Health Data Semantic Normalization
Automated mapping solution that takes raw healthcare content in a variety of forms and performs terminology linking to healthcare standards such as USCDI, US Core, CDISC, or other industry requirements.
Take the data you have and map it to the data you need.
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Terminology Crosswalk
Develop concept maps from local terminology codes to the standards, or from one standard to another. Prepare initial maps in a fully automated way and finalize with lite human curation.
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Semantic Standardization
Start with FHIR resources, CCDA documents, or text strings containing medical content and normalize to USCDI or other terminology standards.
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NLP + Terminology Linking
Start with clinical notes, extract relevant entities that you are interested and link content to the terminologies you need. Can be used for concept indexing of journal articles, extraction of codable criteria from clinical trials writeups, or extraction of clinical information from hospital notes.
Real-world health data is complex, often existing as a mix of unstructured clinical narratives, scanned documents, local codes, and potentially legacy coded data. Much of this information is inaccurately coded or not coded at all, making interoperability effectively impossible.
Our AI-powered Automap converts complex, disparate data into standardized codes, delivering the accuracy and precision required to drive smarter patient screening, reliable querying, focused cohorting, and quality reporting across large or small healthcare data repositories.
Demonstrating Value
See a demonstration of our AutoMap UI as it performs a mapping exercise on a set of content to derive USCDI mappings for conditions, procedures, lab results, and medications across SNOMEDCT, ICD10PCS, LOINC, and RXNORM.
Contact info@terminologyhub.com to sign up for an account to test in our reference environment.