3 Myths About AI in Healthcare Interoperability—Busted

When it comes to healthcare interoperability, AI hype often runs ahead of reality. Bold promises about automating integrations, solving patient matching, or extracting insight from messy clinical notes are everywhere. But what’s really possible today?

In this article, we break down three common myths about AI in interoperability and point you to a practical, data-backed field guide that shows what’s actually working in the field today.

Myth #1: AI Can Fully Automate EHR Integrations

It’s a tempting thought: just point an AI tool at your HL7 messages or FHIR endpoints and let it build the integration for you. But the reality is that integration work still requires deep understanding of clinical context, business logic, and data governance, none of which can be fully outsourced to AI.

That said, AI is making real progress in accelerating parts of the process. Tools like Rota Health use AI prompts to help non-engineers build mappings faster, enabling quicker go-lives for custom integrations. But these tools still require human oversight to ensure semantic accuracy, appropriate data use, and safe system behavior.

Today, AI can assist, not replace.

Myth #2: AI Has Solved Patient Matching

Patient matching remains one of the thorniest problems in health data exchange. Without a universal patient identifier in the U.S., organizations rely on combinations of names, birthdates, and addresses, which often don’t match across systems.

AI does help. It can improve matching algorithms, detect likely duplicates, and even predict which data sources are worth querying. But it hasn’t “solved” the problem. There are still major risks: false matches, missed records, and opaque decisions that are hard to audit.

In other words, AI makes matching better, not bulletproof. Human review and strong governance remain essential.

Myth #3: AI Can Automatically Turn Clinical Notes Into Structured, Usable Data

Unstructured clinical notes have long been a barrier to effective interoperability. It’s no surprise, then, that LLMs and NLP tools are being turned to as a solution. And in many cases, they’re showing impressive capabilities—from identifying conditions and medications to generating structured FHIR outputs from otherwise messy clinical documentation.

But turning those capabilities into real-world impact isn’t automatic.

To make unstructured data useful, organizations need the right workflows, governance, and validation processes in place. Extracting data is only the first step. Ensuring it’s reliable and actionable is what makes it valuable.

Beyond the Myths: What’s Actually Working

Despite the noise, real progress is happening. AI is already evolving interoperability in practical, often under-the-radar ways. For example, robotic process automation (RPA) is connecting systems in new and innovative ways beyond what APIs and other traditional integration methods can support. And, it can dramatically speed up the process of building mappings between different data standards, an otherwise time-intensive task.

Progress like this isn’t flashy. But it’s working.

Robotic process automation and more AI-driven approaches are examined in detail in Newfire’s Field Guide to Strategic Interoperability for Digital Health Operators, covering how they work, what to consider before implementation, and which vendors are leading in the space.

Download the report for a grounded, data-backed view of what’s actually delivering results in healthcare interoperability today.