: Going Beyond Checklists

One of the most important topics we covered was vendor evaluation. Too many healthcare marketers rely on vendor self-certification without doing real due diligence. The standard approach—reviewing a privacy policy, checking a compliance checklist, maybe getting an attestation letter—isn't sufficient anymore.

I shared a practical test that any marketer can perform: act like a consumer. Go to the vendor's website and try to exercise your privacy rights:

• Can you easily find how to opt out of data sales?
• Does the vendor actually honor deletion requests?
• Can you submit an access request and get meaningful information back?
• Do the privacy controls actually work as described?

If a vendor can't pass this basic consumer test, they're not taking privacy seriously— regardless of what their marketing materials claim.

Beyond consumer testing, marketers should ask vendors to explain their methodology: How are audience segments built? What data sources are used? Are any health inferences made? How are consumer rights honored? Vendors who can't answer these questions clearly should raise red flags.

How AI Complicates the Privacy Landscape

We spent significant time discussing how artificial intelligence is blurring the boundary between aggregated and personal data—and creating new compliance challenges for healthcare marketers.

Traditional definitions of "personal information" focused on data that directly identifies an individual or is reasonably linkable to them. AI complicates this by:

• Making de-identification reversible: AI can re-identify individuals from supposedly anonymized datasets with increasing accuracy
• Creating inferences from aggregated data: Machine learning models can derive individual-level predictions from group-level patterns
• Introducing opacity: Black box AI systems make it difficult to explain how targeting decisions are made, creating transparency problems
• Enabling at-scale inference: What was once a manual, limited process can now be automated across millions of individuals

This is particularly concerning in healthcare, where the stakes are higher and regulations are stricter. Marketers using AI-powered targeting tools need to understand not just what the tool does, but how it does it—and whether those mechanisms create regulatory exposure.

Emerging Regulatory Trends Reshaping Healthcare Advertising

We concluded by discussing several regulatory trends that will shape healthcare advertising in the coming years:

Expanded Health Data Definitions

States are broadening what counts as "health data" beyond traditional medical information. This includes biometric data, genetic information, behavioral health indicators, and even data that reveals health conditions through inference rather than direct disclosure.

Stricter Consent Requirements

We're moving toward a consent-based framework for health data processing, where opt-out mechanisms are no longer sufficient. Some states now require affirmative opt-in consent before health data can be used for advertising purposes.

Increased Enforcement Activity

State attorneys general are actively investigating health data practices in advertising. We've seen significant enforcement actions, and more are coming. The risk is no longer theoretical—it's real and growing.

AI-Specific Regulations

New regulations targeting AI systems are emerging, with specific provisions around automated decision-making, algorithmic transparency, and the use of AI in sensitive contexts like healthcare. Marketers using AI-powered tools need to track these developments closely.

Privacy as Competitive Advantage

Throughout the conversation, I emphasized a theme that's central to Blueprint Audiences: privacy should be viewed as an advantage, not just a compliance burden.

When everyone in your market is using the same legacy targeting tools and operating in gray areas of compliance, there's an opportunity to differentiate. Organizations that:

• Build privacy into their strategy from the start
• Use transparent, explainable targeting methods
• Treat consumer rights as opportunities to build trust
• Stay ahead of regulatory trends rather than reacting to enforcement

...will build stronger consumer relationships, face less regulatory risk, and create more sustainable marketing programs. That's the real competitive advantage in healthcare advertising today.

Key Takeaways from the Conversation

• "Privacy safe" requires scrutiny — like "all natural" food labels, you need to look beyond marketing claims
• HIPAA isn't enough — state privacy laws now regulate healthcare advertising more comprehensively than HIPAA
• Test your vendors like a consumer — try exercising privacy rights on vendor sites to verify claims
• Three proven methods work — contextual advertising, consented data, and aggregated insights enable effective, compliant targeting
• AI creates new risks — machine learning blurs the line between aggregated and personal data
• Build to the strictest standard — the state patchwork makes jurisdiction-specific compliance impractical
• Privacy is an advantage — organizations that lead on privacy build stronger trust and face less regulatory risk