Hospitals are investing heavily in AI — from diagnostics to workflow automation. Yet many of these pilots never make it beyond the proof-of-concept stage. Why? The problem isn’t weak algorithms. The real barrier lies in persistent misconceptions about interoperability and what it means to be truly “AI ready.”

Here are three of the most common misconceptions — and what the evidence really shows:

Misconception 1: “Traditional Integration Engines or Traditional ESBs are good enough.”

Traditional integration engines or ESBs were designed for basic connectivity. That may have been sufficient for legacy workflows, but AI workloads are fundamentally different. They demand high concurrency, low latency, and real-time, event-driven flows across many systems simultaneously—something traditional engines were never built to handle.

The KLAS Research and Bain & Company report “Healthcare IT Spending 2024: Innovation, Integration, and AI” highlights that respondents still cite cost (49%) and EHR integration (42%) among their top pain points in scaling healthcare IT and AI initiatives, noting that tighter EHR-to-technology integration is essential to ensure “the right data goes to the right place” and to set organizations up for success. This reinforces that integration challenges persist even before AI scale is considered.

Without the capabilities of high performance, high availability, scalability, and native support for AI agents, integration platforms quickly become throughput bottlenecks—preventing AI from moving beyond pilots into daily clinical workflows.

Misconception 2: “If we pass interoperability, we’re ready for AI.”

Interoperability compliance shows that systems can exchange data, but it doesn’t prove that infrastructure can support AI at scale. Passing HL7/FHIR checks proves systems can exchange data—but it doesn’t mean you can scale AI. A recent Nordic + Modern Healthcare survey found the top barriers to AI scalability are data integration & interoperability (51%), data analytics tools (50%), and data security (37%)—showing that connectivity alone isn’t readiness.

The same research notes that over half of organizations say their systems still need development, and only 15% report infrastructure that’s “easily scalable.” That gap shows why interoperability is the baseline, not the finish line.

Misconception 3: “As long as we have strong algorithms, everything else will follow.”

It’s a common belief that AI success depends primarily on algorithms — that once the models are accurate enough, adoption will naturally follow. In reality, algorithms are only the visible tip; the real foundation lies in infrastructure.

Think of the iceberg model: algorithms and applications are the small portion above the surface for the application for AI agents. Beneath the waterline lies the unseen 90% — engineering, data governance, interoperability, and workflow design — that sustains true clinical adoption. Without this solid foundation, AI risks remaining a showcase, not a solution.

Key Takeaway: Strengthen the foundation of healthcare AI

Interoperability is essential — but it’s only the starting point. Real AI readiness requires an integration backbone that is scalable, resilient, secure, and workflow-ready, with the performance to handle high concurrency and the flexibility to connect emerging AI agents alongside existing clinical systems.

Hospitals that invest in this kind of foundation won’t just run isolated AI pilots — they will embed AI into everyday practice, turning innovation into sustained clinical impact.

Source：

Healthcare IT Spending 2024: Key Insights and Trends

https://engage.klasresearch.com/blog/healthcare-it-spending-2024-key-insights-and-trends/4508/

Survey highlights foundational readiness for AI in healthcare

https://www.nordicglobal.com/blog/survey-highlights-foundational-readiness-for-ai-in-healthcare?utm_source=chatgpt.com