Healthcare marketers today face a dual challenge: deliver more qualified patient leads with greater efficiency, while maintaining strict compliance with HIPAA and other privacy regulations. That’s a tall order, especially when many marketing teams have already maxed out performance from bottom-of-funnel campaigns on Google Ads.
So, where do you go when you’ve squeezed all you can from “best urgent care near me” or “top-rated dentist” keywords?
You go up the funnel.
That’s where third-party (3P) data becomes invaluable. Long used in retail and pharmaceutical marketing, 3P data is now gaining traction with sophisticated healthcare marketers who want to reach niche audiences in a privacy-compliant, performance-driven way.
Why Demographics Aren’t Enough
In 2025, age, gender, and geography simply don’t cut it. Two women in their 40s living in the same ZIP code can have drastically different healthcare needs, behaviors, and levels of digital engagement.
Broad targeting often leads to poor lead quality, wasted media spend, and operational friction. Clinical teams don’t have time to sift through unqualified appointment requests. If you’re trying to grow patient volume without overwhelming your staff, better targeting isn’t optional—it’s essential.
What Is 3P Data—and Why Does It Matter?
Third-party data refers to external datasets you can license or access from trusted vendors. In healthcare, this can include:
- Claims data: ICD/CPT code-based histories that reveal diagnoses or care patterns
- Behavioral data: Media consumption, app usage, mobility, purchase behaviors
- Psychographic data: Attitudes, lifestyle preferences, healthcare mindsets
- Demographic/lifestyle data: Income, family status, education, interests
These data types can be grouped into two broad categories, deterministic and probabilistic, each with its own strengths, limitations, and role in campaign strategy.
Deterministic vs. Probabilistic Targeting: What’s the Difference?
Third-party data providers generally fall into two camps: deterministic and probabilistic. Understanding the difference can help you choose the right data strategy for your goals and audience.
Deterministic audiences are built from known, verifiable data that confirms a person definitively has a diagnosis or characteristic. For example, a person may have had a hip replacement, been diagnosed with anxiety, or be confirmed to have Blue Cross Blue Shield insurance. These are high-confidence data points, often sourced from claims data or payor files.
This data is HIPAA-compliant because it’s tokenized—de-identified before it’s sent to platforms like LiveRamp or Throttle for activation. The end platform never receives personally identifiable information (PII), but you still get a precise audience to target.
The advantage of deterministic targeting is accuracy: you’re reaching the exact people who have the condition, received the procedure, or are on a specific medication. It’s a direct hit. But there are trade-offs. Claims data often lags, which means your timing might be off. And even with identity resolution tools, match rates can be low, leaving you with small, high-frequency audiences.
In contrast, probabilistic audiences rely on inferred data. These segments are modeled based on patterns, predictions, and lookalikes that are built off a deterministic “seed” audience using propensity modeling. The platform identifies people who look like those in the seed set—same behaviors, same traits, similar digital footprints—and groups them into likelihood tiers. For example, someone who visits a series of oral surgery websites may be categorized as a potential implant patient, even if they haven’t taken action yet.
Both methods have value. What makes 3P data powerful is the ability to layer multiple signals together, building audiences that are not only HIPAA-compliant but also highly specific and more likely to convert. Deterministic data provides precision and reliability, making it ideal for bottom-of-funnel targeting. Probabilistic data is often more suitable for scale and upper-funnel reach, especially when combined with a robust testing framework. The best strategies blend both.
Navigating HIPAA, Privacy, and Trust
One of the biggest benefits of 3P data is its ability to support compliant healthcare marketing. Because the data is de-identified and modeled, you avoid transmitting protected health information (PHI) directly to ad platforms. Still, marketers must take proactive steps to stay compliant:
- Work with partners who will sign a BAA (Business Associate Agreement)
- Avoid sending PHI into ad platforms like Meta or Google Ads
- Use a Customer Data Platform (CDP) to transmit conversion data in a compliant manner
When done correctly, better targeting can actually enhance trust by delivering relevant, helpful information that respects where a patient is in their care journey.
However, it’s important to note that not all claims data is eligible for use in advertising. Advertising platforms like Meta have policies that strictly exclude certain highly sensitive health categories from targeting. For instance:
- Substance use disorder (addiction)
- Pregnancy and fertility treatments
- Pediatrics and adolescents
Some categories that might seem off-limits—like cancer or mental health—can in fact be used for advertising under the right conditions. These audiences are typically tokenized and offered by HIPAA-compliant partners as de-identified segments. Ad platforms like Meta already support categories such as “prone to depression” or “interested in cancer treatment.”
Conversely, some categories that may seem sensitive, such as oncology or common mental health conditions like anxiety and depression, can be utilized in advertising if handled properly, and are available as de-identified audience segments from compliant data partners. In fact, major ad platforms already offer categories like people “prone to depression” or interested in cancer treatments, demonstrating that oncology and anxiety/depression information can be leveraged under the right HIPAA-compliant conditions.
The key for healthcare marketers is to clearly understand which data areas are off-limits and which are permissible, and how their use aligns with their objectives, marketing strategy, and brand values.
From Persona to Signal: Making Your Audiences Targetable
Marketers love personas. But most personas aren’t media-ready. To make them actionable, you need to map each persona to real-world, targetable signals. Here’s how:
- Identify measurable attributes
- Bad: “Values convenience” or “puts family first
- Good: Uses wellness apps, lives in a health desert, has a claims history for PT
- Layer behavioral signals
Ask: What are they doing when they’re most engaged or likely to act? For example, a “proactive health seeker” might follow fitness influencers, use an Apple Watch or Oura Ring, and browse content about joint pain or weight loss - Partner with the right vendors
You need data partners who can translate clinical, behavioral, and psychographic signals into targetable segments on platforms like Meta or programmatic DSPs, such as Illumin.
Real-world Use Cases
Once you’ve translated personas into targetable signals, it’s time to bring those strategies to life. Below are real-world examples that show how 3P data can help you find and connect with potential patients before they start searching for care.
Orthopedics: The Weekend Warrior
Men in their 40s and 50s who live active lifestyles often deal with joint pain, sports injuries, or post-surgical recovery. By identifying claim codes tied to ACL tears or PT visits and layering in fitness interests, you can find qualified patients who may need a surgical consult, but haven’t started searching yet.
Data Strategy:
- Claims data: Prior ACL tear, PT visits
- Behavioral signals: Gym membership, sports media consumption
- Psychographics: DIY recovery mindset, “Health-Conscious Male” from Claritas
- Geo-targeting: Within the radius of ortho practices with low utilization
Mammograms: The Healthcare Procrastinator
Many women delay their first screening mammogram despite being eligible. Third-party data can help identify this population—women aged 35–45 with no history of mammography claims, living in regions with low compliance rates. Psychographic overlays like “Busy Caregiver” or “Skeptic” can further refine messaging, helping health systems reach patients who need a nudge.
Data Strategy:
- Claims exclusion: No CPT/HCPCS codes for prior screening
- Risk overlay: Lives in low-compliance zip codes (Centers for Medicare & Medicaid Services or Definitive data)
- Psychographics: “Healthcare Procrastinator,” “Busy Mom”
- Behavioral: Interested in family health, but not breast health content
Meta Advantage+ and Social AI Targeting
In addition to using external data sources, healthcare marketers can also take advantage of AI-powered audience modeling from ad platforms like Meta Advantage+.
These tools use real-time behavioral signals (what users like, follow, engage with, and how they interact with content) to create high-performing segments without relying on PHI. Over time, the AI adapts and refines who sees your ads based on what’s actually driving results.
By feeding Advantage+ with meaningful conversion signals, such as leads, appointments, or custom events, marketers enable the algorithm to find and optimize toward people who exhibit similar behaviors to their ideal patients. Rather than manually defining every targeting parameter, the AI can model high-performing audience segments based on real-time engagement patterns across the platform.
It’s an efficient way to scale reach, particularly when combined with other 3P layered strategies. And it gives you an extra advantage in top-of-funnel campaigns where discovery and awareness are key.
How to Activate & Use 3P Audiences
Once your 3P audience segments are built, the next step is activating them in your media channels. This is where your data partners and identity resolution platforms come into play.
Most 3P audiences are pushed into ad platforms via partners like LiveRamp or Throttle, which handle tokenization and onboarding. These platforms match your audience to known users on media channels without ever exposing personal health data.
You can activate these audiences across a wide range of platforms:
- Meta (Facebook/Instagram)
- TikTok
- Programmatic DSPs like The Trade Desk, DV360, Viant, and Illumin
These destinations allow for robust audience targeting and media optimization using the segments you’ve built. Because activation is identity-agnostic once tokenized, you’re free to scale across environments that support third-party data integrations.
One major limitation: Google Ads currently does not allow healthcare advertisers to import third-party audiences. While there’s speculation this may change, for now, Google’s platform is restricted to native targeting tools.
Choosing platforms that support 3P data integrations ensures you can fully leverage your audience strategy and drive performance without compromising compliance.
Final Thoughts: Smarter Segments Drive Better Results
In 2025, efficiency is everything. Healthcare marketers need to be surgical in their targeting, and third-party data is one of the most powerful ways to do it.
Map your personas. Layer your signals. Test ruthlessly.
When you do, you’ll waste less budget, book more of the right patients, and prove that marketing can be a strategic driver of growth, not just a cost center.
