Many health tech vendors think about standardized electronic health record (EHR) data in terms of standard codes like ICD-10-CM, CPT, SNOMED CT®, or RxNorm®. These standards absolutely matter, but they are only part of the picture.
Every day, clinicians document care with a level of detail that goes far beyond what most downstream systems ever see. Problem lists, encounter diagnoses, referrals, procedures, medications, and labs all carry clinical intent and relationships shaped by IMO Health terminology and intelligence. But once that data moves through traditional interoperability channels, a lot of that nuance gets lost.
So, while vendors think they’re working with a complete patient record, they’re often missing the richness of the underlying clinical data already in the EHR.
The hidden layer behind structured EHR data
Most downstream systems only receive a narrow slice of EHR data that doesn’t capture the full clinical picture: standard codes, normalized vocabularies, and basic interoperability outputs.
Inside the EHR, clinicians document with the level of detail that drives actual care. They document real-world scenarios and encode severity, laterality, complications, procedural context, treatment intent, suspected conditions, and associated findings.
IMO Health defines the language and structured data that captures clinical intent and helps preserve and connect that level of detail for downstream systems, structuring relationships between clinical concepts so the original intent remains intact.
In many cases, this documentation is far more granular than ICD-10 alone can express. And it extends well beyond diagnoses to include procedures, medications, labs, and millions of interconnected data points.
As a result, there is significantly more clinical intelligence embedded in EHR data than most downstream applications ever access.
Why this matters for health tech vendors
This gap has real implications for vendors building analytics, artificial intelligence (AI), workflow, and automation tools.
If your platform only sees static, one-dimensional codes, it misses a lot of the context needed to understand what’s actually happening clinically.
That affects everything from cohorting and risk adjustment to clinical decision support and operational workflows. Broader codes can lead to less precise patient populations, weaker normalization, and analytics that lack precision.
AI models feel this too. The quality of the output depends heavily on the quality and specificity of the input data. When clinical context gets stripped away upstream, models lack the necessary context to infer the meaning that existed in the original documentation.
On the other hand, clinically enriched terminology helps improve signal quality. It produces cleaner inputs for automation, more reliable normalization across systems, and better alignment between what clinicians intended and what downstream tools interpret.
That’s one reason organizations that use IMO Health’s clinical terminology often see measurable improvements in areas like documentation integrity, denial management, normalization speed, and data consistency.
Why you might not be seeing this value today
The challenge is that most interoperability standards weren’t designed to preserve every layer of clinical meaning.
HL7 messages, FHIR resources, and similar standards are built to exchange data efficiently. In most cases, they pass along standardized codes and limited context – not the deeper terminology, relationships, and metadata behind them.
At the same time, EHRs maintain proprietary data models built on licensed terminology content. So even though clinicians interact with highly specific concepts inside the workflow, much of that intelligence never makes it to downstream systems.
That creates a blind spot for health tech vendors.
Many companies assume the data they receive is complete because it’s standardized. But standardized doesn’t always mean comprehensive and complete.
And importantly, this intelligence layer doesn’t originate from the EHR vendor itself. Much of the terminology and clinical relationship structure embedded across leading EHR environments comes from IMO Health.
That opens the door for a much broader set of use cases than many vendors realize, including:
- Analytics and reporting
- Ambient and generative AI
- Revenue cycle management
- Population health
- Clinical decision support
- Data normalization and interoperability
See what your data can really do
The good news is that this additional level of clinical specificity already exists inside the data being captured today. Vendors don’t need clinicians to document differently or organizations to overhaul their workflows.
The opportunity is to access and use existing data more effectively.
With IMO Health capabilities, health tech products can search and map clinical concepts more precisely, normalize and enrich fragmented datasets, and tap into knowledge graph relationships that support more advanced agentic, AI, analytics, and decision support use cases.
Those capabilities can work quietly behind the scenes through APIs and automation, and support human-in-the-loop workflows where transparency and validation matter.
Either way, the goal is the same: stop treating EHR data like a flat list of codes and invest in the richer layer of clinical meaning below it.
Learn more by speaking with an IMO Health expert. Schedule a demo today.
RxNorm® is a registered trademark of the National Library of Medicine.
SNOMED and SNOMED CT are registered trademarks of SNOMED International.
