Terminology services enable the programmatic access of terminology content in a consistent way. Centralizing terminologies and representing them to disseminate predictable terminology content facilitates consistent meaning and shared understanding between users. Terminology services also ensure proper integration of other terminology-enabled software applications, optimal delivery of terminology artifact in harmony with a single consensus standard content model and data format (Vocabulary Task Force Public Hearing, 2010). Without terminology standards, gathering health information becomes difficult, health data comparison becomes next to impossible, interchange of data between health systems cannot occur and secondary uses such as research are not possible. 
Today’s terminology services have varied scope and capability. This variance in scope reflects the differences of local requirements of users availing the services. By determining the exact point of commonality for terminology services, consistent and uniform behaviors of common terminology services become possible. 
- Health terminology services serve as a means by which clinical applications can interoperate among standards and local terminologies and define common business functions for terminology applications. 
- By using terminology server software provided by a terminology service, terminology content and format can be federated or centralized and communication with other network applications becomes possible. 
- Using terminology services opens access to a common platform for terminology updates, which ultimately leads to optimal development and maintenance of terminology content such as use-specific subsets, local extension to existing standards and mappings that help connect concepts belonging to different sets of terminologies. 
The history of health terminologies dates back to the 17th century when English demographer John Graunt decided to study the statistical information about the causes of childhood mortality. He found that the rate of childhood mortality below the age of six was 36 percent. The most common causes of death in his “Bills of Mortality” include bloody flux, rising of the light, mortification, griping of the guts and teeth. 
In 1839, an English epidemiologist named William Farr expressed the medical community’s need for standard terminologies. He stated that disease has, in many instances, been described using three or four terms, and that each of these terms has been used to describe many different diseases: Inconvenient and quite vague names of diseases have been registered instead of primary diseases. 
In 1893, Paris released the Bertillon Classification of Causes of Death. A few years later, the International Lists of Causes of Death was published to classify the most common causes of mortality. ICD-1 was released in 1900 and eventually fell under the control of the World Health Organization (WHO). 
Currently, ICD-9 and Current Procedural Terminology (CPT) are the most widely used administrative terminologies. However, these so-called terminology standards are not designed to adopt Healthcare Information Technology (HIT) applications because they lack adequate structure and granularity, and they are not truly concept based. 
The history of the modern terminology services in the United States started in 1996 when Kaiser, Mayo and Lexicon technology released the YATN, also known as the “Yet Another Terminology Service.” In 1998, three terminology services were released: the MetaPhrase by Lexicon Technology, the UC Davis JTerm Terminology service and Lexicon Query Services (LQS). In 2003, Common Terminology Services (CTS) was implemented. A year later, CTS was updated to CTS1 through HL 7 balloted procedure. In 2009, CTS was updated to its latest version, the CTS2, by going into another ballot. 
Terminology services use three important components to transmit healthcare information across different computer systems within the organization and across different healthcare organizations:
Concepts represent unique ideas. In the healthcare sector, a concept is a generic idea or an abstract that is generalized from an occurrence. Concepts can happen all at the same time for different reasons. Different generalized approaches are needed to determine the exact meaning of these co-occurring concepts expressed on various medical free texts. 
Codes are used to uniquely identify different concepts. Computers use these codes to communicate concepts across different systems. 
Computers communicate concepts using codes. Likewise, human beings use terms to communicate concepts to other humans. Terms refer to concepts. Concepts know no boundary, ethnicity or nationality because they are language- independent. Conversely, terms are language-dependent. Terms depend on the knowledge limitations of users and the language of the person using them. 
Tools and Technique
Terminology services have seven kinds of tools, namely:
Terminology development/editing/maintenance tools
Terminology development/editing/maintenance tools support the clinical representation of concepts. They include important functions such as provision of a reference model using a type of frame-based approach of description logic, data to support version control and automated classification of new terms using a formal method of decomposing terminology services architecture into a set of task-achieving competences or behaviors. These tools should not only support the smallest concepts and simplest expressions but also the other formal structures of clinical document architectures and the resolution of inconsistencies across modelers. 
The examples of terminology development/editing/maintenance tools include:
- LexGrid – A framework for querying, representing and storing biomedical information coordinated by the Mayo Clinic Division of Biomedical Statistics and Informatics
- Medical Entities Dictionary Editor – A large repository of medical concepts derived from a variety of sources including LOINC, UMLS and ICD9-CM
- SNOMED CT development tools – A comprehensive clinical terminology originally created by the College of American Pathologists (CAP)
- Terminology Development Environment – Apelon’s premier solution for creation, evolution and maintenance of ontologies, and structured terminologies
- Protégé (ontology development in frames or OWL) – the model of underlying ontologies that aim to connect data integration of different healthcare applications
Terminology browsers tools
Terminology browsers allow the viewing of one or more terminologies. Aside from viewing, some browsers may also allow editing of terminologies (Appendix B: Terminology Services and Tools).
The examples of single terminology browsers include:
- RELMA (Regenstrief LOINC Mapping Assistant)– a terminology browser that uses the power of Windows to search through the healthcare database and assists in mapping local healthcare codes to LOINC codes
- MED (Medical Entities Dictionary) browser
- CLUE – the freeware browser of SNOMED CT
On the other hand, the examples of browsers for multiple terminologies include:
- Knowledge Source Server of the Unified Medical Language System
- LexGrid of Mayo
- HL 7 Common Terminology Services – the API used by HL7 Software to access terminological content
- Open GALEN – an open source terminology tool for building and delivering terminologies and medical concepts
- Mycroft of Apelon – a free, multi-terminology, standalone browser for browsing national standard terminologies
Terminology Mapping tools
Terminology mapping tools facilitate the mapping of terms from a local or source terminology to a target terminology (Appendix B: Terminology Services and Tools).
Examples of single terminology mapping tools include:
- SNOMED CT development tools
- Regenstrief LOINC Mapping Assistant (RELMA)
Comparatively, mapping tools that use multiple terminologies are:
- Health Level 7 Common Terminology Services
- LexGrid of Mayo
Concept-based indexing and retrieval tools
Concept-based retrieval and indexing tools use tagging and indexing for the proper retrieval of document sets.  The most popular concept-based indexing and retrieval tools include:
- Apelon’s Concept-Based Indexing and Retrieval Solution
- Open GALEN
- Unified Medical Language System – tools that aim to integrate and distribute key terminology, coding standards, and classifications to promote the creation of more effective interoperable healthcare information systems and services
- Cimino’s Infobutton Manager – context-based links that anticipate user needs from one information system to another
Terminology import and export tools
Terminology import tools facilitate the transfer of terminologies in various formats. Mayo’s LexGrid is an example of terminology import tools. 
Natural language processing tools
Natural language processing tools convert natural language into semantic structures and map these parsed terms to standardized terminology. Friedman’s MedLEE is the most popular natural language processing tool. 
Clinical terminology server
A clinical terminology server is a networked software component that aims to centralize access of terminology content by providing effective, complete and consistent terminology service for other healthcare network. 
Administration, which is the ability to manage the loading and exporting of content, and the managing of notifications, is one of the best practices expected from a health terminology service. It is best to note that the administrative functionality of a health terminology service should be exclusive and accessible only to health service administrators.
Search and Query
Terminology services help users experience more effective research inquiries. Using uncontrolled terms when searching causes significant issues from trivial variations in the way search queries are stated. Most people use different words for a single concept and many employ slightly different concepts for a single term. As a result, non-specialists may find access to applications frustrating. 
One of the best ways to achieve optimal search and query functionality of a terminology service is to increase consistency and improve access to web navigation systems and digital collections through vocabulary control. With optimal vocabulary control, the administration can reduce the ambiguity of natural language used when describing and retrieving items for information searching. 
Terms make up controlled vocabularies and they are derived from selected natural language used by vocabulary designers to retrieve information and represent concepts. Ambiguity of natural language as well as the synonyms related to different terms poses potential problems in creating concepts for different terms. Different terms may have the same concept. On the other hand, the same term may mean different concepts depending on how the natural language uses such term in forming sentences.
Using controlled vocabulary can greatly reduce ambiguity between terms by defining the scope of terms, providing a set of synonyms for each concept and restricting the scope of terms so that they will only be related to only one concept. At the simplest level, having a controlled vocabulary ensures consistency in indexing and searching while helping reduce the possible problems that may arise from homograph and synonym mismatches. At a more complex level, concept presentation in semantic structures and hierarchies may help the searcher and the indexer choose the most proper concept for their own purposes.
Authoring and Maintenance
Authoring and maintenance allow the proper creation and preservation of content. To achieve this terminology services requirement, the use of appropriate Application Programming Interface (APIs) is necessary.
APIs allow terminology services to achieve terminology and client “plug-and-play,” define functional contract between users and terminology providers, and facilitate the independent development of client software from service server software.
Association, which is the ability to associate a terminal service, enables the mapping of concepts and other associated attributes from a source terminology to a target terminology. Within a single code system, associations help creates relationships between existing concepts.
With the improvement of health terminology services producing newer terminologies, computer machines can now have better structure and granularity to promote the processing of healthcare information. Traditional patients record written as free texts have limited value as currently stored healthcare information. However, with newer terminologies, a more efficient capture of information at point-of-care can be achieved. Aside from this, sharing of important information between enterprises, which is technically termed as semantic interoperability, and processing of information become more efficient with these new improvements in health terminology services. 
With better terminology, health terminology services can have clean and unique representation of concepts. Only true synonyms of important terms will be available in software applications and computer systems. Newer standards in health terminology services will lead to the abolishment of loosely grouped similar terms in computer systems that will ultimately lead to better accuracy in health data retrieval. 
With the improvement of health terminology services, interrelationships between concepts will be better supported resulting in formation of rich ontologies and improved definition of concepts through their relationships with other concepts. 
The use of modifiers as concepts to support other concepts, such as in the phrase of mild difficulty of breathing, will become more evident with newer terminologies. Concepts gathered to phrases will be readily stored and retrieved without any additional effort at point-of-care; furthermore, minimal effort will be needed to generate any report using them. 
- Appendix B: Terminology Services and Tools (n.d.). Retrieved September 11th, 2015 from perspectives.ahima.org.
- Hamm, R. (2009, March 26). Terminology Services in Support of Healthcare Interoperability.
- Hamm, R. (2009, May 4). Terminology Services: A Key Technology for Interoperability.
- Stearns, M. (n.d.). Standards in HealthCare. Retrieved September 11th, 2015 from www.redwoodmednet.org
- Vocabulary Task Force Public Hearing (2010, September 2). Retrieved September 11th, 2015 from HealthIT.gov