Snowflake's latest research dropped a number that should make every healthcare CTO uncomfortable: 85% of healthcare leaders now view interoperability as foundational to scaling AI. Not "nice to have." Not "on the roadmap." Foundational.
And yet most health systems are still running AI pilots on isolated data silos, wondering why their models don't generalize beyond a single department.
The Pilot Trap
Healthcare AI has a pattern: build a model on one clean dataset, demonstrate value in a controlled setting, then watch it stall when you try to deploy it across the organization. The bottleneck is rarely the model. It's the data.
Most health systems have dozens of source systems — EHRs, claims platforms, lab systems, imaging archives, IoT devices, patient engagement tools. Each speaks its own dialect. HL7v2 here, FHIR R4 there, flat files from that legacy system nobody wants to touch. The AI pilot worked because someone spent three months hand-mapping fields from two systems. That approach doesn't scale to twenty.
This is interoperability debt. It accumulates silently while leadership celebrates pilot wins, and it comes due the moment you try to operationalize AI across the enterprise.
Interoperability Is a Data Engineering Problem Now
For years, interoperability lived in the integration team's domain — interface engines, HL7 routing, FHIR facades. That era is ending. When AI needs unified, high-quality, semantically consistent data across the entire organization, interoperability becomes a data engineering challenge.
The modern healthcare data stack needs to treat interoperability as a first-class concern, not an upstream problem someone else handles. That means:
- Semantic unification at the transformation layer. Your dbt models shouldn't just clean data — they should map disparate source terminologies to canonical concept sets. SNOMED, LOINC, RxNorm aren't optional enrichments. They're the foundation your AI models need to generalize.
- Contract-driven ingestion. Every source system feeding your warehouse should have a defined schema contract. When the EHR vendor changes their API response shape, you should know before your pipeline breaks — not after your model starts hallucinating on stale mappings.
- Streaming interoperability. Batch-oriented FHIR extracts won't cut it for operational AI. Snowflake Streams, CDC patterns, and event-driven architectures need to extend into clinical data flows. If your AI agent is making real-time recommendations, it can't be working with yesterday's labs.
MCP and the New Grounding Paradigm
One emerging pattern worth watching: Model Context Protocol (MCP) as a grounding layer for healthcare AI. RevSpring just launched what they're calling healthcare's first MCP grounding layer at HIMSS26, designed to give AI models structured, accurate access to provider data for patient billing interactions.
This matters because it addresses a fundamental problem with healthcare AI agents: they need real-time, contextual access to operational data, and the traditional API-based approach creates tight coupling between models and source systems.
MCP provides a standardized protocol for AI models to discover and interact with data sources. Think of it as FHIR for AI context — a common interface that lets models access the data they need without bespoke integrations for every source system. For data engineering teams, this means thinking about your data platform not just as a warehouse that serves dashboards and batch models, but as a context provider for autonomous AI agents. Your Snowflake instance isn't just a query engine anymore. It's a knowledge substrate that AI systems need to tap into — in real time, with appropriate governance and access controls.
What Your Data Team Should Be Doing Right Now
The 85% stat isn't surprising. What's surprising is how few organizations have translated this awareness into architectural action. Here's what separates teams that will scale AI from those that won't:
- Audit your semantic coverage. How many of your dbt models map source fields to standard terminologies? If the answer is "some" or "the ones we needed for that one project," you have interoperability debt.
- Build a canonical patient model. Not an MDM product. A dbt-driven, version-controlled, tested data model that resolves patient identity across systems and serves as the spine for all downstream AI.
- Invest in data contracts for clinical sources. Use dbt contracts, Great Expectations, or Soda to enforce schema and quality expectations at the boundary between source systems and your transformation layer.
- Prototype MCP integration. Even if you're not deploying AI agents today, build a proof-of-concept that exposes your curated data through MCP. The teams that figure out this pattern early will have a structural advantage when agentic AI goes mainstream in clinical workflows.
The health systems that scale AI won't be the ones with the best models. They'll be the ones that solved interoperability at the data engineering layer — quietly, unglamoriously, one semantic mapping and schema contract at a time. The 85% know the problem. The question is which teams are actually building the infrastructure to fix it.