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SNOMED started in 1965 as a Systematized Nomenclature of Pathology (SNOP) and was further developed into a logic-based health care terminology. [6] [7]SNOMED CT was created in 1999 by the merger, expansion and restructuring of two large-scale terminologies: SNOMED Reference Terminology (SNOMED RT), developed by the College of American Pathologists (CAP); and the Clinical Terms Version 3 (CTV3 ...
In 2002 CAP's SNOMED Reference Terminology (SNOMED RT) was merged with, and expanded by, the National Health Service's Clinical Terms Version 3 (previously known as the Read codes) to produce SNOMED CT. [2] [3] Versions of SNOMED released prior to 2001 were based on a multiaxial, hierarchical classification system.
To support the implementation of SNOMED CT, a number of publications are produced by IHTSDO. These range from user guides to technical implementation guides as well as some educational materials and videos. Documents are available through the public website, but some items such as the videos can be found via YouTube.
Logical Observation Identifiers Names and Codes (LOINC) is a database and universal standard for identifying medical laboratory observations. First developed in 1994, it was created and is maintained by the Regenstrief Institute, a US nonprofit medical research organization.
The first version was developed in the early 1980s by Dr James Read, a Loughborough general medical practitioner. [2] The scheme was structured similarly to ICD-9: . each code was composed of four consecutive characters: first character 0-9, A-Z (excepting I and O), remaining three characters 0-9, A-Z/a-z (excepting i,I,o and O) plus up to three trailing period '.' characters
A central part of the openEHR specifications is the set of information models, known in openEHR as 'reference models'. [6] The models constitute the base information models for openEHR systems, and define the invariant semantics of the Electronic Health Record (EHR), EHR Extract, and Demographics model, as well as supporting data types, data structures, identifiers and useful design patterns.
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What hampers the use of SNOMED-CT are systems created which do not understand the cognitive models of clinicians and which do not understand how to properly use SNOMED-CT. User interfaces can be simplified (but only so much), and still capture complex concepts, but it requires better developers than what are working on the problem today.