SNOMED CT (Systematized Nomenclature of Medicine -- Clinical Terms), is a systematically organised computer processable collection of medical terminology covering most areas of clinical information such as diseases, findings, procedures, microorganisms, substances, etc. It allows a consistent way to index, store, retrieve, and aggregate clinical data across specialties and sites of care. It also helps organising the content of medical records, reducing the variability in the way data is captured, encoded and used for clinical care of patients and research.[1] The primary purpose of SNOMED CT is to support the effective clinical recording of data with the aim of improving patient care. It is a structured collection of medical terms that are used internationally for recording clinical information and are coded in order to be computer processable. It covers areas such as diseases, symptoms, operations, treatments, devices and drugs. Its purpose is to consistently index, store, retrieve, and aggregate clinical data across specialties and sites of care. It helps organizing the content of electronic health records systems, reducing the variability in the way data is captured, encoded and used for clinical care of patients and research. Specific language editions are available which augment the international Edition and can contain language translations as well as additional national terms. SNOMED CT is considered by some to be the most comprehensive, multilingual clinical healthcare terminology in the world. It provides for consistent information interchange and is fundamental to an interoperable electronic health record. It can be used to record the clinical details of individuals in electronic patient records and support application functionality such as informed decision making, linkage to clinical care pathways and knowledge resources, shared care plans and as such support long term patient care. The availability of free automatic coding tools and services, which can return a ranked list of SNOMED CT descriptors to encode any clinical report, could help healthcare professionals to navigate the terminology.
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Over four decades, SNOMED has developed from a pathology-specific nomenclature (SNOP) into a logic-based health care terminology.[2] In January 2002, SNOMED CT was created by the merger, expansion, and restructuring of the College of American Pathologists (CAP) SNOMED RT (Reference Terminology) and the UK National Health Service (NHS) Clinical Terms (also known as the Read codes).[3][4] The historical strength of the Systematized Nomenclature of Medicine (SNOMED) was its coverage of medical specialties, while the strength of Clinical Terms Version 3 was its terminologies for general practice. SNOMED CT cross maps to such other terminologies as ICD-9-CM, ICD-O3, ICD-10, Laboratory LOINC and OPCS-4. It supports ANSI, DICOM, HL7, and ISO standards. SNOMED CT is currently used in a joint project with the WHO as the ontological basis of the upcoming ICD 11. The National Library of Medicine (NLM), on behalf of the U.S. Department of Health and Human Services, entered into an agreement with College of American Pathologists for a perpetual license for the core SNOMED CT (in Spanish and English) and ongoing updates. The contract provides to NLM a perpetual license to distribute SNOMED within the NLM’s Unified Medical Language System UMLS Metathesaurus for no cost use within the U.S. by both U.S. government (federal, state, local, and territorial) and private organizations. The contract also covers updates to SNOMED CT issued by the College of American Pathologists between June 30, 2003 and June 29, 2008. In April 2007, SNOMED CT was acquired by IHTSDO, The International Health Terminology Standards Development Organisation. SNOMED CT is currently available in American English, British English and Spanish, with other translations under way or nearly completed in French, Dutch, Danish, and Swedish, .
SNOMED CT Concepts are representational units that categorize all the things that characterize health care processes and need to be recorded therein. In 2011, SNOMED CT includes more than 311,000 concepts, which are uniquely identified by a concept ID, i.e. the concept 22298006 refers to Myocardial infarction. All SNOMED CT concepts are organized into acyclic taxonomic (is-a) hierarchies; for example, Viral pneumonia IS-A Infectious pneumonia IS-A Pneumonia IS-A Lung disease. Concepts may have multiple parents, for example Infectious pneumonia is also a child of Infectious disease. The taxonomic structure allows data to be recorded and later accessed at different levels of aggregation. SNOMED CT concepts are linked by approximately 1,360,000 links, called relationships.
Concepts are further described by various clinical terms or phrases, called Descriptions, which are divided into Fully Specified Names (FSNs), Preferred Terms (PTs), and Synonyms. Each Concept has exactly one FSN, which is unique across all of SNOMED CT. It has, in addition, exactly one PT, which has been decided by a group of clinicians to be the most common way of expressing the meaning of the concept. It may have zero to many Synonyms. Synonyms are additional terms and phrases used to refer to this concept. They do not have to be unique or unambiguous.
SNOMED CT can be characterized as a multilingual thesaurus with an ontological foundation. Thesaurus-like features are concept–term relations such as the synonymous descriptions "Acute coryza", "Acute nasal catarrh", "Acute rhinitis", "Common cold" (as well as Spanish "resfrío común" and "rinitis infecciosa") for the concept 82272006.
Under ontological scrutiny SNOMED-CT is a class hierarchy (with extensive overlap of classes in contrast to typical statistical classifications like ICD). This means that the SNOMED-CT concept 82272006 defines the class of all the individual disease instances that match the criteria for "common cold" (i.e. a patient where cold is recorded in their record as head cold, and in another as Acute coryza can both be found as instances of common cold). The superclass (Is-A) Relation relates classes in terms of inclusion of their members. That is, all individual "cold-processes" are also included in all superclasses of the class Common Cold, such as Viral upper respiratory tract infection (Figure).
SNOMED CT's relational statements are basically triplets of the form Concept1 - Relationx - Concept2, with Relationx being from a small number of relation types (called linkage concepts), e.g. finding site, due to, etc. The interpretation of these triplets is (implicitly) based on the semantics of a simple [Description logic] (DL). E.g., the triplet Common Cold - causative agent – Virus, corresponds to the first-order expression
forall x: instance-of (x, Common cold) -> exists y: instance-of (y, Virus) and causative-agent (y, x)
or the more intuitive DL expression
Common cold subClassOf causative-agent some Virus
In the Common cold example the concept description is "primitive", which means that necessary criteria are given that must be met for each instance, without being sufficient for classifying a disorder as an instance of Common Cold . In contrast, the example Viral upper respiratory tract infection depicts a fully described concept, which is represented in description logic as follows:
Viral upper respiratory tract infection equivalentTo Upper respiratory infection and Viral respiratory infection and Causative-agent some Virus and Finding-site some Upper respiratory tract structure and Pathological-process some Infectious process
This means that each and every individual disorder for which all definitional criteria are met can be classified as an instance of Viral upper respiratory tract infection.
As of 2011, SNOMED CT content limits itself to a subset of the EL++ formalism, restricting itself to the following operators:
SNOMED CT provides a compositional syntax[5] for building new concepts. For example, there might not be an explicit concept for a burn in the palm of the hand. Using the compositional syntax it could be described as
413350009 | finding with explicit context | :
{ 246090004 | associated finding | = ( 284196006 | burn of skin | :
246112005 | severity | = 24484000 | severe |
, 363698007 | finding site | = 321472003 | structure of dermatoglyphic patterns of palm |)
, 408729009 | finding context | = 410515003 | known present |
, 408731000 | temporal context | = 410512000 | current or specified |
, 408732007 | subject relationship context | = 410604004 | subject of record |
}
Such expressions are said to have been 'post-coordinated'. When used, post coordinated conditions avoids the need to create large numbers of defined Concepts within SNOMED CT. However, most systems only allow for pre-coordinated conditions. Reliable analysis and comparison of any such post-coordinated expression - with respect to both those concepts already within the SNOMED CT release dataset and any other ad hoc concepts created or yet to be created by its community of end users - properly requires an appropriate machinery to efficiently process description logics expressions.
Earlier SNOMED versions had faceted structure ordered by semantic axes, requiring that more complex situations required to be coded by a coordination of different codes. This had two major shortcomings. On the one hand, the necessity of post-coordination was perceived as a user-unfriendly obstacle, which has certainly contributed to the rather low adoption of early SNOMED versions. On the other hand, uniform coding was difficult to obtain. E.g.,Acute appendicitis could be post-coordinated in three different ways[6] with no means to compute semantic equivalences. SNOMED RT had addressed this problem by introducing description logic formula. With the addition of CTV3 a large number of concepts were redefined using formal expressions. However, the fusion with CTV3, as a historically grown terminology with many close-to user descriptions, introduced some problems which still affect SNOMED CT. In addition to a confusing taxonomic web of many hierarchical levels with massive multiple inheritance (e.g. there are 36 taxonomic ancestors for Acute appendicitis), many ambiguous, context-dependent concepts have found their way into SNOMED CT. Pre-coordination was sometimes pushed to extremes, so there are, for example, 350 different concepts for burns found on the head.
Although browsers like CliniClue support the compositional syntax, there is almost no application in clinical practice, as it seems too complex for users and is not sufficiently supported by coding tools. DL-specific syntaxes, e.g. the Manchester Syntax,[7] as developed by the Semantic Web community would be more intuitive, but it would still require to semantically clarifying SNOMED-CT specific syntax elements such as the Role Group.[8]
A further phenomenon which characterizes parts of SNOMED CT is the so called epistemic intrusion.[9] In principle, the task of terminology (and even an ontology) should be limited to providing context-free term or class meanings. The contextualization of these representational units should be ideally the task of an information model.[10] Human language is misleading here, as we use syntactically similar expression to represent categorically distinct entities, e.g. Ectopic pregnancy vs. Suspected pregnancy. The first one refers to a real pregnancy, the second one to a piece of (uncertain) information. In SNOMED CT most (but not all) of these context-dependent concepts are concentrated in the subhierachy Situation with explicit context. A major reason for why such concepts cannot be dispensed with is that SNOMED CT takes on, in many cases, the functionality of information models, as the latter do not exist in a given implementation.
With the establishment of IHTSDO SNOMED CT became more accessible to a wider audience. Criticism of the state of the terminology was sparked by numerous substantive weaknesses as well as on the lack of quality assurance measures.[11] From the beginning IHTSDO was open regarding such (also academic) criticism. In the last few years considerable progress has been made regarding quality assurance and tooling.
The need for a more principles ontological foundation was gradually accepted, as well as a better understanding of description logic semantics. Redesign priorities were formulated regarding observables,[12] disorders, findings,[13] substances, organisms etc. Translation guidelines[14] were elaborated as well as guidelines for content submission requests and a strategy for the inclusion of pre-coordinated content. There are still known deficiencies regarding the "ontological commitment" of SNOMED CT,[15] e.g., the clarification of which kind of entity is an instance of a given SNOMED CT concept. The same term can be interpreted as a disorder or a patient with a disorder, for example Tumour might denote a process or a piece of tissue; Allergy may denote an allergic reaction or just an allergic disposition. A more recent strategy is the use of rigorously typed upper-level ontologies to disambiguate SNOMED CT content.
The increased take-up of SNOMED CT into applications in daily use across the world to support patient care is leading to a larger engaged community. This has led to an increase in the resource allocated to authoring SNOMED CT terms as well as to an increase in collaboration to take SNOMED CT into a robust industry used standard. This is leading to an increase in the number of software tools and development of materials that contribute to knowledge base to support implementation. A number of on-line communities that focus on particular aspects of SNOMED CT and its implementation are also developing.
In theory, description logic reasoning can be applied to any new candidate post-coordinated expressions in order to assess whether it is a parent or ancestor of, a child or other descendent of, or semantically equivalent to any existing concept from the existing pre-coordinated concepts. However, partly as the continuing fall-out from the merger with CTV3, SNOMED still contains undiscovered semantically duplicate primitive and defined concepts. Additionally, many concepts remain primitive whilst their semantics can also be legitimately defined in terms of other primitives and roles concurrently in the system. Because of these omissions and actual or possible redundancies of semantic content, real-world performance of algorithms to infer subsumption or semantic equivalence will be unpredictably imperfect.
SNOMED CT is used in a number of different ways, some of which are:
More specifically, the following sample computer applications use SNOMED CT:
SNOMED CT is maintained and distributed by the IHTSDO, an international non-profit standards development organization, located in Copenhagen, Denmark. The use of SNOMED CT in production systems requires a license. There are two models. On the one hand SNOMED CT can be achieved by national membership in the IHTSDO (charged according to the GNP). On the other hand it can be used via a corporate business license (dependent on the number of end users). LDCs (least developed countries) can use SNOMED CT without charges. For scientific research in medical informatics, for demonstrations or evaluation purposes SNOMED CT sources can be freely downloaded and used. The original SNOMED CT sources in tabular form are accessible by registered users of the Unified Medical Language System (UMLS) who have signed an agreement. Numerous online and offline browsers are available . Those wishing to obtain a license for its use and to download SNOMED CT should contact their National Release Centre, links to which are provided on the IHTSDO web site .