SNOMED CT Overview
SNOMED CT is a clinical terminology containing concepts, with unique meanings and formal logic-based definitions, organized into hierarchies. The clinical content of SNOMED CT includes diagnoses and other clinical findings, clinical observations, drug products, organisms, specimen types, body structures, and surgical and non-surgical procedures.
SNOMED CT enables clinical information to be consistently represented at an appropriate level of detail within electronic health records. The relationships within SNOMED CT then facilitate meaning-based retrieval of this information at the preferred level of detail for the given query. This provides significant flexibility and facilitates the integration of data from divergent models of use, such as different user interfaces or databases, into convergent models of meaning, such as for the representation of data for reporting or statistical analysis purposes. Clinical systems can thereby query and analyze electronic health record data recorded in different settings, at varying levels of granularity and across multiple axes. This enables SNOMED CT to support a variety of clinical processes, which may require either detailed or high-level information - from investigation, to diagnosis and clinical research.
SNOMED CT content is represented using three main types of component:
Concepts - unique clinical meanings
Descriptions - human readable terms used to refer to a concept
Relationships - links between concepts that help to define the meaning of each concept
In addition to these three types of components, SNOMED CT also supports:
Expressions – a structured combination of one or more concept identifiers used to represent a new clinical meaning
Reference sets – a mechanism for representing references to SNOMED CT components for a variety of purposes, including subsets, aggregation hierarchies, maps and language preferences
In this section we introduce these SNOMED CT features and explain how they may be used to support analytics over health data. For more detailed information about SNOMED CT features, please refer to the SNOMED CT Starter Guide and the SNOMED CT Technical Implementation Guide.
We also discuss the specific benefits enabled by using SNOMED CT. For more details about the benefits of SNOMED CT please refer to Building the Business Case for SNOMED CT.
Concepts
SNOMED CT concepts represent clinical meanings. Each concept has a permanent concept identifier, which uniquely identifies the clinical meaning. For example:
SNOMED CT's concepts, and their logic-based definitions, allow analytics services to perform meaning-based queries, rather than purely lexical (or string-matching) searching.
Descriptions
SNOMED CT descriptions link appropriate human readable terms to concepts. Each concept can have many descriptions, which represent different synonymous ways of referring to the same clinical meaning. Each description is written in a specific language, and new descriptions can be created to support a variety of languages. Like concepts, descriptions also have a permanent unique identifier.
The richness of description content assists the process of searching and finding concepts using user interfaces or database queries. It may also be used to enhance string-matching in natural language processing applications, including analytics over multi-lingual data.
Relationships
SNOMED CT relationships represent an association between two concepts. Relationships are used to logically define the meaning of concept in a way that can be processed by a computer. A third concept, called a relationship type, is used to represent the meaning of the association between the source and destination concepts. There are different types of relationships available within SNOMED CT.
Subtype relationships, which use the |is a| relationship type, are the most widely used type of relationship. The SNOMED CT concept hierarchy is constructed from |is a| relationships. For example, the concept 128276007 |cellulitis of foot| has an |is a| relationship to both the concept 118932009 |disorder of foot| and the concept 128045006 |cellulitis|. Subtype relationships are used in many analytics scenarios to aggregate groups of concepts together, or to perform queries using more abstract (less detailed) concepts that match more specific (or more detailed) concepts stored in health records.
Attribute relationships contribute to the definition of the source concept by associating it with the value of a defining characteristic. For example, the concept |viral pneumonia| has a |causative agent| relationship to the concept |Virus| and a |finding site| relationship to the concept |lung|. Attribute relationships are used in analytics scenarios in which the meaning of a concept is needed to determine whether a record matches the query criteria.
Concept Model
The rules which define how SNOMED CT concepts may be defined are called the SNOMED CT concept model. The SNOMED CT concept model defines the permitted attributes and values that may be applied to each kind of concept. For example, concepts in the |clinical finding| hierarchy are permitted to have a |finding site| relationship, and the valid values of these relationships must belong to the |anatomical or acquired body structure| hierarchy. The SNOMED CT concept model provides the foundation for processing the clinical meanings recorded in clinical records and enables the appropriate use of clinical information for decision support and other analytics services.
Expressions
An expression is a structured combination of one or more concept identifiers used to represent a clinical meaning. SNOMED CT postcoordinated expressions enable clinical meanings to be represented, which cannot be represented using a single SNOMED CT concept. For example, the following postcoordinated expression represents 'pain in the left thumb:
53057004 |hand pain|:
363698007 |finding site|= (
76505004 |thumb structure|: 272741003 |laterality|=7771000 |left|)SNOMED CT postcoordinated expressions allow analytics services to perform meaning-based queries over a more extensive set of clinical meanings than just individual concepts.
Reference Sets
A reference set (or 'refset') is a mechanism used to refer to a set of SNOMED CT components and to add customized information to these components. Reference sets can be used for many different purposes, including representing subsets of concepts, descriptions or relationships, language and dialect preferences, maps to and from other code systems, ordered lists, navigation hierarchies and aggregation hierarchies. For more information about the different types of reference sets, please refer to the Reference Set Release Files Specification.
Reference sets are used for a range of analytics purposes, including:
Representing subsets of SNOMED CT concepts with which query criteria are defined and clinical records are matched;
To represent non-standard aggregations of concepts for specific use cases;
To define maps from other code systems to SNOMED CT so that clinical data can be prepared for analytics to be performed using SNOMED CT;
To define language or dialect specific sets of descriptions over which lexical searches can be performed.
Description Logic Features
SNOMED CT concepts are modelled in such a way that their meaning can be represented using a formal family of logics called Description Logic (DL). Description logic enables computers to make inferences about the concepts in SNOMED CT and their meanings, and to classify SNOMED CT using a DL reasoner. Description logic also allows the formal computation of:
Subsumption – Testing pairs of expressions to see whether one is a subtype of the other
Equivalence – Testing pairs of expressions to see whether they have the same logic-based meaning
Subsumption and equivalence are both extremely useful functions when retrieving or querying clinical information. For example, when retrieving all clinical records related to 73211009 |diabetes mellitus|, it would usually be necessary to retrieve records referring to any subtype of this concept, such as 23045005 |insulin dependent diabetes mellitus type 1A|.
Analytics Benefits of SNOMED CT
In addition to providing the features already described in this section, SNOMED CT also offers a number of additional benefits for the provision of analytics including:
SNOMED CT allows clinical data to be recorded at an appropriate level of detail, and then queried at either the same level or a less detailed level of detail;
SNOMED CT's broad coverage can enable queries across data captured within different disciplines, specialties and domain areas;
SNOMED CT provides a robust versioning mechanism, which helps to manage queries over longitudinal health records;
SNOMED CT is international, which enables queries, decision support rules and code system maps to be shared and reused between countries;
SNOMED CT includes localization mechanisms, which allow the same query to be applied to data from different countries, dialects, regions and applications;
SNOMED International provides maps between SNOMED CT and other international coding systems and classifications, including LOINC (Logical Observation Identifiers Names and Codes) and ICD (International Classification of Diseases, both ICD-10 and ICD-9-CM). This enables the additional benefits of these other specialized standards to be integrated with the use of SNOMED CT.
Using SNOMED CT to support analytics services can also enable the following benefits:
Enhancing the care of individual patients by supporting:
Retrieval of appropriate information for clinical care – e.g. for a clinical dashboard
Guideline and decision support integration
Retrospective searches for patterns requiring follow-up
Enhancing the care of populations by supporting:
Epidemiology monitoring and reporting
Research into the causes and management of diseases
Identification of patient groups for clinical research or specialized healthcare programs
Providing cost-effective delivery of care by supporting:
Guidelines to minimize risk of costly errors
Reducing duplication of investigations and interventions
Auditing the delivery of clinical services
Planning service delivery based on emerging health trends
Last updated