As healthcare organizations rush to deploy large language models (LLMs), many are running into the same hard truth: LLMs are only as good as the structure beneath them.
As IQVIA recently noted, natural language models struggle to reliably reason across clinical data without help translating between medical ontologies and code systems. Clinical knowledge graphs provide essential structure to artificial intelligence (AI) models. In healthcare, however, the bigger challenge is ensuring graphs can communicate with one another, and that clinical meaning is grounded before AI reasoning begins.
Not all knowledge graphs are built for clinical reality
Most healthcare knowledge graphs in production today are organized around codes: ICD-10-CM, SNOMED CT®, CPT®, and related synonym-based nodes and schemas. For straightforward use cases, code-first anchoring can work. But in real-world care, code-only synonymy often breaks – code sets change meaning, vocabularies split or merge concepts, and terms get reused.
When you move beyond a short history or a narrow presentation, “good enough to bill” is not the same as “good enough to reason.” Studies cited by the American Medical Informatics Association and other research repeatedly show that crosswalk-driven approaches can produce incorrect connections – especially as clinical nuance and context increase.
As clinical data becomes more complex, a code-centric approach can no longer fully resolve ambiguity or represent patient context accurately. Codes were never designed to capture intent, nuance, or context. The result is a widening gap between what the data says, what the clinician means, and what downstream systems believe it means.
What “graph to graph” actually means
“Graph to graph” describes a division of responsibility across clinical data systems. Many existing graphs focus on actions such as evidence routing, workflow triggers, scoring, and alerts. IMO Health operates at a different layer: establishing consistent clinical meaning across observations, diagnoses, medications, patient history, and contextual data before those signals enter downstream structures.
As a clinical semantic control plane, IMO Health creates a clinician–grounded foundation that other healthcare knowledge graphs can safely build upon. Downstream systems can then apply proprietary logic, workflow intelligence, or domain‑specific context without having to reinterpret or reconstruct the underlying clinical meaning.
In practice, this helps customer graphs and AI agents start from clinically grounded intent rather than codes applied inconsistently across settings. This distinction matters because consistent grounding makes downstream interpretation more deterministic. Agents align more reliably across systems, and similar encounters are less likely to diverge because of upstream coding or language differences.
Why code-based grounding breaks at scale
Synonym-to-code mapping creates systematic blind spots and forces teams to constantly chase terminology drift: clinical language evolves, coding guidance changes, and specialty context reshapes meaning.
A PEDSNet case study illustrates the risks of relying solely on code-based mappings. Patients documented with “reflex sympathetic dystrophy of the lower limb” could be missed because the associated SNOMED crosswalks pointed to a deprecated code. In this case, a code-only approach to synonym mapping would have excluded the cohort entirely, and 886 pediatric patients could have received an incorrect evidence-based care recommendation if downstream decision support referenced only that representation.
Some pipelines may appear effective based on aggregate metrics, but failures are concentrated among the most clinically complex patients – where misinterpretation carries the greatest risk. This is also where clinician trust in AI begins to erode.
A trusted clinical semantic control plane allows teams to deploy agents with greater confidence instead of relying on brittle rules and constant retraining. When meaning is normalized at the foundation level, downstream models and workflows can scale without repeatedly compensating for interpretation errors upstream.
IMO health as the foundation of clinical AI
IMO Health’s Knowledge Graph serves as a foundational infrastructure within healthcare data architectures. Built around how clinicians speak and write, it provides a reliable starting point for any downstream knowledge graph – ensuring that additional context, domain logic, or AI‑driven insight is layered on top of clinically stable meaning rather than compensating for ambiguity underneath.
By continuously incorporating clinical terminology, and updating faster than standard code set releases, IMO Health allows technical teams to focus less on re-engineering interpretation and more on building effective clinical AI solutions.
IMO Health’s Knowledge Graph can be accessed and traversed through the Knowledge Graph API using GraphQL queries, enabling structured, flexible exploration.
Consistency is key
Integrating a knowledge graph does not guarantee clinical understanding. Graph‑to‑graph architectures recognize a simple rule: meaning must be established before action. The stronger and more stable that foundation, the more effectively downstream systems can evolve without breaking trust. This is an evolution in how healthcare data models relate, not a replacement for what already exists. For organizations building healthcare agents and agentic services, consistent clinical semantics are foundational.
See how IMO Health’s Knowledge Graph preserves clinical intent across the care journey so data can be understood, trusted, and used.
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