Guide to Health Informatics 2nd Edition
Enrico Coiera
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Chapter 17 - Healthcare terminologies and
classification systems
The terms disease and remedy were formerly understood and therefore
defined quite differently to what they are now; so, likewise, are the
meanings and definitions of inflammation, pneumonia, typhus, gout,
lithiasis, &c., different from those which were attached to them thirty years
ago…It is evident ... that great mischief will in most cases ensue if, in such
attempts at definition and explanation, greater importance is attached to a
clear and determinate, than to a complete and comprehensive
understanding of the objects and questions before us. In a field like ours,
clearness can in general be purchased only at the expense of
completeness and therefore truth.
Oesterlen, Medical Logic, (1855)
Coding and classification systems have a long history in medicine. Current
systems can trace their origins back to epidemiological lists of the causes of
death from the early part of the eighteenth century. François Bossier de Lacroix
(1706-1777) is commonly credited with the first attempt to classify diseases
systematically (ICD-10, 1993). Better known as Sauvages, he published the work
under the title Nosologia Methodica.
Linnaeus (1707-1778) who was a contemporary of Sauvages also published his
Genera Morborum in that period. By the beginning of the nineteenth century, the
Synopsis Nosologiae Methodicae, published in 1785 by William Cullen of
Edinburgh (1710-1790) was the classification in most common use.
It was John Graunt who, working about a hundred years earlier, is credited with
the first practical attempts to classify disease for statistical purposes. Working on
his London Bills of Mortality, he was able to estimate the proportion of deaths in
different age groups. For example, he estimated a 36% mortality for liveborn
children before the age of 6. He did this by taking all the deaths classified as
convulsions, rickets, teeth and worms, thrush, abortives, chrysomes, infants, and
livergrown. To these he added half of the deaths classed as smallpox, swinepox,
measles, and worms without convulsions. By all accounts his estimate was a
good one (ICD-10, 1993).
It has only been in the last few decades that these terminological systems have
started to attract wide-spread attention and resources. The ever growing need to
amass and analyse clinical data, no longer just for epidemiological purposes, has
provided considerable incentive and resources for their development. Further,
with the development of computer technology, there has been a belief that such
wide-spread collection and analysis of data are now possible. In parallel, the
requirement for clinicians to participate in that data collection has meant that they
have had more opportunity to work with terminologies, and begin to understand
their benefits and limitations.
In the previous chapter, the basic concepts of term, code, and classification were
introduced. In this chapter, several of the major coding and classification systems
in routine use in healthcare will be introduced, and their features compared.
Some specific limitations of each system will be highlighted. In reality there are a
large number of such systems in development and use, and they cannot all be
identified here. The systems discussed are however representative of most
systems in common use, and can serve as an introduction to them. Throughout,
a historical perspective will be retained, since in this case the lessons of the past
have deep implications for the present. The more general limitations of all
terminological systems will be addressed in the following chapter.
17.1
The International Classification of Diseases
Purpose. The International Classification of Diseases (ICD) is published by the
World Health Organisation (WHO). Currently in its tenth revision (ICD-10), its
goal is to allow morbidity and mortality data from different countries around the
world to be systematically collected and statistically analysed. It is not intended,
nor is it suitable, for indexing distinct clinical entities (Gersenovic, 1995). The
International Nomenclature of Diseases (IND) provides the set of recommended
terms and synonyms that correspond to the entries classified in the ICD codes.
History. The ICD can trace its ancestry to the early days of healthcare
terminologies. William Farr (1807-1883) became the first medical statistician for
the General Register Office of England and Wales. Upon taking office, he found
the Cullen classification in use, but that it had not been updated in accordance
with medical advances, nor did it seem suitable for statistical purposes. In his first
Annual Report of the Registrar General, he noted:
‘The advantages of a uniform statistical nomenclature, however imperfect,
are so obvious, that it is surprising that no attention has been paid to its
enforcement in Bills of Mortality. Each disease has, in many instances,
been denoted by three or four terms, and each term has been applied to
as many different diseases: vague, inconvenient names have been
employed, or complications have been registered instead of primary
diseases. The nomenclature is of as much importance in this department
of enquiry as weights and measures in the physical sciences, and should
be settled without delay. (ICD-10, 1993).’
Farr toiled hard at improving the classification, and by 1855, the International
Statistical Congress adopted a classification based on the work of Farr, and Marc
d’Espine of Geneva. Subsequently steered by Jaques Bertillon, this developed
into the International List of Causes of Death. This was adopted in 1893, and
continued to develop through the turn of the century and beyond, and ultimately
evolved into the current ICD system.
In particular, the system was expanded to include not just causes of death, but
diseases resulting in measurable morbidity. This expansion started with the
urging of Farr. It was supported by Florence Nightingale, who in 1860 urged the
adoption of Farr’s disease classification for the tabulation of hospital morbidity in
her paper Proposals for a uniform plan of hospital statistics. In 1900 at the First
International Conference to revise the Bertillon Classification, a parallel
classification of diseases for use in statistics of sickness was finally adopted.
Level of acceptance and use. The ICD today is used internationally by WHO for
comparison of statistical returns. It is also adopted by many individual countries
in the preparation of their statistical returns. Most other major classification
systems endeavour to make their systems compatible with ICD, so that data
coded in these systems can be mapped directly to ICD codes. ICD thus acts as a
defacto reference point for many healthcare terminologies.
Classification structure. The ICD-10 is a multiple-axis classification system. At its
core, the basic ICD is a single list of three alphanumeric character codes. These
are organised by category, from A00 to Z99 (excluding U codes which are
reserved for research, and for the provisional assignment of new diseases of
uncertain aetiology). This level of detail is the mandatory level for reporting to the
WHO mortality database and for general international comparisons.
The classification is structured into 21 chapters, and the first character of the ICD
code is a letter associated with a particular chapter (Table 17.1).
Chapter I
Chapter II
Chapter III
Chapter IV
Chapter V
Chapter VI
Chapter VII
Chapter VIII
Chapter IX
Chapter X
Chapter XI
Chapter XII
Chapter XIII
Infectious and parasitic diseases
Neoplasms
Diseases of the blood and blood forming
organs and certain disorders affecting the
immune mechanism
Endocrine, nutritional and metabolic diseases
Mental and behavioural disorders
Diseases of the nervous system
Diseases of the eye and adnexa
Diseases of the ear and mastoid process
Diseases of the circulatory system
Diseases of the respiratory system
Diseases of the digestive system
Diseases of skin and subcutaneous tissue
Diseases of musculoskeletal system and
Table 17.1: The
ICD-10 chapter
headings (adapted
from ICD-10, 1993).
Figure 17.1: The
ICD family of
disease and healthrelated
classifications
(adapted from ICD10, 1993).
Chapter XIV
Chapter XV
Chapter XVI
Chapter XVII
Chapter XVIII
Chapter XIX
Chapter XX
Chapter XXI
connective tissue
Diseases of the genitourinary system
Pregnancy, childbirth and the puerperium
Certain conditions originating in the perinatal
period
Congenital malformations, deformations and
chromosomal abnormalities
Symptoms, signs and abnormal clinical and
laboratory findings
Injuries,
poisoning
and
certain
other
consequences of external causes
External causes of morbidity and mortality
Factors affecting health status and contact with
health services of a person not currently sick
Within chapters, the 3 character codes are divided into homogenous blocks
reflecting different axes of classification. In Chapter I for example, the blocks
signify the axes of mode of transmission and of the broad group of the infecting
organism. Within Chapter II on neoplasms, the first axis is the behaviour of the
neoplasm, and the next is its site. Within all blocks some codes are reserved for
conditions not specified elsewhere in the classification.
When more detail is required, each category in ICD can be further subdivided,
using a fourth numeric character after a decimal point, creating up to 10
subcategories. This is used, for example, to classify histological varieties of
neoplasms. A few ICD chapters adopt five or more characters to allow further
subclassification along different axes.
Since ICD continues to be used for ever-wider applications beyond its intent, the
WHO decided in the 10th revision to develop the concept of a family of related
classifications surrounding this core set. This ‘family’ contains lists that have
been condensed from the full ICD, and lists expanded for speciality-based
adaptations (Figure 17.1). It also contains lists that cover topics beyond morbidity
and mortality. For example, there are classifications of medical and surgical
procedures, disablement and so forth (Gersenovic, 1995).
Primary Health Care
Information Support
ICD
lay reporting
community-based health
information schemes
3 character core
diagnoses
symptoms
abnormal
laboratory findings
injuries and
poisonings
external causes of
morbidity and
mortality
factors influencing
health status
Other health related
classifications
impairments, disabilities
and handicaps
procedures
reasons for encounter
short
tabulation
lists
ICD
4-character
classification
Speciality-based
Adaptations
oncology
dentistry
dermatology
psychiatry
neurology
obstetrics and
gynaecology
rheumatology and
orthopaedics
paediatric
general medial
practice
ICN
International
Nomenclature
of Diseases
The International Classification of Functioning, Disability and Health (ICF) is a
more recent member of the ICD ‘family’. While ICD-10 focuses on classifying a
patient’s diagnosis, ICF is aimed at capturing a description of their capacity to
function. ICF describes how people live with their health condition and describes
body functions and structures, activities and participation. The domains are
classified from body, individual and societal perspectives. Since an individual's
functioning and disability occurs in a context, ICF also includes a list of
environmental factors. The ICF is intended to assist with measuring health
outcomes.
Limitations. The ICD has developed as a practical, rather than theoretically
based, classification. There have been compromises between classification
based on axes of aetiology, anatomical site and so on. There have also been
adjustments made to it to meet the needs of different statistical applications
beyond morbidity and mortality, for example social security. As such, the ICD
exists as a practical attempt at compromise between various health care needs.
Consequently, for many applications, finer levels of detail may still be needed, or
other axes of classification required.
17.2
Diagnosis Related Groups
Purpose. Diagnosis Related Groups (DRGs) relate a patient’s diagnosis and
treatment to the cost of their care (Murphy-Muth, 1987; Feinstein, 1988).
Developed in the United States by the Health Care Finance Administration,
DRGs were designed to support the calculation of federal reimbursement for
healthcare delivered through the U.S. Medicare system.
A patient’s principal diagnoses and the procedures they are treated with during
hospital admission are used to select the group in the DRG classification that
most appropriately describes they overall type of care that has been delivered.
Next the group selected is associated with a typical cost. Specifically, DRG
funding requires the use of a cost weighting that is applied by the funding agency
to determine the actual amount that should be paid to an institution for treating a
patient with a particular DRG. The weightings are determined by a formula that is
typically developed on a state or national basis.
DRGs are also used to determine an institution’s overall case-mix. The case-mix
index helps to take account of the types of patient an individual institution sees,
and estimates their severity of illness. Thus a hospital seeing the same
proportion of patients as another, but dealing with more severe illness, will have
a higher case-mix index. An institution’s case-mix index can then be used in the
formula that determines reimbursement per individual DRG. Unsurprisingly
different versions of the reimbursement formula favour different types of
institution, and case-mix represents an area for ongoing debate and research.
History. In the mid 1970s the Centre for Health Studies at Yale University began
work on a system for monitoring hospital utilisation review (Rothwell, 1987).
Following a 1976 trial of a DRG system, it was decided to base the final system
on the ICD-9-CM which would provide the basic diagnostic categories. The ICD9-CM (clinical modification) classification was developed from the ICD-9 by the
American Commission on Professional and Hospital Activities. It contains finergrained clinical detail than the old ICD-9, and along with its successors
developed in various countries for ICD-10, is intended for healthcare review and
reimbursement use.
Level of acceptance and use. DRGs are used routinely in the United States for
management review and payment for Medicare and Medicaid patients. Given the
importance of reimbursement world-wide, DRGs have undergone ongoing
development, and have been adopted in one form or another in many countries
outside the USA, including Australia (AR-DRG), Canada (CMG) and countries of
Europe and Asia.
Classification structure. Patients are initially assigned a code from ICD-9 CM or a
clinical modification of ICD-10. ICD clinical modifications are multiaxial systems
closely based on the ICD structure. Diagnoses are then partitioned into one of
about 23 Major Diagnostic Categories (MDCs) according to body organ system
or disease. The aim of this step is to group codes into similar categories that
reflect consumption of resources and treatment (Figure 10.1). The categories are
next partitioned based upon the performance of procedures, and on other
variables such as the presence of complications and co-morbidities, patient age,
and length of stay, before a DRG is finally assigned (Rothwell, 1987). There is
thus a process of category reduction at each stage, starting from the many
thousands of ICD codes to the few hundred DRGs:
ICD  MDC  DRG
Limitations. Given the local variations in clinical practice, disease incidence,
patient selection, procedures performed, and resources, DRGs and case-mix
indices will always only give approximate estimates of the true resource
utilisation. For example, should a hospital that is developing new and expensive
procedures be paid the same amount as an institution that treats the same type
of patient with a more common and cheaper procedure? Should quality of care
be reflected in a DRG? For example, if a hospital delivers good quality of care
that results in better patient outcomes, should it be paid the same as a hospital
that performs more poorly for the same type of patient?
As importantly, those institutions that are best able to create DRGs accurately
are more likely to receive reimbursement in line with their true expenditure on
care. There is thus an implication in the DRG model that an institution actually
has the ability to accurately assemble information to derive DRGs and a casemix index. Given local and national variations in information systems and coding
practice, it is likely that institutions with poor information systems will be
disadvantaged, unless the information infrastructure across a region is a ‘level
playing field’.
Developments. DRGs are designed for use with inpatients. Accordingly, other
systems have been developed for other areas of healthcare. Systems such as
Ambulatory Visit Groups (AVGs) and Ambulatory Payment Classifications
(APCs) have been developed for outpatient or ambulatory care in the primary
sector. These are based upon a patient’s diagnosis, intervention, visit status and
physician time. Given the increasing age of the population in western nations,
there is a tremendous ongoing cost that comes from the chronic care needed by
the elderly. Consequently, systems such as Resource Utilisation Groups (RUGs)
and the Australian National Sub-Acute and Non-Acute Patient Classification (ANSNAP) have been developed to help determine the usage of sub-acute and longterm care resources. RUGs are based upon the time spent by nursing home staff
when caring for a patient. SNAP includes measures of functional ability.
17.3
The Read codes
Purpose. The Read codes (now simply called the Clinical Terms in the UK) are
produced for clinicians, initially in primary care, who wish to audit the process of
care. The Clinical Terms Version 3 (CTV3) is intended, like SNOMED
International, to code events in the electronic patient record (O’Neil et al., 1995).
History. The Read codes were introduced in the UK in 1986 to generate
computer summaries of patient care in primary care. In the subsequent revision
Version 2, their structure was changed and based upon ICD-9 and OPCS-4, the
Classification of Surgical Operations and Procedures. As Version 2 became
increasingly inadequate, the UK’s Conference of Medical Royal Colleges, and
the government’s National Health Service (NHS) established a joint Clinical
Terms Project, comprising some 40 working groups representing the different
specialities. This was subsequently joined by groups representing nurses and
Table 17.2: allied health professionals. Version 3 of the Read codes was created in
Example Read response to the output of the Terms project.
Version 3.1 Level of acceptance and use. Use of the Read codes is not mandatory in
template showing the UK. However, in 1994 it was recommended by the medical and nursing
allowable professional bodies as the preferred dictionary for clinical information
combinations of systems. The Read codes have been purchased by the UK government and
terms with qualifier made Crown Copyright.
attributes, and Classification structure. The Read codes have undergone substantive
attribute values changes through their various revisions, altering not just the classification
(adapted from and terminological content, but also their structure. In Versions 1 and 2,
O’Neil et al., 1995). Read was a strictly hierarchical classification system.
Read Version 3 is released in 2 stages and was a ‘superset’ of all previous
releases, containing all previous terms, to allow backward compatibility with past
versions. Version 3.0 is a kind of compositional classification system. Like
SNOMED, a term can appear in several different ‘hierarchical structures’,
classified against different axes. Unlike ICD or SNOMED, the codes themselves
do not reflect a given hierarchy. They simply act as a unique identifier for a
clinical concept. The ‘hierarchy’ exists as a set of links between concepts. Terms
can inherit properties across these links. For example, ‘pulmonary tuberculosis’
may naturally inherit from a parent respiratory disorder or a parent infection term.
In Version 3.1, a set of qualifier terms such as anatomical site was added that
can be combined with existing terms. When terms are composed, these
composites exist outside of any strict hierarchy. To help in the combination of
qualifiers with terms, they are grouped into templates. These capture some rules
that help describe the range of possible qualifiers that a term in Read can take
(Table 17.2).
Object
Bone operation
Fixation of fracture
Fixation of fracture
using intramedullary
nail
Fixation of fracture
using intramedullary
nail
Applicable
Attribute
Site
Reduction method
Reaming method
Nail Type
Applicable values
Bone, Part of Bone
Percutaneous, open,
closed
Hand, powered rigid,
powered flexible, etc.
Flexible, Locking,
Rigid, etc.
The Read Codes Drug and Appliance Dictionary is part of the Clinical Terms and
covers medicinal products, appliances, special foods, reagents and dressings.
The dictionary is designed for use in software that requires capture of medication
and treatment data such as electronic patient records and prescribing systems.
Like other major systems, Read offers mapping to ICD-9 codes to permit
international reporting, and in some cases also provides ICD-10 mapping. A set
of Quality Assurance Rules have been developed for the Clinical Terms which
are designed to check the clinical, drug and cross-mapping domains between the
current and previous versions of the terms and other major terminologies like
ICD-10, and for areas of overlap between the domains themselves (Schulz et al.,
1998). Each QA rule is written to interrogate the various files that make up the
Read Code releases and is designed to identify those concepts or terms that
violate the basic structure of the Read Codes.
Although Read Version 3 does not overtly emphasise axes of classification like
SNOMED, both systems allow terms to be linked to each other and to inherit
properties across those links. Therefore the underlying potential for
expressiveness is the same at the structural level. Differences in the number and
type of terms, and the richness of interconnections between them are probably
greater determinants of difference between these coding systems, than any
underlying structural difference. The presence of a fixed hierarchy, as we find
with ICD or SNOMED, carries certain benefits of regularity when exploring the
system. It also imposes greater constraints when it is necessary to alter the
system because of changes to the terminology. In Read, this burden of regularity
begins to be shifted to the rules guiding the composition of terms.
Limitations. The Read templates for term composition are limited in their ability to
control combination. A much richer language and knowledge base would be
needed to regulate term combination (Rector et al., 1995).
17.4
SNOMED
Purpose. The Systematized nomenclature of medicine is intended to be a
general-purpose, comprehensive and computer-processable terminology to
represent and, according to its creators, will index “virtually all of the events
found in the medical record” (Côté et al., 1993).
History. SNOMED was derived from the 1968 edition of the Manual of tumour
nomenclature and coding (MONTAC) and the Systematized nomenclature of
pathology (SNOP). SNOMED International (or SNOMED III) is a development of
the second edition of SNOMED, published in 1979 by the College of American
Pathologists (CAP).
Level of acceptance and use. SNOMED is reportedly used in over 40 countries,
presumably largely in laboratories for the coding of reports to generate statistics
and facilitate data retrieval. Although CAP is a not for profit organisation, in the
past SNOMED license fees have often been significant and may have impeded
its more widespread adoption.
Classification structure. SNOMED is a hierarchical, multi-axial classification
system. Terms are assigned to one of eleven independent systematised
modules, corresponding to different axes of classification (Table 17.3). Each term
is placed into a hierarchy within one of these modules, and assigned a five or six
digit alphanumeric code (Figure 17.2).
Figure 17.2:
SNOMED Codes
are hierarchically
structured. Implicit
in the code,
tuberculosis is an
infectious bacterial
disease.
D E– 1 4 8 0 0
Tuberculosis
Table 17.3: The
SNOMED
International
modules (or
axes).
Bacterial infections
E = Infectious or parasitic diseases
D = disease or diagnosis
Module designator
Topography (T)
Morphology (M)
Function (F)
Diseases/Diagnoses (D)
Procedures (P)
Occupations (J)
Living Organisms (L)
Chemicals, Drugs & Biological Products (C)
Physical Agents, Forces & Activities (A)
Social Context (S)
General Linkage-Modifiers (G)
Terms can also be cross-referenced across these modules. Each code carries
with it a packet of information about the terms it designates, giving some notion
of the clinical context of that code (Table 17.4).
SNOMED also allows the composition of complex terms from simpler terms, and
is thus partially compositional. SNOMED International incorporates virtually all of
the ICD-9-CM terms and codes, allowing reports to be generated in this format if
necessary.
Table 17.4: An
example of
SNOMED’s
Nomenclature
Classification
nomenclature and
Axis
T
+M
+L
+F
=D
classification. Some
Term Lung
+
+ M.
+ Fever
=
terms (e.g.
Granuloma
tuberculosis
Tuberculosis
Tuberculosis) can
Code T-28000 + M-44000
+ L-21801
+ F= DE-14800
be cross-referenced
03003
to others, to give
the term a richer
clinical context
(adapted from
Rothwell, 1995).
SNOMED RT (Reference Terminology) was released in 2000 to support the
electronic storage, retrieval and analysis of clinical data (Spackman et al, 1997).
A reference terminology provides a common reference point for comparison and
aggregation of data about the entire health care process, recorded by multiple
different individuals, systems, or institutions. Previous versions of SNOMED
expressed terms in a hierarchy that was optimized for human use. In SNOMED
RT, the relationships between terms and concepts are contained in a machineoptimised hierarchy table. Each individual concept is expressed using a
description logic, which makes explicit the information that was implicit in earlier
codes (Table 17.5).
SNOMED III termcode and English
nomenclature:
SNOMED III components of the
concept:
Cross-reference field in SNOMED
III:
Parent term in the SNOMED III
hierarchy:
Essential characteristics, in
SNOMED RT syntax:
D5-30150 Postoperative
esophagitis
T-56000 Esophagus
M-40000 Inflammation
F-06030 Post-operative state
(T-56000)(M-40000)(F-06030)
D5-30100 Esophagitis, NOS
D5-30150:
D5-30100 &
(assoc-topography T-56000) &
(assoc-morphology M-40000) &
(assoc-etiology F-06030)
Table 17.5:
Comparison
between implicitly
coded information
about
“postoperative
esophagitis” in
SNOMED III Codes
and the explicit
coding in SNOMED
RT. (from
Spackman et al,
1997)
Limitations. It is possible, given the richness of the SNOMED International
structure, to express the same concept in many ways. For example, acute
appendicitis has a single code D5-46210. However, there are also terms and
codes for ‘acute’, ‘acute inflammation’, and ‘in’. Thus this concept could be
expressed either as Appendicitis, acute; or Acute inflammation, in, Appendix; and
Acute, inflammation NOS, in, Appendix (Rothwell, 1995). This makes it difficult
for example, to compare similar concepts that have been indexed in different
ways, or to search for a term that exists in different forms within a patient record.
The use of description logic in SNOMED RT is designed to solve this problem.
Further, while SNOMED permits single terms to be combined to create complex
terms, rules for the combination of terms have not been developed.
Consequently such compositions may not be clinically valid.
17.5
SNOMED CT (Clinical Terms)
Purpose. SNOMED Clinical Terms is designed for use in software applications
like the electronic patient record, decision support systems, and to support the
electronic communication of information between different clinical applications.
A concept may be the
source of any number of
relationships
Figure 17.3:
Outline of the
SMOMED CT core
structure (after
College of
American
Pathologists, 2001).
Concept
A concept may be the
target of any number of
relationships
A concept may represent
the type of any number of
relationships
Relationship
A concept is described by
the term in one or more
descriptions
Description
Constraint: All Concepts except a designated
“Root” Concept are the source of at least one
“ISA” (subtype) Relationship. The “Root”
Concept is the target of "ISA“ Relationships
from each member of a set of “top level”
Concepts.
Its designers goal is that SNOMED CT should become the accepted international
terminological resource for healthcare, supporting multilingual terminological
renderings of common concepts.
History. In 1999 the College of American Pathologists and the UK NHS
announced their intention to unite SNOMED RT and Clinical Terms Version 3.
The stated intention in creating the common terminology was to decrease
duplication of effort and to create a unified international terminology that supports
the integrated electronic medical record. SNOMED CT was first released for
testing in 2002.
Level of acceptance and use. SNOMED CT supersedes SNOMED RT and the
Clinical Terms Version 3. It will gradually replace CTV3 in the UK as the
terminology of choice used in the National Health Service (NHS).
Classification structure. The SNOMED CT core structure includes concepts,
descriptions (terms) and the relationships between them (Figure 17.3). Like
SNOMED-RT and CTV3, SNOMED CT is a compositional and hierarchical
terminology. It is multiaxial and utilises description logic to explicitly define the
scope of a concept. There are 15 top-level hierarchies (Table 17.6). The
hierarchies go down an average of 10 levels per concept.
Procedure / intervention includes all purposeful activities performed in
the provision of health care.
Finding / disorder groups together concepts that result from an
assessment or judgment.
Measurable / observable entity includes observable functions such as
“vision” as well as things that can be measured such as “hemoglobin
level”.
Social / administrative concept aggregates concepts from the CTV3
“administrative statuses” and “administrative values” hierarchies as well
as concepts from the SNOMED RT “social context” hierarchy.
Body structure includes anatomical concepts as well as abnormal
body structures, including the “morphologic abnormality” concepts.
Organism includes all organisms, including micro-organisms and
infectious agents (including prions), fungi, plants and animals.
Substance includes chemicals, drugs, proteins and functional
categories of substance as well as structural and state-based
categories, such as liquid, solid, gas, etc.
Physical object includes natural and man-made objects, including
devices and materials.
Physical force includes motion, friction, gravity, electricity, magnetism,
sound, radiation, thermal forces (heat and cold), humidity, air pressure,
and other categories mainly directed at categorizing mechanisms of
injury.
Event is a category that includes occurrences that result in injury
(accidents, falls, etc), and excludes procedures and interventions.
Environment / geographic location lists types of environment as well
as named locations such as countries, states, and regions.
Specimen lists entities that are obtained for examination or analysis,
usually from the body of a patient.
Context-dependent category distinguishes concepts that have precoordinated context, that is, information that fundamentally changes the
type of thing it is associated with. For example, “family history of” is
context because when it modifies “myocardial infarction”, the resulting
“family history of myocardial infarction” is no longer a type of heart
disease. Other examples of contextual modifiers include “absence of”,
“at risk of” etc.
Attribute lists the concepts that are used as defining attributes or
qualifying attributes, that is, the middle element of the object-attributevalue triple that describes all SNOMED CT relationships.
Qualifier value categorizes the remaining concepts (those that haven’t
been listed in the categories above) that are used as the value of the
object-attribute-value triples.
Table 17.6: The
top-level hierarchies
of SMOMED CT.
SNOMED CT incorporates SNOMED RT and Clinical Terms Version 3 (Kim and
Frosdick, 2001) as well as mappings to classifications such as ICD-9-CM and
ICD-10. It is substantially larger than either SNOMED-RT or CTV3, containing
over 300,000 concepts, 400,000 terms and more than 1,000,000 semantic
relationships. SNOMED CT also integrates LOINC (Logical Observation Identifier
Names and Codes) to enhance its coverage of laboratory test nomenclature.
Most of the features of the parent terminologies are incorporated into SNOMED
CT. For example the CTV3 templates, although not explicitly named in the new
structure, are essentially functionally preserved in SNOMED CT.
Limitations: Since SNOMED CT is a compositional terminology, there is strong
requirement to prevent illogical compositions being created, and while a form of
type checking is implemented, explicit compositional controls are not evident in
the early releases of the terminology.
Reviewing a sample of 1,890 descriptions obtained from the initial merging of the
two parent terminologies found a 43% redundancy in terms (Sable et al, 2001).
While some terms were simply common to both parent systems, many terms
were problematic in some way. For example, some terms were either vague or
ambiguous, used the logical connectors ‘and’ and ‘or’ incorrectly, had flawed
hierarchy links, or contained knowledge about disease processes that should
have been beyond the scope of the terminology. Many of these problematic
terms were identified automatically, but many others required visual inspection
and discussion to be resolved. While the process of merging the two
terminologies has substantially improved the quality assurance standard of the
resulting terminology, these problems raise many issues fundamental to
terminology construction, which are discussed in the following chapter.
17.6
The Unified Medical Language System (UMLS)
Purpose: The UMLS is the Rosetta stone of international terminologies. It links
the major international terminologies into a common structure, providing a
translation mechanism between them. The UMLS is designed to aid in the
development of systems that retrieve and integrate electronic biomedical
information from a variety of sources and to permit the linkage of disparate
information systems, including electronic patient records, bibliographic
databases, and decision support systems. A long-term research goal is to enable
computer systems to "understand" medical meaning
History: In 1986, the U. S. National Library of Medicine (NLM) began a long-term
research and development project to build a Unified Medical Language System
(Humphreys and Lindberg, 1989).
Level of acceptance and use: Broad use of the UMLS is encouraged by
distributing it free-of-charge under a license agreement. The UMLS is widely
used in clinical applications, and the NLM itself uses the UMLS in significant
applications including PubMed and the web-based consumer health information
initiative at ClinicalTrials.gov.
Classification structure: The UMLS is composed of three "Knowledge Sources", a
Metathesaurus, a semantic network, and a lexicon (Lindberg et al, 1993).
The UMLS Metathesaurus provides a uniform format for over 100 different
biomedical vocabularies and classifications. Systems integrated within the UMLS
include ICD-9, ICD-10, the Medical Subject Headings (MeSH), ICPC-93, WHO
Adverse Drug Reaction Terminology, SNOMED-II, SNOMED-III, and the UK
Clinical Terms. The 2002AD edition of the Metathesaurus includes 873,429
concepts, 2.10 million concept names in its source vocabularies, and over 10
million relationships between them.
The Metathesaurus is organized by concept and does not include an overarching hierarchy. It can be conceptualised as a web rather than as a hierarchical
tree, linking alternative names and views of the same concept together and
identifying useful relationships between different concepts. This method of
structuring UMLS allows the component terminologies to maintain their original
structure within UMLS, as well as linking similar concepts between the
component terminologies.
Each concept has attributes that define its meaning, e.g., semantic types or
categories to which it belongs, its position in the source terminology hierarchy,
and a definition. Major UMLS semantic types include organisms, anatomical
structures, biologic function, chemicals, events, physical objects, and concepts or
ideas.
A number of relationships between different concepts are represented including
those that are derived from the source vocabularies. Where the parent
terminology expresses a full hierarchy, this is fully preserved in UMLS. The
Metathesaurus also includes information about usage, including the name of
databases in which the concept originally appears.
The UMLS is a controlled vocabulary and the UMLS Semantic Network is used to
ensure the integrity of meaning between different concepts. It defines the types
or categories to which all Metathesaurus concepts can be assigned and the
permissible relationships between these types (e.g., "Virus" causes "Disease or
Syndrome"). There are over 134 semantic types that can be linked by 54 different
possible relationships. The primary link is the `isa' link, which establishes the
hierarchy of types within the Network. A set of non-hierarchical relations between
the types includes `physically related to,' `spatially related to,' `temporally related
to,' `functionally related to,' and `conceptually related to.'
The SPECIALIST Lexicon is intended to assist in producing computer
applications that need to translate free-form or natural language into coded text.
It contains syntactic information for terms and English words, including verbs that
do not appear in the Metathesaurus. For example, it is used to generate natural
language or lexical variants of words e.g. the word “treat” has three variants that
all have the same meaning as far as the Metathesaurus is concerned: treats,
treated or treating.
Limitations: The very size and complexity of the UMLS may be barriers to its use,
offering a steep learning curve compared to any individual terminology system.
Its size also poses great challenges in system maintenance. Every time one of
the individual terminologies incorporated into UMLS changes, technically those
changes must be reflected in the UMLS. Consequently regular and frequent
updates to the UMLS are issued, and as the system grows the likelihood of
errors being introduced will increase, as we shall see in the next chapter.
Table 17.7: A
comparison of
coding for four
different clinical
concepts using
some of the major
coding systems
(National Centre for
Classification in
Health, Australia).
The richness of the linkages between concepts also offers subtle problems
at the heart of terminological science. For example, the ‘meaning’ of a
UMLS concept comes from its relationships to other concepts, and these
relationships come from the original source terminologies. However a
precise concept definition from one of the original terminologies like ICD or
SNOMED may be blurred by addition of links from another terminology that
contains a similar concept (Campbell et al, 1998). For example,
“gastrointestinal transit” in the Medical Subject Headings (MeSH) is used to
denote both the physiologic function and the diagnostic measure
(Spackman et al., 1997).
Since UMLS is not designed to contain an ontology, which could aid with
conceptual definition, it is difficult to control for such semantic drift.
Clinical
Concept
UMLS
ICD10
ICD9CM
Edition
Chronic
ischaemic
448589
Chronic
ischaemic
heart
disease
I25.9
Chronic
ischaemic heart
disease
Epidural
haematoma
"453700
Hematoma,
epidural"
Lymphosarcoma
Common
heart
disease
Cold
17.7
4th
READ 1999
SNOMED
International
1998
SNOMED
CT 2002
414.9 Chronic
ischaemic heart
disease
XE0WG Chronic
ischaemic heart
disease NOS
14020 Chronic
ischaemic heart
disease
84537008
Chronic
ischaemic
heart
disease
S06.4 Epidural
haemorrhage
432.0
Nontraumatic
extradural
haemorrhage
Xa0AC
Extradural
haematoma
89124 Extradural
haemorrhage
68752002
Nontraumatic
extradural
haemorrhage
"1095849
Lymphoma,
diffuse"
C85.0
Lymphosarcoma
200.1
Lymphosarcoma
B601z
Lymphosarcoma
"95923
Lymphosarcoma,
diffuse"
"1929004
Malignant
lymphoma,
nonHodgkin"
1013970
Common
cold
J00
Acute
nasopharyngitis
[common cold]
460
Acute
nasopharyngitis
[common cold]
XE0X1
Common cold
35210 Common
cold
82272006
Common
cold
Comparing coding systems is not easy
Unsurprisingly, the same clinical concept might look very different when coded
using different classification systems (Table 17.7) The different origins of the
systems, and the different revision histories each has had, inevitably result in the
use of different terms for similar concepts. While it is beguiling to try to compare
the utility of different coding systems, such comparisons are often ill-considered.
This is because it is not always obvious how to compare the ability of different
systems to code concepts found in a patient record. For example, Campbell et al.
(1994), reported results of various systems coding terms found in selected
problem lists from US patient records. They assessed that ICD-9-CM and Read
Version 2 ‘perform much more poorly for problem coding’ than either SNOMED
or the UMLS systems. As a consequence they concluded that ‘both UMLS and
SNOMED are more complete than alternative systems’ when developing
computer-based patient records.
Such generalisations are not meaningful. Firstly, term requirements vary from
task to task. Indeed, terms develop out of the language of particular groups on
particular tasks. It is thus not meaningful to compare performance on one task
and deduce that similar outcomes will result for tests on other tasks.
As critically, term use will vary between user populations. The terms used in a
primary care setting will differ to those used in a clinic allied to a hospital,
reflecting different practices and patient populations. Differing disease patterns
and practices also distinguish different nations. A system like Read Version 2,
designed for UK primary care, may not perform as well in US clinics as a US
designed system. The reverse may also be true of a US designed system applied
in the UK.
In summary, coding systems should be compared on specified tasks and
contexts, and the results should only cautiously be generalised to other tasks and
contexts. Equally the poor performance of coding systems on tasks outside the
scope of their design should not reflect badly on their intended performance.
Discussion Points
1. How likely is it that a single terminology system will emerge as an
international standard for all clinical activities?
2. Take the two terminologies created from the discussion section of the
previous chapter, and now merge the two into one common
terminology. As you go, note the issues that arise, and the methods
you used to settle any differences. Explain the rational (or otherwise)
basis for the merger decisions.
3. Are there any clinically significant differences that might arise out of the
different codings in Table 17.7? What impact might such differences
make on epidemiological surveys of population health?
4. You have been asked to oversee the transition from ICD-9-CM to ICD10-CM at your institution. What social and technical challenges do you
expect to face? How will you plan to deal with them?
5. Many countries will take a major terminology like ICD and customise it
to suit their local needs. Discuss the costs and benefits of this
approach from an individual country’s point of view. What might the
impact of localisation be on the collection of international statistics?
Chapter summary
1. The International Classification of Diseases (ICD) is published by the World
Health Organisation. Currently in its tenth revision (ICD-10), its goal is to allow
morbidity and mortality data from different countries around the world to be
systematically collected and statistically analysed.
2. Diagnosis Related Groups (DRGs) relate patient diagnosis to cost of
treatment. Each DRG takes the principle diagnosis or procedure responsible
for a patient’s admission, and is given a corresponding cost weighting. This
weight is applied according to a formula to determine the amount that should
be paid to an institution for a patient with a particular DRG. DRGs are also
used to determine an institution’s overall case-mix.
3. The Systematized Nomenclature of Medicine (SNOMED) is intended to be a
general-purpose, comprehensive and computer-processable terminology to
represent. Derived from the 1968 edition of the Manual of Tumour
Nomenclature and Coding, the second edition of SNOMED International is
reportedly being translated into twelve separate languages.
4. The Read codes are produced for clinicians, initially in primary care, who wish
to audit the process of care. Version 3 is intended, like SNOMED International,
to code events in the electronic patient record.
5. Coding systems should be compared on specified tasks, and results should
only cautiously be generalised to other tasks, and populations. Equally the
poor performance of coding systems on tasks outside their design should not
reflect badly on their intended performance.
| Resources | Glossary | References| Cover | Author |
ewc@pobox.com © Enrico Coiera 1997-2003
updated 10 Oct 03