Health Information Standards and Discovery

SNOMED CT: CORE Problem List Subset and Rule-Based Maps

Research Area: Health Information Standards and Discovery

Researchers: Kin Wah Fung

SNOMED CT:CORE project iconSNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms), is the most comprehensive, multi-lingual medical terminology in the world. It is emerging as the standard terminology clinical terminology for use in the Electronic Health Record (EHR). According to the "Meaningful Use" of the EHR incentive program of the Centers for Medicare & Medicaid Services (CMS), one of the certification criteria of EHR is that problem list data should be encoded in SNOMED CT. The problem list is considered to be an essential part of the Electronic Health Record (EHR) by various sanctioning bodies and medical information standards organizations, including the Institute of Medicine, Joint Commission, American Society for Testing and Materials and Health Level Seven. This lack of a common standard leads to duplication of effort and impedes data interoperability.

CORE Problem List Subset of SNOMED CT

Based on the analysis of the problem list vocabularies and their usage frequencies in eight large-scale US and overseas healthcare institutions, a subset of the most frequently used problem list terms in SNOMED CT was identified. This subset is published as the CORE (Clinical Observations Recording and Encoding) Problem List Subset of SNOMED CT. The CORE Subset can be used as a starter set for institutions that do not yet have a problem list vocabulary based on SNOMED CT. This will save significant development effort and reduce unintentional variations in the choice of terms. Existing problem list vocabularies can also be mapped to the CORE Subset which will facilitate data interoperability. Since its first publication in 2009, the CORE Subset has received considerable attention from the IHTSDO (International Health Terminology Standards Development Organization), the SNOMED CT user community, EHR software vendors and terminology researchers. The CORE Subset has been installed in various EHR products, and used as a focus for SNOMED CT-related research, mapping projects and quality assurance. The CORE Subset is updated 4 times a year to synchronize with changes in SNOMED CT and the UMLS. The CORE Subset currently contains about 6,000 concepts. Download the latest version here:

Other NLM-developed SNOMED CT Subsets

Mapping SNOMED CT codes to and from ICD codes

SNOMED CT is clinically-based, and oriented for direct use by healthcare providers, to document whatever is needed for patient care. ICD codes are oriented more for coding professionals to use after patient care has already been provided, for statistical data collection and billing. ICD codes lump less common diseases together in "catch-all" categories, for example, J15.8 Pneumonia due to other specified bacteria, which could result in loss of information. SNOMED Ct has more "granular" (specific) clinical coverage than ICD:SNOMED CT (clinical finding) has 100,000 codes, ICD-10-CM has 68,000 codes, and ICD-9-CM has 14,000 codes.

Due to the differences in granularity, emphasis and organizing principles between SNOMED CT and ICD-10-CM, it is not always possible to have a one-to-one map between a SNOMED CT concept and an ICD-10-CM code. (The same challenge applies for mapping SNOMED CT to and from ICD-10, or ICD-9-CM). To address this challenge, the SNOMED CT to ICD-10-CM Map follows an approach that is consistent with the approach used by the IHTSDO and WHO. When there is a need to choose between alternative ICD-10-CM codes, each possible target code is represented as a “map rule” (the essence of “rule-based mapping”). Related map rules are grouped into a “map group”. Map rules within a map group are evaluated in a prescribed order at run-time, based on contextual information and co-morbidities. Each map group will resolve to at most one ICD-10-CM code. In the event that a SNOMED CT concept requires more than one ICD-10-CM code to fully represent its meaning, the map will consist of multiple map groups.