Every few years, a capability moves from “nice to have” to “table stakes,” and health tech teams face the same question: do we build it ourselves, or bring in a partner who’s already solved it?
Right now, that capability is the knowledge graph.
As AI applications increasingly need deterministic clinical context – not just terminology lookups – knowledge graphs have become foundational infrastructure for healthcare software. The demand is real: in a survey of 100 US health tech companies, nearly half said they’re likely or certain to invest in a clinical knowledge graph within two years – yet only 4% feel ready to build one. The appeal is straightforward: a knowledge graph grounds AI outputs in clinical truth and preserves the specificity models lose on their own.
Building one in-house is tempting. You already own your data model, you know your product, and standing up a graph database is a well-understood engineering exercise. But there’s a difference between building a graph and building a clinical knowledge graph – one that preserves meaning as data moves across documentation, coding, billing, and analytics. That’s where the effort gets underestimated.
It’s worth remembering how these projects tend to go. A McKinsey analysis of more than 6,000 IT projects found that only one in 200 delivered their intended benefits on time and within budget. On average, projects exceeded budgets by 75%, ran 46% behind schedule, and generated 39% less value than expected.
Clinical knowledge graphs combine the same characteristics that make enterprise technology projects difficult: specialized expertise, complex dependencies, and continuous evolution rather than a clear finish line.
Making the graph clinically true is challenging
The engineering is hard enough on its own – but connecting nodes and edges isn’t the hardest problem. The hard problem is making sure those connections are clinically true – and stay that way. Not all knowledge graphs are created equal; without deep clinical grounding, even well-structured data loses meaning over time.
To be effective, a healthcare knowledge graph has to be built on clinically validated terminology, mapped across standards such as SNOMED CT®, ICD-10-CM, and CPT®, and enriched with explicit and inferred relationships that agents and AI models can reason over – the foundation for reasoning, not the reasoner itself. Connecting a problem to its likely treatments, associated findings, and relevant procedures – accurately, bidirectionally, across millions of concepts – is the work of clinical informaticists, terminologists, and mapping analysts, not a schema.
Then comes the part nobody budgets for
Even if you build it, you’re not done. Codes change. Guidelines shift. Regulatory updates land whether you’re ready or not. A clinical knowledge graph is a living asset, and the maintenance is relentless: every term, mapping, and relationship must be pressure-tested and kept current, or the ripple effects spread across every system that depends on it.
This is the piece that health tech teams most often underestimate. Maintaining terminology and mappings pulls engineers and clinical staff away from the product you’re actually trying to ship, indefinitely.
One health tech company handed this work to IMO Health and cut manual data-quality effort by 92%, freeing 15+ full-time engineers to get back to the roadmap.
What over three decades buys you
IMO Health has spent nearly 30 years building and maintaining clinical terminology and mappings – the foundation every major US EHR relies on – and that same expertise underpins our Knowledge Graph.
Our Knowledge Graph is a continuously maintained, clinically governed context layer – not static reference content. It captures real-world signals from more than 23 million daily provider interactions, delivers roughly 8,000 validated updates a day, and runs every clinically relevant change through a formal governance process led by real people, with versioning, review, and rollback built in.
Grounded in unique IMO IDs that are typically more specific than the standard code sets they map to, it gives your applications and AI a stable, deterministic source of clinical meaning – the grounding layer that makes downstream analytics and AI more accurate and explainable.
Already invested in a knowledge graph? You don’t have to start over
Here’s the part that surprises people: choosing IMO Health doesn’t mean tearing out what you’ve built. Our Knowledge Graph supports graph-to-graph communication – it establishes clinician-grounded clinical meaning before data enters your downstream graph, so your systems can keep applying their own logic, workflows, and intelligence without having to reinterpret the underlying clinical semantics.
In other words, you can keep your graph and let ours do the clinical heavy lifting underneath it. Through REST and GraphQL APIs, or an MCP server for agentic workflows, it fits the architecture you already have.
The bottom line
Some technologies are worth building in-house. But a clinically accurate, continuously maintained knowledge graph is a decades-long commitment to expertise and upkeep that few teams can spare the resources for – and even fewer can afford to get wrong. The smarter investment is to build on a foundation that’s already trusted, already governed, and already maintained.
To see how IMO Health’s Knowledge Graph can give your team a clinically grounded foundation, without the build, connect with an IMO Health expert.
CPT is a registered trademark of the American Medical Association.
SNOMED and SNOMED CT are registered trademarks of SNOMED International.
