If an AI vendor’s system gets smarter over time, it’s worth knowing exactly how—and whether your organization’s data, in any form, could ever shape results for a different health system. Prodigy™, MRO’s AI engine for clinical data abstraction, is built to improve without ever mixing one client’s data with another’s.
What Counts as “Your Data” in an AI Abstraction Workflow
When people ask about data isolation, they’re often only thinking about the original patient record. But in an AI-powered abstraction workflow, “your data” covers more than that: the source documentation Prodigy reads, the values it abstracts from that documentation, the evidence links connecting each value back to its source, and the corrections or confirmations your clinical team makes along the way.
All of it, from the original chart to the final abstracted value, stays within your organization’s own environment. Your organization’s specific data is not moved into another client’s workflow or used to train an LLM for another health system.
How Prodigy Keeps Your Data Separate from Every Other Partner’s
Prodigy is designed to improve over time in two ways, and neither one involves mixing client data.
Within your own registry program, ongoing use improves accuracy, evidence quality, and the abstractor experience over time—using only your organization’s own data. As Prodigy supports more registries and more health systems, general lessons can inform how the underlying system approaches a new program, but no client’s specific records are ever moved into another client’s workflow to get there.
In short: your organization’s data stays within your environment, while broader lessons can inform improvements to Prodigy without moving your specific records into another client’s workflow.
How Healthcare AI Data Separation Works
Say two health systems both use Prodigy to support their NCDR registry programs. Each system’s documentation, abstracted data, and results stay entirely within that system’s own environment. If Prodigy’s underlying models improve over time, that improvement comes from general model and process refinement, not from feeding one health system’s patient records into another’s results. Health System A’s patient records are not moved into Health System B’s workflow or used to train an LLM for Health System B. Prodigy can still benefit from broader model and process improvements informed by experience across programs.
Why This Matters for Compliance and Audit Readiness
For health systems managing registry participation across multiple facilities, this isn’t just a technical detail; it’s something your compliance, privacy, and quality teams need to be able to stand behind. Patient information is subject to strict regulatory protection, and being able to clearly explain how an AI vendor handles that data, rather than pointing to a certification and hoping it covers the question, is part of what keeps registry data defensible.
A vendor whose system improves broadly, without depending on any single client’s data being reused elsewhere, gives your team a clear, specific answer if that question ever comes up in an audit, a security review, or a board conversation about AI adoption and risk management.
Prodigy is built to do more than automate abstraction. It’s designed to protect the trust health systems place in their data while supporting complete, accurate, and defensible registry results.
Ready to see how Prodigy can support your quality program?
Frequently Asked Questions About AI Data Security in Healthcare
Can my hospital’s clinical data be used to train an AI model for another organization?
With Prodigy, no. Client data is processed separately, and any improvements to the system are generalized at the model and process level rather than by sharing individual client records.
How does an AI system improve over time without sharing data between clients?
Prodigy improves in two ways: through ongoing use within your own registry program, and through general lessons that carry over to new registries or health systems, without moving your specific data into another client’s workflow.
Does Prodigy store or use protected health information (PHI) outside my organization’s environment?
No. Client data, including PHI, is processed within the boundaries of your organization’s own environment and is not used to shape results or outcomes for another client.
What data specifically stays within my organization’s environment?
Source documentation, abstracted values, evidence links, and any corrections made during clinical review stay within your organization’s own environment and are not moved into another client’s workflow.