| Medical terminologies,
nomenclatures, coding and classification systems: an introduction
terminologies or coding systems,
are structured list of terms which together
with their definitions are designed to describe unambiguously
the care and treatment of patients.
Terms cover diseases,
diagnoses, findings, operations, treatments, drugs, administrative
items etc., and can be used to support recording and reporting a
at varying levels of detail, whether on paper or, increasingly,
via an electronic medical record.
A nomenclature is a relatively simple system of names;
a vocabulary is a system of names with explanations of their meanings;
a classification is a systematic organisation of things into classes,
and a thesaurus (such as MeSH) is designed to index medical literature and support search over
But many of the terms used in this field
can prove difficult to define accurately,
and their use in practice can be inconsistent.
We refer readers for more detailed introductory
information and discussion on medical terminologies to the tutorial
by Jeremy Rogers of Manchester University (see link below),
and to Part 5 (chapters 16-18) of Enrico Coiera's Guide to Health Informatics entitled 'Language, coding and classification'.
Medical coding and classification
systems form part of current moves towards implementing a
for health": a common (computerized) medical language
for global use.
The US Institute of Medicine 2003 report, Patient Safety: Achieving a New Standard
for Care, highlights the importance of terminologies to healthcare
and provides the following summary
of their purpose and a likely outcome of current efforts
in the field:
"If health professionals are to be able to send and receive data in an understandable
usable manner, both the sender and the receiver must have common clinical terminologies
for describing, classifying, and coding medical terms and concepts. Use of standardized
clinical terminologies facilitates electronic data collection at the point of care;
retrieval of relevant
data, information, and knowledge; and reuse of data for multiple purposes
(e.g., disease surveillance, clinical decision support, patient safety reporting).
"No single terminology has the depth and breadth to represent the broad spectrum of
medical knowledge; thus a core group of well-integrated, non-redundant clinical
terminologies will be needed to serve as the backbone of clinical information
and patient safety systems." [Patient Safety: Achieving a New Standard for Care P14-15 (OC)]
Some discussion points:
- A very large number of coding and classification systems
have been developed for healthcare.
- Many standards have been proposed but widespread adoption
has been slow.
- Current standards tend to compete.
- Existing medical vocabularies vary in their coverage
- Many classifications overlap.
- Historically, vocabulary and classification systems have been designed
to meet different and specific goals.
Many codes have been designed mainly to support
administation (e.g. billing) so have typically
included, for example, only a limited number of diagnosis codes for
Widely-used but essentially
system, such as ICD, have been mandated by government
agencies and/or payor organizations but capture
clinical data at an insufficient level of detail to support clinical needs
that lie outside the limited range of activities they were
designed to support.
- Systems designed to cover clinical information have
tended to cover a relatively narrow subset of healthcare,
such as nursing procedures or problem lists.
- Some systems that concentrate on coding fine-grained
primary clinical data have been proprietary, custom-built,
limited, difficult for clinicians to use
and have resulted, in some cases, in low user acceptance.
- Coding systems can lose clinical information.
- It can be difficult to compare clinical coding systems.
- One stated ideal would be a system that allowed clinicians to record primary
clinical data using natural language which could be automatically
turned into standardized code.
- Interoperability is a significant problem. Content,
structure, completeness, detail, cross-mapping, taxonomy,
definitions, clarity vary between existing vocabularies.
- A single, comprehensive standard medical terminology
which would improve the automated
flow of clinical information
does not exist - though remains a goal for many.
- Many of established medical coding systems lack a precise
semantic underpinning. (The recent emergence of description
logic encoded medical terminologies - particularly SNOMED CT - aims to address this
- Comprehensive clinical terminology systems are needed
to help integrate patient data with health information technologies such as electronic medical records.
SNOMED CT aims to help structure and computerize the medical record
but needs to be used correctly and consistently to preserve data quality and maximise shareability.
- Integration of electronic patient records and medical terminologies with decision support systems is
being researched, for example by the SAGE project in the USA and as part of the DeGeL digital guideline library infrastructure (Israel/USA).
- Medical terminologies are evolving from relatively
"simple code-name-hierarchy structures, into rich,
knowledge-based ontologies of medical concepts" [Cimino,
- Recent work has aimed to build shareable and reusable
computerised vocabularies (such as GALEN)
- Semantic tools are under development to help end users
- The more recent emergence of Description Logic encoded
medical terminologies has the potential to facilitate
transition to the Semantic Web and pave the way for integration
of medical computer systems into E-Grid networks.
Cimino [Cimino, 1998],
continuing work published in [Cimino,
1989], has listed a set of desirable features for
computerised medical vocabularies in the 21st Century
offering the following propositions:
- Specify multiple hierarchies (rather than lists)
- Maintain formal definitions (replacing informal descriptions)
- Stress structurual and knowledge representation issues
(not just on expanding content)
- Maintain systematics approaches to updating vocabularies
- Ensure domain completeness
- Eliminate "not elsewhere classified" terms (which may be introduced to deal with incompleteness in a vocabulary)
- Allow for graceful evolution in vocabulary
design to support inclusion of new developments in healthcare
and error correction.
- Be able to recognise redundancy where the same information
is expressed in different ways.
- Ensure concept permanence: don't delete a concept
or change its meaning
- Don't include meaningless concept identifiers
- Support multiple granularities to meet the differing
needs of different users
- Maintain multiple consistent views: ensure consistency
over different views of a hierarchy
- Represent context specific information to maintain
the relationship between a concept and teh context in
which it is used.
|Introductory and general references
|E. Coiera. The Guide to Health
Informatics (2nd Edition). Arnold, London, October
17 sample chapter - freely available: Healthcare
Terminologies and Classification Systems]
Other relevant chapters in this book include:
- Chapter 16: Terms, codes and classification
- Chapter 18: The trouble with coding
This sample chapter (17) from part of Part 5 of Coiera's
book (Language, Coding and Classification)
covers the history of medical classification
systems and nonemclatures; discusses the characteristics,
benefits and limitations of a number of specific systems
(ICD, SNOMED ....), and looks at valid (and invalid)
ways of comparing them.
| J.H. van Bemmel, M.A. Musen (Editors).
The Handbook of Medical Informatics. Springer-Verlag,
New York, 1998.
on MIEUR website]
| Wyatt JC, Liu JL. Basic concepts in medical
informatics. J Epidemiol Community Health. 2002 Nov;56(11):808-12.
|" This glossary defines terms used in
the comparatively young science of medical informatics. It is
hoped that it will be of interest to both novices and professionals
in the field. "
de Lusignan S.
Codes, classifications, terminologies and nomenclatures: definition, development and application in practice.
Inform Prim Care. 2005;13(1):65-70.
The Primary Care Informatics Working Group of EFMI is working to help develop the core theory of primary care informatics (PCI). Codes, classifications, terminologies and nomenclatures form an important part of the science of PCI, as they allow clinical information to be readily stored and processed in information systems. This article provides definitions and a history of the International Classification for Primary Care (ICPC), and of the Read code and the Systematized Nomenclature for Medicine (SNOMED). The Working Group wishes to encourage shared definitions and an understanding of the practical application of structured data to improve quality in clinical practice.
comparisons and issues
| Rector AL. Clinical Terminology:
Why is it so hard? Methods of Information in Medicine
||Abstract " Despite years of work,
no re-usable clinical terminology has yet been demonstrated
in widespread use. This paper puts forward ten reasons
why developing such terminologies is hard. All stem
from underestimating the change entailed in using
terminology in software for 'patient centred' systems
rather than for its traditional functions of statistical
and financial reporting. Firstly, the increase in
scale and complexity are enormous. Secondly, the resulting
scale exceeds what can be managed manually with the
rigour required by software, but building appropriate
rigorous representations on the necessary scale is,
in itself, a hard problem. Thirdly, 'clinical pragmatics'--practical
data entry, presentation and retrieval for clinical
tasks--must be taken into account, so that the intrinsic
differences between the needs of users and the needs
of software are addressed. This implies that validation
of clinical terminologies must include validation
in use as implemented in software. "
Terminology, codes and classifications in perspective: the challenge of re-use.
Br J Healthcare Comput Info Manage 2000; 17(3): 20–3.
Despite years of effort at designing clinical coding systems, results have been unsatisfactory. Alan Rector explains the challenges.
Many healthcare organisations expend major resources on terminology-related problems, and the NHS is embarking on a major collaborative project. Despite efforts extending over two decades, however, there is still little consensus. Part of the explanation is that we have underestimated the magnitude of the changes implied by the strategy of deriving most information from support for patient care — from use by people to use by software, from single purpose use to multipurpose re-use, and from entry by coding staff to entry by healthcare professionals. This paper explores some of the implications of these changes and some responses to them.
| Cimino JJ. Review paper: coding
systems in health care. In: van Bemmel JH, McCray
AT, eds. IMIA Yearbook of Medical Informatics 1995.
Stuttgart, New York: Schattauer, 1995. Reprinted in
Methods of Information in Medicine; 1996;35(4-5):273-284.
| "Computer-based patient data
which are represented in a coded form have a variety
of uses, including direct patient care, statistical
reporting, automated decision support, and clinical
research. No standard exists which supports all of
these functions. Abstracting coding systems, such
as ICD, CPT, DRGs and MeSH fail to provide adequate
detail, forcing application developers to create their
own coding schemes for systems. Some of these schemes
have been put forward as possible standards, but they
have not been widely accepted. This paper reviews
existing schemes used for abstracting, electronic
record systems, and comprehensive coding. It also
discusses the remaining impediments to acceptance
of standards and the current efforts to overcome them,
including SNOMED, the Gabrieli Medical Nomenclature,
the Read Clinical Codes, GALEN, and the Unified Medical
Language System (UMLS). "
J.J. Desiderata for Controlled Medical Vocabularies
in the Twenty-First Century. Methods Inf Med. 1998
| " Builders of medical informatics
applications need controlled medical vocabularies
to support their applications and it is to their advantage
to use available standards. In order to do so, however,
these standards need to address the requirements of
their intended users. Over the past decade, medical
informatics researchers have begun to articulate some
of these requirements. This paper brings together
some of the common themes which have been described,
including: vocabulary content, concept orientation,
concept permanence, nonsemantic concept identifiers,
polyhierarchy, formal definitions, rejection of "not
elsewhere classified" terms, multiple granularities,
multiple consistent views, context representation,
graceful evolution, and recognized redundancy. Standards
developers are beginning to recognize and address
these desiderata and adapt their offerings to meet
Cimino JJ, Hripcsak G, Johnson SB, et al. Designing an introspective, multipurpose, controlled medical vocabulary. Proc 13th Annu Symp Comput Appl Med Care. 1989:513-8.
| Zielstorff, RD. Characteristics
of a good nursing nomenclature from an informatics
perspective. Online Journal of Issues in Nursing.
Sept. 30, 1998
Journal of Issues in Nursing]
| " The purpose for which a nomenclature
is designed dictates its characteristics. Very few
clinical nomenclatures have been designed for use
in automated record systems. For this reason, system
designers have had to adapt existing nomenclatures
and classification systems for use in the automated
systems they develop. Researchers have delineated
the characteristics of a "good" nomenclature for purposes
of structured data capture, storage, analysis, and
reporting. Some of these characteristics are: domain
completeness, granularity, parsimony, synonymy, non-ambiguity,
non-redundancy, clinical utility, multiple axes, and
combinatorial. In addition, the terms should have
unique and context-free term identifiers, each term
should have a definition, terms should be arranged
hierarchically with the ability to have multiple parents,
and it must be possible to map terms to other standard
classifications. These concepts are defined and rationalized
in the context of the functions expected of an automated
record system. "
| Campbell JR, Carpenter P, Sneiderman
C et al. Phase II evaluation of clinical coding schemes:
completeness, taxonomy, mapping, definitions, and
clarity. CPRI Work Group on Codes and Structures.
J Am Med Inform Assoc. 1997 May-Jun;4(3):238-51.
| " OBJECTIVE: To compare three
potential sources of controlled clinical terminology
(READ codes version 3.1, SNOMED International, and
Unified Medical Language System (UMLS) version 1.6)
relative to attributes of completeness, clinical taxonomy,
administrative mapping, term definitions and clarity
(duplicate coding rate). METHODS: The authors assembled
1929 source concept records from a variety of clinical
information taken from four medical centers across
the United States. The source data included medical
as well as ample nursing terminology. The source records
were coded in each scheme by an investigator and checked
by the coding scheme owner. The codings were then
scored by an independent panel of clinicians for acceptability.
Codes were checked for definitions provided with the
scheme. Codes for a random sample of source records
were analyzed by an investigator for "parent" and
"child" codes within the scheme. Parent and child
pairs were scored by an independent panel of medical
informatics specialists for clinical acceptability.
Administrative and billing code mapping from the published
scheme were reviewed for all coded records and analyzed
by independent reviewers for accuracy. The investigator
for each scheme exhaustively searched a sample of
coded records for duplications. RESULTS: SNOMED was
judged to be significantly more complete in coding
the source material than the other schemes ... SNOMED
also had a richer clinical taxonomy judged by the
number of acceptable first-degree relatives per coded
concept ... Only the UMLS provided any definitions;
these were found for 49% of records which had a coding
assignment. READ and UMLS had better administrative
mappings ... and SNOMED had substantially more duplications
of coding assignments ... associated with a loss of
clarity. CONCLUSION: No major terminology source can
lay claim to being the ideal resource for a computer-based
patient record. However, based upon this analysis
of releases for April 1995, SNOMED International is
considerably more complete, has a compositional nature
and a richer taxonomy. Is suffers from less clarity,
resulting from a lack of syntax and evolutionary changes
in its coding scheme. READ has greater clarity and
better mapping to administrative schemes (ICD-10 and
OPCS-4), is rapidly changing and is less complete.
UMLS is a rich lexical resource, with mappings to
many source vocabularies. It provides definitions
for many of its terms. However, due to the varying
granularities and purposes of its source schemes,
it has limitations for representation of clinical
concepts within a computer-based patient record. "
| de Keizer NF, Abu-Hanna A, Zwetsloot-Schonk
JH. Understanding terminological systems. I: Terminology
and typology. Methods Inf Med 2000 Mar;39(1):16-21.
| " Terminological systems are
an important research issue within the field of medical
informatics. For precise understanding of existing
terminological systems a referential framework is
needed that provides a uniform terminology and typology
of terminological systems themselves. In this article
a uniform terminology is described by putting relevant
fundamental notions and definitions used by standard
organizations such as CEN and ISO into perspective,
and interrelating them to arrive at a useful typology
of terminological systems. This typology is illustrated
by applying it to five well-known existing terminological
| de Keizer NF, Abu-Hanna A. Understanding
terminological systems. II: Experience with conceptual
and formal representation of structure. Methods Inf
Med. 2000 Mar;39(1):22-9.
| " This article describes the
application of two popular conceptual and formal representation
formalisms, as part of a framework for understanding
terminological systems. A precise understanding of
the structure of a terminological system is essential
to assess existing terminological systems, to recognize
patterns in various systems and to build new terminological
systems. Our experience with the application of this
framework to five well-known terminological systems
is described. "
| Strang N, Cucherat M, Boissel
JP. Which coding system for therapeutic information
in evidence-based medicine Comput Methods Programs
Biomed 2002 Apr;68(1):73-85.
Methods Programs Biomed]
| " The coding of information
in the computer representation of clinical trials
is essential both for the rationalisation of the activities
involved in the production of therapeutic information
for evidence-based decision support and for the integration
of the messages produced by these activities with
clinical information and electronic patient record
systems. There is no standard coding system available,
however, so building on existing evaluations, we performed
a simple semi-quantitative evaluation of ICD-10, CDAM,
MEDDRA, MESH, READ, SNOMED and UMLS to provide objective
criteria for the choice of a coding system. Inclusion
and exclusion criteria for four clinical trials recorded
in TriSum constituted the corpus of evaluation texts.
Criteria included coding coverage, size, integration
and language coverage. The results of the comparison
lead us to choose SNOMED as the most appropriate coding
system for our needs. The absence of a European Medical
Language System project is observed, as is the need
for combinatorial as opposed to enumerative systems.
Medical informatics standards applicable to emergency department information systems: making sense of the jumble.
Acad Emerg Med. 2004 Nov;11(11):1198-205.
[Acad Emerg Med]
The adoption of medical informatics standards by emergency department information systems (EDISs) is not universal, despite obvious benefits. Clinicians and administrators looking to obtain an EDIS need to know exactly what the various standards can do for them and how the systems they depend on can be integrated and extended. In addition to the standard methods for systems to communicate (chiefly Health Level 7 [HL7]) and those required for submission of claims (Current Procedural Terminology [CPT]-4, International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM], and X12N), there are several other available standards that are clinically useful and can greatly improve the ability to access and exchange patient information. Major advances in the Unified Medical Language System of the National Library of Medicine have made the patient medical record information standards (Systematized Nomenclature of Medicine [SNOMED], Logical Observation Identifiers, Names, and Codes [LOINC], RxNorm) easily accessible. Detailed knowledge of the arcana associated with the technical aspects of the standards is not needed (or desired) by clinicians to use standards-based systems. However, some knowledge about the commonly used standards is helpful in choosing an EDIS, interfacing the EDIS with the other hospital information systems, extending or upgrading systems, and adopting decision support technologies.
| Rector A, Rossi A, Consorti MF,
Zanstra P. Practical development of re-usable terminologies:
GALEN-IN-USE and the GALEN Organisation. Int J Med
Inf. 1998 Feb;48(1-3):71-84.
| " Medical terminology is now
playing a key role in medical software. This requires
new techniques with which many clinical users, classification
experts and applications developers are unfamiliar.
There is a conflict in that the more re-usable techniques
for terminology needed to support sharing of information
among many different applications are more difficult
to use for any one application. A layered approach
to re-use is described which combines techniques from
first generation systems and relatively easily understood
second generation systems with the formal rigour of
third generation systems to resolve this conflict.
The methodology also provides a potentially rigorous
approach to defining the relationship between terminology
and structure in the electronic healthcare record
architecture. It provides a natural migration pathway
from existing systems to powerful re-usable multilingual
JJ. Terminology tools: state of the art and practical
lessons. Methods Inf Med 2001;40(4):298-306.
| " OBJECTIVES: As controlled
medical terminologies evolve from simple code-name-hierarchy
arrangements, into rich, knowledge-based ontologies
of medical concepts, increased demands are placed
on both the developers and users of the terminologies.
In response, researchers have begun developing tools
to address their needs. The aims of this article are
to review previous work done to develop these tools
and then to describe work done at Columbia University
and New York Presbyterian Hospital (NYPH). METHODS:
Researchers working with the Systematized Nomenclature
of Medicine (SNOMED), the Unified Medical Language
System (UMLS), and NYPH's Medical Entities Dictionary
(MED) have created a wide variety of terminology browsers,
editors and servers to facilitate creation, maintenance
and use of these terminologies. RESULTS: Although
much work has been done, no generally available tools
have yet emerged. Consensus on requirement for tool
functions, especially terminology servers is emerging.
Tools at NYPH have been used successfully to support
the integration of clinical applications and the merger
of health care institutions. CONCLUSIONS: Significant
advancement has occurred over the past fifteen years
in the development of sophisticated controlled terminologies
and the tools to support them. The tool set at NYPH
provides a case study to demonstrate one feasible
| Cimino JJ. From data to knowledge
through concept-oriented terminologies: experience
with the Medical Entities Dictionary. J Am Med Inform
Assoc 2000 May-Jun;7(3):288-97.
| " Knowledge representation
involves enumeration of conceptual symbols and arrangement
of these symbols into some meaningful structure. Medical
knowledge representation has traditionally focused
more on the structure than the symbols. Several significant
efforts are under way, at local, national, and international
levels, to address the representation of the symbols
though the creation of high-quality terminologies
that are themselves knowledge based. This paper reviews
these efforts, including the Medical Entities Dictionary
(MED) in use at Columbia University and the New York
Presbyterian Hospital. A decade's experience with
the MED is summarized to serve as a proof-of-concept
that knowledge-based terminologies can support the
use of coded patient data for a variety of knowledge-based
activities, including the improved understanding of
patient data, the access of information sources relevant
to specific patient care problems, the application
of expert systems directly to the care of patients,
and the discovery of new medical knowledge. The terminological
knowledge in the MED has also been used successfully
to support clinical application development and maintenance,
including that of the MED itself. On the basis of
this experience, current efforts to create standard
knowledge-based terminologies appear to be justified.
|Jeremy Rogers, Medical Informatics
Group, University of Manchester. NHS Connecting for Health website.
|Entry on OpenClinical: 10 July 2005
Last main update: 24 July 2005