James DeBiase Healthcare Interoperability Expert

Healthcare organizations are investing millions in AI initiatives, but many may be overlooking a critical dependency.

AI requires data.

Not just large amounts of data, but data that is accurate, complete, timely, and consistent.

Over the past 25 years working in healthcare interoperability, I’ve seen organizations struggle with duplicate patient records, inconsistent terminology, incomplete clinical histories, delayed interfaces, and data quality issues. These challenges are not new.

What’s new is that AI can amplify them.

When interoperability and governance are strong, AI can deliver meaningful insights, automate workflows, and improve decision-making.

When they are not, AI can produce inaccurate recommendations faster than ever before.

The conversation around healthcare AI often focuses on models, algorithms, and computing power. In my view, one of the more important questions is:
Are our interoperability and data governance foundations ready for AI?
I’ve been exploring this topic in a paper examining the relationship between AI, interoperability, governance, and data quality in healthcare.

I’d be interested in hearing from others:

Do you believe AI will reduce interoperability challenges, or will it increase the importance of getting interoperability right?