Hospitals across your network can use the same code and mean different patients. Vercori finds that problem before your study runs.
Common data models standardize how data is structured. They do not standardize what clinicians mean when they record a diagnosis.
One hospital requires a confirmed test before coding a condition. Another codes it on clinical judgment alone. A third applies a different threshold entirely. All use the same concept ID. All pass every standard data quality check.
But when you combine them in a study, you are combining different patient populations and you will not know it from the data.
The result is a study that looks clean and produces a number that is wrong.
Illustrative scenario · HFrEF · Based on Gluckman et al., Clinical Cardiology 2024
No software to install on your end. Each site in your network analyzes its own CDM instance locally. No patient records go anywhere.
Each site sends Vercori a statistical summary, a semantic fingerprint, of how it defines each concept. That is all we receive.
Vercori compares fingerprints across your entire network, across six measurable dimensions per concept, and identifies where sites diverge.
Every concept is rated, every divergence is explained, every reviewer decision is recorded in a sealed audit log. Ready for your submission package.
A report for each concept in your study. Each one is rated: consistent across sites, divergent with a known explanation, or divergent and needing clinical review.
Every decision is documented by a qualified reviewer and recorded in a sealed audit log. Ready for your submission package.
Attach it to your submission. Reference it in your methods. Use it to answer the question: how do you know your sites were measuring the same thing?
You do not have to hope your sites agree. Before a single result is generated, Vercori shows you exactly which concepts your sites define differently. Then you make the call: proceed, narrow the scope, or fix the mismatch before you lock your protocol. A verdict on the data, not a bet on it. No finding out after the results are in, when your only options are re-running the analysis or publishing with a caveat.
Each institution runs the analysis locally. The only thing that reaches Vercori is a statistical fingerprint of how the site defines each concept, never a patient record. No raw data pooling, no patient exposure to sign off on. The privacy question that usually stalls multi-site work is answered before it is asked.
When your data quality is challenged, an assumption will not hold. Vercori gives you a quantified, tamper-evident record of every concept in your study: which are consistent, which diverge, what was done, and who decided. So when an FDA reviewer asks how you know your sites were measuring the same thing, the answer is already documented, before the question is ever asked.
Find out if your sites are actually measuring the same thing before you combine their data.
Give your sponsors documented proof that semantic consistency was checked across every site, not assumed.
Strengthen your network's research credibility with a quantified trust layer between sites.
A data quality dashboard checks whether your data is correct within a site. We check whether your sites mean the same thing. Both matter. We pick up where it stops.
FDA's 2024 guidance on real-world data for drug and biological products and its 2025 medical device guidance both expect documented assessment of completeness, accuracy, and consistency across sites, not just structural correctness. Passing structural quality checks is no longer a complete answer.
With over one billion patient records across 34 countries in OMOP alone, the probability that all sites define concepts identically is effectively zero.
A single post-submission data quality challenge can delay approval, trigger re-analysis, or undermine a study that took years to complete.
Cross-site coding variation in federated OMOP networks is documented, discussed, and widely acknowledged. What has been missing is a practical way to measure it, document it, and act on it before a study runs.
That is what Vercori does.
Vercori was founded by Sandra Estremera-Zink, J.D.
The name comes from two words: verdict and core. The product renders a verdict on the core concepts driving your study before the study runs.
The methodology is patent pending and has been submitted to the 2026 OHDSI Global Symposium. It is grounded in published research on cross-site concept variation in OMOP networks.
We are accepting pilot partners now. Pilot studies are scoped individually based on network size and use case. Book a demo to discuss.
If you run multi-site OMOP studies, operate a network site, or advise pharma sponsors on real-world evidence, we want to hear from you.
Pilot terms
Pilot studies are scoped individually based on network size and use case. We're open to discussing co-authorship on a publication if the results warrant it.