2020, international classification of diseases (icd) did not allow post-coordination and snomed ct was the only terminology that supported postcoordination. since 2020, a new version of icd-11 now also supports postcoordination. veterinary content the international edition of snomed ct only includes human terms. in 2014, clearly veterinary concepts were moved into a snomed ct veterinary extension. this extension is managed by the veterinary terminology services lab at the va-md college of veterinary medicine at virginia tech. known deficiencies and mitigation strategies 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 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. a further phenomenon which characterizes parts of snomed ct is the so-called epistemic intrusion. 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. 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. 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 principled ontological foundation was gradually accepted, as well as a better understanding of description logic semantics. redesign priorities were formulated regarding observables, disorders, findings, substances, organisms etc. translation guidelines 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, 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 for research 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 descendant 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 validation using consistent rules is important for the quality of snomed ct. to that end, in 2009, a prototype machine readable concept model (mrcm) was created by the snomed ct team. in a follow up work, this model is being revised to utilize snomed ct expression constraints. snomed ct and icd snomed ct is a clinical terminology designed to capture and represent patient data for clinical purposes. the international statistical classification of diseases and related health problems (icd) is an internationally used medical classification system; which is used to assign diagnostic and, in some national modifications, procedural codes in order to produce coded data for statistical analysis, epidemiology, reimbursement and resource allocation. both systems use standardized definitions and form a common medical language used within electronic health record (ehr) systems. snomed ct enables information input into an ehr system during the course of patient care, while icd facilitates information retrieval, or output, for secondary data purposes. in 2010s, the advantage of snomed ct over icd was the multiple parent hierarchy of snomed ct. since 2020 release of icd 11, this advantage is less important because icd-11 foundational level allows an icd 11 concept to have multiple parents. snomed cticd (mortality and morbidity statistics view) typeterminology systemclassification system purposeinformation inputinformation output functiondescribes and defines clinical information for primary data purposesaggregates and categorizes clinical information for secondary data purposes snomed ct and loinc loinc is a terminology that contains laboratory tests. since 2017, snomed international started creating terms for loinc components and created a set of snomed ct expressions that capture the meaning of many loinc terms. snomed ct and meddra there is overlap between meddra and snomed ct that is not beneficial for pharmaceutical industry. in 2021, two maps map between snomed ct and meddra were jointly published by both organizations (from snomed ct to meddra and from meddra to snomed ct). use snomed ct is used in a number of different ways, some of which are: it captures clinical information at the level of detail needed for the provision of healthcare through sharing data it can reduce the need to repeat health history at each new encounter with a healthcare professional information can be recorded by different people in different locations and combined into simple information views within the patient record use of a common terminology decreases the potential for differing interpretation of information electronic recording in a common way reduces errors and can help to ensure completeness in recording all relevant data standardised information makes analysis easier, supporting quality, cost effective practice, research and future clinical guideline development a clinical terminology allows a health care provider to identify patients based on specified coded information, and more effectively manage screening, treatment and follow up use cases more specifically, the following sample computer applications use snomed ct: electronic health record systems computerized provider order entry cpoe such as e-prescribing or laboratory order entry catalogues of clinical services; e.g., for diagnostic imaging procedures knowledge databases used in clinical decision support systems (cdss) remote intensive care unit monitoring laboratory reporting emergency room charting cancer reporting genetic databases access snomed ct is maintained and distributed by snomed international, an international non-profit standards development organization, located in london, uk.. the use of snomed ct in production systems requires a license. there are two types of license: country/territory membership in snomed international (charged according to gross national product). affiliate 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 website. license free subsets to facilitate adoption of snomed ct and use of snomed ct in other standards, there are license free subsets. for example, a set of 7 314 codes and descriptions is free for use by users of dicom-compliant software (without restriction to ihtsdo member countries). global patient set (gps) subset gps was released in sep 2019 and contains 21 782 concepts. top level concepts snomed ct concepts typically belong to a single hierarchy (with the exception of drug-device combined concepts). some hierarchies, have a concept model defined (e.g., clinical findings). for other domains (e.g., organism, substance, qualifier value), there is no concept model yet defined. procedure procedure example concept in this hierarchy represent procedures performed on a patient. there is a well established defined concept model for procedures. procedure site (direct or indirect) specifies on what part of body the procedure is performed. a separate set of rules exist for evaluation procedures. evaluation procedures are procedures where evidence is evaluated to support the determination of a value, inference or conclusion. evaluation procedures have additional attributes, such as 'has specimen','property' or 'measurement method'. event as of 2016, the event hierarchy does not have a concept model defined. in 2006, some concepts from the 'clinical finding' hierarchy were moved to the event hierarchy. those concepts retained some of their attributes. (e.g., causative agent) observable entities snomed international is working on creating a concept model for observable entities. body structure body parts represent one of the largest hierarchies within snomed ct. the modeling is based on foundational model of anatomy but it differs from the model in some aspects (e.g., region is taken as 3d region and not a 2d region). important attributes include: 'laterality', several types of 'part of' relationships, and 'is a'. pharmaceutical / biologic product pharmaceutical product example pharmaceutical and biologic products are modeled using constructs of active ingredient, presentation strength, and basis of strength. since 2018, harmonization of snomed ct drug content with idmp standard is an editorial goal. the following types of entities are present: medicinal product a higher level term grouping drugs. for example, 398731002 | product containing sulfamethoxazole and trimethoprim (medicinal product) | clinical drug concept that represents a concrete drug product as used in clinical practice. for example, 317335000 | product containing precisely esomeprazole 20milligram/1 each conventional release oral tablet (clinical drug)| dose form concept representing how the product is delivered. for example, 385219001 | conventional release solution for injection (dose form) |. authoring conventions a goal for snomed ct is consistency. several mechanisms are employed to ensure this. machine readable concept model is used to check for compliance with a set of rules. rules for creating fully specified name for a concept define allowed and not allowed patterns. when defining a concept, a proximal primitive parent rule is used (in stated definition) to employ best description logic derived classification of concepts. separate conventions govern grouping of relationships. ability to group related relationships is an important strength of snomed ct. rules in machine readable concept model (mrcm) specify by domain which relationships are never grouped (e.g., 'is a' or 'laterality' attributes) and which relationships are always grouped (e.g., 'finding site'). for correct subsumption inference, some relationships may be in a group but consist of a single relationship. another convention for snomed ct international edition is to avoid creating