Training or validating a healthcare AI model requires data — but real patient and claims data can’t be used as-is. We build de-identified, compliant datasets that preserve the signal your model needs without the regulatory exposure.
This is one of the most common blockers we see in healthcare AI projects: teams have a clear idea of the model they want to build, but no compliant way to get the training data it needs. Waiting months for an internal compliance review to grant access to real patient data — or worse, building on synthetic data that doesn’t reflect real-world messiness — both slow a project down. We help close that gap directly.
This service is particularly relevant for organizations building their first healthcare AI product, where the chicken-and-egg problem is most acute: you need real data to prove the model works, but you need to prove the model works before compliance will approve broader data access.
We apply established de-identification standards and rigorous review to remove identifying information while retaining the structural and clinical patterns your model depends on.
The goal is data that a model can genuinely learn from, not data that’s been stripped of everything useful in the process. That means preserving realistic variation in document formats, claim structures, and clinical detail while removing anything that could identify an individual patient — a balance that generic de-identification tools, built for other industries, typically get wrong in one direction or the other.
Every dataset we build goes through a documented review before delivery, so you receive not just the data itself but a clear record of what de-identification steps were applied and why — evidence you can show your own compliance or legal team if questions come up later.
Compliant datasets for every stage of your model's lifecycle
Model training
Building the initial dataset a new healthcare AI model learns from.
Model validation
Testing an existing model against realistic data it hasn't seen before.
Benchmarking
Comparing multiple models or vendors against the same standardized dataset.
Research
Supporting academic or internal research initiatives that require realistic but fully compliant healthcare data.
We can also combine multiple use cases within a single engagement — for example, building a dataset that supports both initial model training and a later, independent validation step, so the two efforts use consistent, comparable data.
Dataset support is scoped per engagement based on data source, use case, and applicable regulatory requirements.
Because every organization’s data sources and compliance obligations are different, this service is scoped individually rather than offered as an off-the-shelf product. We’ll tell you upfront, before any work begins, whether a given dataset request is something we can support and what regulatory steps it will require — available where the underlying data and use case allow.
If your specific use case falls outside what we’re able to support, we’ll tell you directly and, where possible, point you toward a more suitable approach rather than taking on an engagement we don’t think we can deliver well.
A member of our team will reach out within one business day to schedule a short discovery call — no obligation.