Standardized Data Critical to Scaling AI, Healthcare Benefits

Why It Matters

Standardizing health data unlocks AI’s potential to reduce billions in admin spend and improve patient outcomes, reshaping the economics of U.S. healthcare.

Key Takeaways

• Interoperability seen as strategic, not compliance
• Intermountain building unified cloud data layer
• Standardized computable data reduces admin costs
• AI to support clinical decisions and population health
• CMS regulatory push accelerates data exchange initiatives

Pulse Analysis

The convergence of workforce strain and an aging population is forcing health systems to rethink data strategy. While many organizations still view data exchange as a compliance exercise, leaders like Dan Liljenquist argue that true interoperability requires "computable data"—standardized, structured information ready for machine analysis. Intermountain Health’s response is a cloud‑native unified data layer that extracts, normalizes, and semantically aligns EHR content, moving beyond simple FHIR API transfers and eliminating costly manual data cleaning.

This technical foundation creates a fertile environment for artificial intelligence to move from pilot projects to enterprise‑wide deployment. By feeding AI models with consistent, high‑quality data, Intermountain anticipates breakthroughs in medication management, early chronic‑disease intervention, and automated administrative workflows. The approach promises to cut the roughly $750 billion annual administrative burden, potentially saving hundreds of billions and reallocating resources toward direct patient care. Moreover, a standardized data model facilitates population‑health analytics, enabling providers to identify trends and intervene proactively across their multi‑state network.

Regulatory momentum, especially from CMS, is reinforcing this strategic shift. New policies incentivize data sharing and penalize fragmented records, aligning financial drivers with technology adoption. As more health systems emulate Intermountain’s model, the industry could see a cascade of AI‑enabled services that improve quality while containing costs. The strategic framing of interoperability and AI as growth engines rather than IT projects signals a broader transformation, positioning data as the core asset in the next era of value‑based care.