AI has become healthcare’s version of a blockbuster movie. It’s the feature of every keynote, every investor meeting, and every vendor pitch. With AI hype at full volume, it’s tempting to believe the technology alone will rewrite the rules of care delivery.
Yet anyone who’s peeked behind the curtain knows the real magic isn’t the performance; it’s the machinery powering it. And, in healthcare, machinery is interoperability.
AI creates value when it can access high-quality, contextual data and integrate with clinical, operational, and imaging systems, all while preserving security, workflows, and compliance. Most organizations aren’t there yet.
That’s why the next era of healthcare AI will be defined not by algorithms, but by interoperability.
AI Hype Meets Healthcare Reality
For all the excitement around AI, most health systems are still wrestling with the fundamentals:
- Incomplete data
- Mismatched person records
- Siloed systems
- Inconsistent data exchange methods
- Security models built for an era long before autonomous agents
AI is not a magic layer you place on top of fragmented architecture. It depends on what sits underneath. If the underlying data is unreliable or inaccessible, the model doesn’t get smarter. It gets riskier.
That’s why the next wave of innovation belongs to interoperability.
AI can only transform care if it can safely reach the information it needs, when it needs it, in a form it can understand.
The Evolution: From Messages to APIs to AI Agents
Healthcare data exchange has moved in clear stages.
First came HL7v2 messages, which reliably shuffled data between systems but were never built for flexibility. Then came APIs, which made data more accessible and real-time, paving the way for consumer-friendly experiences.
Now we’re entering a new phase of AI agents—systems that don’t just analyze information, but autonomously make decisions and take action to automate complex healthcare workflows at scale.
Agents don’t behave like traditional applications. They request context dynamically, retrieve data from multiple systems, reason about it, and initiate follow-up tasks. But they can only operate safely if they can:
- identify the right patient,
- access data through secure, governed channels, and
- interact across new and legacy systems without breaking workflows.
This is the moment where interoperability and AI become inseparable.
The Challenge: Architecture That Wasn’t Built for AI
Many organizations are discovering that the barrier to AI adoption isn’t the model. It’s everything else. Legacy interfaces can’t support machine-driven access patterns. Identities are mismatched or duplicated. Security architectures assume a human user, not an autonomous agent. And data sits in silos created by decades of system-by-system modernization.
Healthcare is cautious for good reason. But the shift is inevitable. Clinicians, patients and care teams increasingly expect AI to be available inside everyday workflows. The question is not if health systems will modernize their data infrastructure, it’s whether they’ll modernize fast enough. For example, a discharge-planning agent is only as good as its ability to reliably see the full medication list, payer rules, and follow-up appointments in one place.
The Opportunity: Infrastructure Built for AI
The organizations that succeed with AI in the next decade will not be the ones experimenting with the flashiest models. They will be the ones who build a stable, secure, identity-aware foundation that can support AI at scale.
That means moving from ad-hoc interoperability to a unified fabric that connects legacy systems, modern APIs, cloud environments, and AI-driven workflows. It means introducing stronger governance so that systems and agents know who is requesting data, why they’re requesting it, and whether they should have access. It means putting identity at the center: if you can’t trust who the data belongs to, you can’t trust the AI built on top of it.
This is where the work at Rhapsody is focused: building the conditions AI needs to operate safely in healthcare. That work spans integration, person data matching, terminology, and secure APIs – so that whatever AI a customer chooses can trust the data it sees and the permissions it has. The Rhapsody platform is designed to bridge the old and the new (i.e. HL7 and FHIR, cloud and on-prem, APIs and AI agents) all while preserving the governance, context and security that healthcare requires. Capabilities like Rhapsody’s new API Guardian strengthen this foundation by ensuring healthcare APIs are securely exposed, governed, and ready for AI-driven automation. And as we introduce Axon, our agent-ready orchestration layer, we’re giving organizations a way to experiment with AI agents on top of the infrastructure they already trust, instead of ripping and replacing core systems.
The Path Forward
AI will reshape how care is delivered, documented, reimbursed, and coordinated. But the organizations that benefit most won’t be those that chase the biggest model. They’ll be the ones that lay the right foundation: interoperable, governed, secure, identity-driven and ready for agentic workflows.
At Rhapsody, that’s the future we are building toward: helping healthcare steadily evolve its architecture so AI can move from proof-of-concept to proof-of-value.
Healthcare’s AI future won’t be built model-first; it will be built infrastructure-first.
Follow along this series over the next few months, as we unpack what it takes to get there – from multimodal data readiness and secure API layers to agent-driven workflows and, ultimately, how we’re approaching Axon as the AI agent foundation for our customers.
