Data standardization
Standardize data with domain-specific clinical AI
Achieve
accuracy for primary and secondary ICD-10-CM codes
Unlike open source LLMs that achieve as little as 34% coding accuracy, IMO Health delivers responsible AI that leverages 30 years of clinical point of care data.
Improve clinical data accuracy
Minimize manual work by automating the mapping of problem, procedure, lab and medication codes.
Extract and enrich clinical data
Leverage rich terminology and mapping to normalize unstructured data and connect to a wider range of codes.
Fast-track AI initiatives
Collaborate with IMO Health experts to speed AI development and future-proof your solutions.
Whether you're on the cusp of an AI transformation or accelerating your go-to-market strategy, IMO Health will meet you where you're at.
FAQS
Still have questions? Let's get them answered.
Click to explore our data standardization FAQs or contact us if you don’t see yours. We’re happy to help.
How can IMO Health accelerate my AI initiatives?
IMO Health can help your organization in a host of ways. For example:
Enhance your data lake: Integrate IMO Health’s comprehensive lexicals and harmonized code sets to structure and enrich your data, priming it for high-powered AI analytics and machine learning.
Ground your models in clinical truth: Implement IMO Health as the foundational grounding layer for your AI, helping to ensure model reliability and responsible outputs for any clinical use case.
Optimize RAG for healthcare: Utilize IMO Health to give your LLMs the clinical context they need. Enhance your retrieval-augmented generation framework to provide precise, verifiable answers that standard models can’t deliver.
Refine your recommendation engine: Leverage the deep relationships within the IMO Health knowledge layer to dramatically improve the precision and clinical relevance of your AI-driven recommendations.
Boost search relevancy: Integrate IMO Health’s clinical vocabulary and mappings to solve complex search queries, ensuring users receive the most relevant and accurate results across all related code sets.
To learn more, book a demo today.
How does IMO Health perform against other LLMs?
It’s no secret that LLMs tend to be poor medical coders. In particular, out-of-the-box models struggle with this complex and nuanced task that is integral to endless initiatives across the healthcare ecosystem.
In a recent test conducted by IMO Health, using a typical dataset, the top-performing out-of-the-box LLM achieved an accuracy of just 45% on ICD-10-CM code prediction. By enhancing the LLM with the UMLS as a RAG resource, IMO Health was able to improve the performance to 64%. However, when we evaluated the medical coding solution powered by IMO Health’s knowledge layer, which combines LLMs with our proprietary terminology, resources and techniques, the performance reached 92% accuracy on the same dataset.
In short, in order to be effective, LLMs must be well-trained and fine-tuned – and this is an area where IMO Health excels. Learn more in our guide, Optimizing LLMs for precise analytical output: The IMO Health approach.
What makes IMO Health's technology so effective?
IMO Health’s technology is built on clinical expertise and deep semantic understanding, embodied in our foundational knowledge layer. At its core is a continuously updated library of medical terminology and code mappings, curated over 30 years and refined through real-world clinical use. This content reflects how providers actually document at the point of care and is structured to capture meaning across specialties, contexts, and workflows.
Our solutions ingest billions of terminology interactions and harmonize millions of clinical concepts each month, using semantic models to normalize and clarify data. This allows us to transform inconsistent documentation into structured, interpretable information so AI systems grasp not just the words, but their intent.
Ultimately, IMO Health enables healthcare organizations to build AI and analytics on a clinically grounded, explainable foundation. From improving NLP to reducing manual mapping and enhancing model transparency, our knowledge layer bridges the gap between raw data and real clinical insight.