---
title: "Beyond basic tokenization: How modern PPRL delivers 10x match accuracy"
description: Learn what Privacy-Preserving Record Linkage (PPRL) is, how it works, and why it's essential for secure, privacy-compliant real-world data integration.
image: https://blog.healthverity.com/hubfs/ask-an-expert-blog-featured-alt-5.png
---
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# Beyond basic tokenization: How modern PPRL delivers 10x match accuracy
[By \- Emery Niemiec, Principal Government Sales,](https://blog.healthverity.com/author/emery-niemiec-principal-government-sales) July 31, 2025
In this edition of our *Ask an Expert* series, we turn to Emery Niemiec, Senior Director, Partnerships and Emerging Markets at HealthVerity, for insight into one of the most powerful technologies enabling secure, privacy-compliant data integration today: Privacy-preserving record linkage, or PPRL.
Creating a privacy-preserving token is relatively straightforward. Accurately determining whether two imperfect records belong to the same person is much harder. That’s the difference between basic tokenization and modern privacy-preserving record linkage (PPRL).
A token can help protect patient identity. But the quality of the evidence built from linked healthcare data depends on something more fundamental: Are you connecting the right information to the right person?
At HealthVerity, our approach to [identity resolution](https://healthverity.com/identity-manager/)has resulted in 10x greater match accuracy, with a measured false-positive rate of approximately 0.2%. **So, what makes that possible?**
## Tokenization vs. Identity Resolution: What's the Difference?
A token is a unique identifier created from information like a person's name, address, or date of birth. The resulting token is a random-looking string of letters and numbers that does not contain or reveal the personal information used to create it. It allows information about the same person to be connected across datasets without exposing those identifying details.
But creating a token is only part of the challenge. Identity resolution determines whether the right information across datasets belong to the same person. That sounds simple, but healthcare data rarely lines up perfectly. Names change. People move. Information may be missing, entered incorrectly, or captured differently across systems. That's why two PPRL solutions can both create privacy-preserving tokens but produce very different results.
## Why Deterministic Patient Matching Falls Short:
One way to determine whether records belong to the same person is through deterministic matching: if selected information matches exactly, the records are treated as belonging to the same person. That works well when the data is perfect. Healthcare data isn't. The same patient might appear as:
1. Elizabeth Martinez in one system
2. Liz Martinez in another
3. Elizabeth Garcia after a name change
4. Elizabeth Martinez-Garcia somewhere else
Deterministic matching can miss these connections. The opposite can happen, too: two different people may share enough information that they're incorrectly linked. Both errors matter. A false negative means records belonging to the same person aren't connected. A false positive means records belonging to different people are incorrectly combined. Either can distort the patient's journey and the evidence built from it.
[HealthVerity Identity Manager](https://healthverity.com/identity-manager/)has demonstrated a false-positive rate of approximately 0.2% and false-negative rates of approximately 2–5%, approximately 10x greater match accuracy than basic tokenization approaches.
## How Probabilistic Matching Improves PPRL Accuracy:
Instead of asking only, “Do these fields match?” HealthVerity uses probabilistic matching, meaning consideration of multiple pieces of a person’s identity, to determine how likely it is that records belong to the same person.
This means a difference in one field doesn't automatically rule out a connection, and a match on a few fields isn't automatically enough. The decision is based on the broader picture, not any single piece of information.
## How Historical Data Improves Patient Matching Accuracy:
Accurate identity resolution depends on more than the two records being compared. It also depends on context.
HealthVerity's identity resolution is supported by a database representing more than 330 million de-identified individuals, with historical identity information such as names, addresses, and other identifying details and how they have changed over time. That history provides important context for distinguishing between different people who may look similar in the data and recognizing the same person even when their information changes.
Consider two records for John Smith. Looking only at those records may not tell you whether they're the same person. Information about previous names, addresses, and other identifying details provides additional context to make that determination. **The more context you have, the more confidently you can determine who is, and isn't, the same person.**
### How HealthVerity PPRL Protects Patient Privacy:
Better matching can't come at the expense of patient privacy. That's why [HealthVerity Identity Manager](https://healthverity.com/identity-manager/) starts by protecting identity at the source. De-identification technology operates behind the data holder's firewall, so patient identities never leave the data holder's private environment. Healthcare data is de-identified before it moves into the analytic environment. **In simple terms: the identity doesn't need to travel with the data.**
**[](https://healthverity.com/identity-manager/)**
To make matching possible without exposing that identity, HealthVerity uses privacy-preserving techniques such as Bloom filter hashing, which allow identifying information to be compared without revealing the information itself. Combined with machine learning, these protected signals can be used to determine the likelihood of a match without exposing direct identifiers to analysts.
This approach to de-identification is assessed under the HIPAA Privacy Rule's Expert Determination method. Unlike HIPAA's Safe Harbor method, which follows a prescribed set of rules for removing identifiers and does not require an individualized expert risk assessment, Expert Determination requires a qualified expert to assess the actual risk of re-identification in the resulting data.
## How PPRL Creates Longitudinal Patient Data:
Once HealthVerity recognizes the same person across datasets, the HVID provides a common thread connecting information across healthcare sources and over time, allowing new data to be connected without having to figure out who each record belongs to all over again. That common identity can connect information across the healthcare ecosystem:
[](https://healthverity.com/identity-manager/)
Each source contributes something different: claims can show diagnoses, treatments, and utilization; EHRs add clinical detail; labs provide test results; hospital data captures events within a stay; and mortality data adds critical outcome information. When these pieces come together, they create a more complete view of the patient journey, helping understand not just what happened, but the care, context, and outcomes surrounding it.
## 5 Questions to Ask a PPRL Provider:
Not all PPRL approaches work the same way. When evaluating a solution, organizations should ask how identity is actually resolved.
**1. How do you handle imperfect data?** Can the technology account for missing information, typos, and changes over time?
**2. Do you measure false positives and false negatives?** A match rate alone doesn't tell you whether the matches are correct.
**3. What information is used to make a match?** Does the technology only compare the records being linked, or can it draw on a broader history of identity information to make a more accurate decision?
**4. Can identity persist across sources and over time?**
**5**. **Where does identifiable information go?** Does it ever leave the data holder's environment?
## Why PPRL Requires More Than Tokenization:
The real challenge is accurately recognizing the same person across fragmented, imperfect, constantly changing healthcare data, and doing it without compromising privacy.
That requires knowing you have the right person, measuring the validity of those connections, protecting patient identity, and keeping their healthcare journey connected as new information becomes available.
Ultimately, better linkage isn't about connecting more records. It's about being confident you're connecting the right records. And that's what turns fragmented healthcare data into trustworthy, longitudinal evidence.
[](https://blog.healthverity.com/hs/cta/wi/redirect?encryptedPayload=AVxigLKOZPIBQt32G2aLXuPSog1oKajFbZvuLxC74FyJo5u4RoC8sq8zuz0ohcZsKON1CqATPGX47UAf3a1TIKt%2F%2F3fvnSuCz1jGlrjzfXoSg1q0kjO%2Beu%2FP4Xze%2BK%2B5JhI%2FWFIdUhR0I8dKyULFP8xvuCr9nrDvINhk3iX%2BOBHE0703NZLWlXTIwJiEZMatFpOBi0caZxnuL14%3D&webInteractiveContentId=192717537905&portalId=5412465)
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#### [What is Privacy-Preserving Record Linkage (PPRL)? A definitive guide](https://blog.healthverity.com/what-is-privacy-preserving-record-linkage)
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Privacy preserving record linkage keeps data de-identified
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