IMO Health is excited to announce the availability of natural language capabilities within IMO Precision Normalize. This new functionality will simplify the process to extract, standardize, and codify unstructured free text from sources like clinical electronic health record (EHR) notes.
With nearly 80% of clinical data existing in unstructured formats, there is a wealth of information that is inaccessible – making it highly inefficient to use. That’s because transforming clinical free text into structured, standardized, usable data is both costly and time-consuming.
IMO Precision Normalize with NLP is built on Melax Tech’s NLP development platform. Trusted by more than 650 organizations, Melax Tech solutions deliver proven tools and NLP expertise to support a variety of use cases. Trained on IMO Health’s rich, foundational terminology – which includes more than five million clinical terms spanning diagnosis, procedure, and medication domains along with comprehensive code mappings – the new IMO Precision Normalize is uniquely designed to accurately identify clinical concepts and operationalize both structured and unstructured data, faster.
Bridge the gap between free text and structured data, at scale, to unlock more value
IMO Precision Normalize with NLP:
- Extracts clinical concepts from unstructured free-text and standardizes information to a common terminology with comprehensive code mappings
- Identifies and connects clinical concepts and relationships across related entities
- Detects a variety of contextual metadata including time orientation, negation, and body location, for more specific and accurate matching
- Allows for flexible deployment with IMO Health-hosted or self-hosted options, and integrates seamlessly within tech stacks via API calls
This is IMO Health’s first solution to combine Melax Tech’s NLP capabilities with our robust clinical terminology and data quality platform. We are excited to help organizations across the healthcare industry to scale their data operations and extract more value from unstructured clinical data.
