Stop Treating FHIR Like a Database: Healthcare’s Billion-Dollar Storage Mistake
By Reece Robinson, CTO
Healthcare is drowning in its own data — and we’re storing it in the wrong places.
By 2020, the sector was generating 2.3 zettabytes of data each year — the equivalent of 2.3 trillion DVDs spinning silently in the dark. Yet most of it sits locked in formats designed for messaging, not meaning.
Meanwhile, up to 25% of healthcare spending — as much as $935 billion a year in the U.S. alone — is pure waste (FS-007, FS-008). That’s money lost to duplication, administrative sprawl, and diagnostic errors that still consume 17.5% of total U.S. healthcare expenditure (FS-009). The irony? Much of this waste exists because health systems can’t see their own data clearly enough to act on it.
FHIR Was Built to Move, Not to Think
FHIR, HL7, CCDA — these are the postal services of healthcare data. They move information from A to B. But we’ve made the postal truck our warehouse.
FHIR was built for exchange, not storage. Its JSON bundles are optimized for transmission, not query performance. You can’t ask a pile of HL7 messages: “Show me every patient with A1c over 8 and missed eye exams.”
Trying to store analytics-grade data in FHIR is like archiving national census results in envelopes because you like the stationery.
Raw Data Delivers Zero Insight
Healthcare’s core standards were never built for population analytics or clinical intelligence. They encode how data is sent, not what it means.
HL7 uses text strings delimited by pipes and carets.
CCDA buries meaning in XML templates.
FHIR wraps context in endlessly nested JSON.
They’re brilliant for interoperability. They’re terrible for analytics. And when systems force FHIR into the role of a database, they don’t get intelligence — they get metadata about their metadata.
The FHIR Storage Trap
Vendors keep promising the holy grail of “one standard for everything.” It doesn’t exist. Here’s what really happens:
Every FHIR update breaks your schema. Your data now depends on a committee’s release cycle.
Translation erodes fidelity. Each conversion from HL7 or CCDA to FHIR loses nuance and introduces clinical noise.
Complexity multiplies. Supporting multiple standard versions explodes your mapping debt.
Coverage remains incomplete. FHIR still can’t handle key determinants like housing or income without awkward hacks.
The result? A data architecture that costs more to maintain than the problems it was supposed to fix.
The Cost of Storing Wrong
When systems can’t integrate, the human and financial fallout is vast.
Nearly 100 million people are pushed into extreme poverty each year by healthcare costs (FS-001).
30% of rare-disease patients wait five to thirty years for the right diagnosis (FS-005).
One in ten people worldwide lives with a rare disease (FS-006).
Behind every statistic is a system that collects data but fails to learn from it.
Healthcare Intelligence Needs a Purpose-Built Core
Analytics-ready data requires a foundation designed for storage, governance, and query — not transmission. That’s why leading systems are shifting from FHIR-as-storage to independent Health Data Models that act as neutral layers between input and insight.
At Orchestral, our Health Data Model ingests any format — HL7, CCDA, FHIR, CSV — validates it, and organizes it into a clinically coherent structure ready for analytics, AI, and regulation.
It’s informed by standards, not enslaved to them.
How Independence Pays Off
Freedom from version chaos: FHIR evolves; your data model doesn’t break.
Governed flexibility: Add new domains through graphical configuration, not code rewrites.
Regulatory confidence: Audit-ready lineage and provenance out of the box.
Immediate insight: Population queries, quality measures, and AI-readiness from day one.
That’s how systems move from chasing compliance to running healthcare like an intelligent program.
Why It Matters
Healthcare’s problem isn’t that it lacks standards — it’s that it confuses standards for strategy. Every zettabyte of unstructured health data is a missed opportunity to close care gaps, cut waste, and deliver equity at scale.
The fix isn’t another API. It’s architecture. The Health AI Orchestrator connects these layers — ingest, model, govern, activate — so health systems can finally see, understand, and act as one.
Stop treating data exchange like data storage.
Use FHIR to move information.
Use an independent health data model to make it mean something.
Or keep paying for wasted storage, wasted time, and wasted lives.