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Methods of Information in Medicine
© F. K. Schattauer Verlagsgesellschaft mbH (1998)
I
,
Desiderata for Controlled Medical
Vocabularies in the Twenty-First
Century
Department of Medical Informatics,
Columbia University, New York, USA
Abstract: 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. Overthe 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 them.
Keywords: Controlled Medical Terminology, Vocabulary, Standards, Review
1. Introduction
The need for controlled vocabularies
in medical computing systems is widely
recognized. Even systems which deal
with narrative text and images provide
enhanced capabilities through coding of
their data with controlled vocabularies.
Over the past four decades, system
developers have dealt with this need by
creating ad hoc sets of controlled terms
for use in their applications. When the
sets were small, their creation was a
simple matter, but as applications have
grown in function and complexity, the
effort needed to create and maintain
the controlled vocabularies became
substantial. With each new system, new
efforts were required, because previous
vocabularies were deemed unsuitable
for adoption in or adaptation to new
applications. Furthermore, information
in one system could not be recognized
by other systems, hindering the ability
to integrate component applications
into larger systems.
Consider, for example, how a computer-based medical record system
might work with a diagnostic expert
system to improve patient care. In order
to achieve optimal integration of the
two, transfer of patient information
from the record to the expert would
need to be automated. In one attempt
to do so, the differences between the
controlled vocabularies of the two
systems was found to be the major
obstacle – even when both systems were
created by the same developers [1].
The solution seems obvious: standards [2]. In fact, many standards have
been proposed, but their adoption has
been slow. Why? System developers
generally indicate that, while they
would like to make use of standards,
they can’t find one that meets their
needs. What are those needs? The
answers to this question are less clear.
The simple answer is, “It doesn’t have
what I want to say.” Standards developers have taken this to mean that the
solution is equally simple: keep adding
terms to the vocabulary until it does say
what’s needed. However, systems developers, as users of controlled vocabularies, are like users everywhere: they
may not always articulate their true
needs. Vocabulary developers have
labored to increase their offerings, but
have continued to be confronted with
ambivalence. A number of vocabularies
have been put forth as standards [3] but
they have been found wanting in some
recent evaluations [4-6].
Over the past ten years or so, medical informatics researchers have been
studying controlled vocabulary issues
directly. They have examined the structure and content of existing vocabularies to determine why they seem
unsuitable for particular needs, and
they have proposed solutions. In some
cases, proposed solutions have been
carried forward into practice and new
experience has been gained. As we
prepare to enter the twenty-first century, it seems appropriate to pause to
reflect on this additional experience, to
rethink the directions we should pursue,
and to identify the next set of goals for
the development of standard, reusable,
mUltipurpose controlled medical vocabularies.
2. Desiderata
The task of enumeration of general
desiderata for controlled vocabularies
IS hampered in two ways. First, the
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J. J. Cimino
2.1 Content, Content, and Content
Like the three most important factors in assessing the value of real estate
(location, location, and location), the
importance of vocabulary content can
not be over stressed. The first criticisms
of vocabularies were almost universally
for more content. The need for expanded term coverage continues to be a
problem, as can be seen in numerous
studies which evaluate available standards for coverage of a particular
domains. For example, recent publications examining the domain of nursing
terminology are almost completely
focused on the issue of what can be said
[11-13]. Issues such as how things can be
said, or how the vocabulary is organized
are apparently less urgent, although
sometimes solutions may need to go
beyond the simple addition of more
terms [14, 15].
One approach to increasing content
is to add terms as they are encountered,
responding as quickly as possible to
needs as they arise [16]. In
approach, one adds complex expressions as needed rather than attempting a
systematic, anticipatory solution. For
example, rather than try to anticipate
every kind of fracture (simple vs. compound, greenstick vs. avulsion vs. compression, etc.) of every bone, one would
add terms for the most common and
add more as needed. This avoids the
large numbers of terms occurring
through combinatorial explosion and
the enumeration of nonsensical combinations (such as “compound greenstick
fracture of the stapes”, an anatomically
implausible occurrence for a small bone
in the middle ear).
An alternative approach is to enumerate all the atoms of a terminology and
allow users to combine them into necessary coded terms [17], allowing compositional extensibility [18]. The tradeoff is that, while domain coverage may
become easier to achieve, use of the
vocabulary becomes more complex.
Even with this atomic approach, the
identification of all the atoms is nontrivial. The atoms must be substantial
enough to convey intended meaning
and to preserve their meaning when
combined with others. They must be
more than simply the words used in
medicine. For example, the atom
“White” could be used for creating
terms like “White Conjunctiva” but
would be inappropriate to use in
constructing terms such as “WolffParkinson-White Syndrome”. The word
“White” needs to be more than a collection of letters – after all, we could represent all medical concepts with just the
letters of the alphabet (26, more or
less), but this would hardly advance the
field of medical informatics. The atomic
approach must also consider how to
differentiate between atoms and molecules. “White” and “Conjunctiva” are
almost certainly atoms, but what about
“Wolff-Parkinson-White Syndrome”?
No matter what approach is taken,
the need for adding content remains.
This occurs because users will demand
additions as usage expands and because
the field of medicine (with its attendant
terminology) expands. The real issue to
address in considering the “content
desideratum” is this: a formal methodology is needed for expanding content.
A haphazard, onesy-twosy approach
usually fails to keep up with the needs
of users and is difficult to apply consistently. The result can be a patchwork
of terms with inconsistent granularity
and organization. Instead, we need
formal, explicit, reproducible methods
for recognizing and filling gaps in
content. For example, Musen et al.
applied a systematic approach (negotiation of goals, anticipation of use, accommodation of a user community, and
evaluation) to creation of a vocabulary
for use in a progress note system [19].
Methods of similar rigor need to be
developed which can be used for
content discovery and expansion in
large, multipurpose vocabularies. More
attention must be focused on how
representations are developed, rather
than what representations are produced
[20].
2.2 Concept Orientation
Careful reading of medical informatics research will show that most
systems that report using controlled
vocabulary are actually dealing with
the notion of concepts. Authors are
becoming more explicit now in stating
that they need vocabularies in which
the unit of symbolic processing is the
concept – an embodiment of a particular meaning [21-25]. Concept orientation means that terms must correspond
to at least one meaning (“nonvagueness”) and no more than one meaning
(“nonambiguity”), and that meanings
correspond to no more than one term
(“nonredundancy”) [26, 27].
Review of the literature suggests that
there is some argument around the
issue of ambiguity. Blois argues that
while low-level concepts (such as pro395
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desired characteristics of a vocabulary
will vary with the intended purpose of
that vocabulary and there are many
possible intended purposes. I address
this issue by stating that the desired
vocabulary must be mUltipurpose.
Some of the obvious purposes include:
capturing clinical findings, natural
language processing, indexing medical
records, indexing medical literature,
and representing medical knowledge.
Each reader can add his or her own
favorite purpose. A vocabulary intended for any of these can, and often has
been, created. But the demands placed
on a vocabulary become very different
when it must meet several purposes.
A second obstacle to summarizing
general desiderata is the difficulty
teasing out individual opinions from the
literature and unifying them. The need
for controlled vocabularies for medical
computing is almost as old as computing
itself [7-9]. However, it is only in the
past ten years or so that researchers
have gone past talking about the
content of a vocabulary and started to
talk about deeper representational
aspects. Before then, the literature
contains many implications and halfstated assertions. Since then, authors
have become more explicit, but the
“terminology of terminology” has not
yet settled down to a level of general
understanding about what each of us
means when we discuss vocabulary
characteristics [10]. It is a foregone
conclusion, then, that the summary I
present here is bound to misrepresent
some opinions and overlook others.
With these disclaimers, then, I will
attempt to enumerate some of the
characteristics that seem to be emerging
from recent vocabulary research. Some
of them may seem obvious, but they
are listed formally in order that they not
be overlooked.
2.3 Concept Permanence
The corollary of concept orientation
is concept permanence: the meaning of
a concept, once created, is inviolate. Its
preferred name may evolve, and it may
be flagged inactive or archaic, but its
meaning must remain. This is important, for example, when data coded
under an older version of the vocabulary need to be interpreted in view of
a current conceptual framework. For
example, the old concept “pacemaker”
can be renamed “implantable pacemaker” without changing its meaning
(as we add the concept “percutaneous
pacemaker”). But, the name for the
old concept “non-A-non-B hepatitis”
can not be changed to “Hepatitis C”
because the two concepts are not
exactly synonymous (that is, we can’t
infer that someone diagnosed in 1980 as
having non-A-non-B hepatitis actually
had hepatitis C). Nor can we delete the
old concept, even though we might no
longer code patient data with it.
2.4 Nonsemantic Concept Identifier
If each term in the vocabulary is to
be associated with a concept, the
concept must have a unique identifier.
The simplest approach is to give each
concept a unique name and use this
for the identifier. Now that computer
storage costs are dropping, the need for
the compactness provided by a code
(such as an integer) has become less
compelling. If a concept may have
several different names, one could be
chosen as the preferred name and
the remainder included as synonyms.
However, using a name as a unique
identifier for a concept limits our ability
to alter the preferred name when necessary. Such changes can occur for a number of reasons without implying that the
associated meaning of the concept has
changed [32].
Because many vocabularies are organized into strict hierarchies, there has
been an irresistible temptation to make
the unique identifier a hierarchical code
which reflects the concept’s position in
the hierarchy. For example, a concept
with the code 1000 might be the parent
of the concept with the code 1200
which, in turn might be the parent of the
concept 1280, and so on. One advantage
to this approach is that, with some
familiarity, the codes become somewhat readable to a human and their
hierarchical relationships can be understood. With today’s computer inter-
faces, however, there is little reason
why humans need to have readable
codes or, for that matter, why they even
need to see the codes at alL Another
advantage of hierarchical codes is that
querying a database for members of
a class becomes easier (e.g., searching
for “all codes beginning with 1” will
retrieve codes 1000, 1200, 1280, and
so on). However, this advantage is lost
if the concepts can appear in mUltiple
places in the hierarchy (see “Polyhierarchy”, below); fortunately, there are
other ways to perform “class-based”
queries to a database which will work
even when concepts can be in multiple
classes [32].
There are several problems with
using the concept identifier to convey
hierarchical information. First, it is
possible for the coding system to run
out of room. A decimal code, such as
the one described above, will only allow
ten concepts at any level in the hierarchy and only allow a depth of four [34].
Coding systems can be designed to
avoid this problem, but other problems
remain. For example, once assigned a
code, a concept can never be reclassified without breaking the hierarchical
coding scheme. Even more problematic, if a concept belongs in more than
one location in the hierarchy (see
“Polyhierarchy”, below), a convenient
single hierarchical identifier is no
longer possible. It is desirable, therefore, to have the unique identifiers for the
concepts which are free of hierarchical
or other implicit meaning (i.e., nonsemantic concept identifiers); such information should instead be included as
attributes of the concepts [14].
2.5 Polyhierarchy
There seems to be almost universal
agreement that controlled medical
vocabularies should have hierarchical
arrangements. This is helpful for locating concepts (through “tree walking”),
grouping similar concepts, and conveying meaning (for example, if we see
the concept “cell” under the concept
“anatomic entity” we will understand
the intended meaning as different than
if it appeared under the concepts
“room” or “power source”). There is
some disagreement, however, as to
whether concepts should be classified
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tons) may be precisely defined, highlevel concepts, including clinical concepts like myocardial infarction, are
defined not by necessary attributes but
by contingent ones (e.g., the presence of
chest pain in myocardial infarction)
[28]. Moorman et aL suggest that ambiguity can be allowed in the vocabulary
as long as it is reduced to unequivocal
meaning, based on context, when actually used (e.g., stored in a clinical record) [29].
However, a distinction must be made
between ambiguity of the meaning of
a concept and ambiguity of its usage
[26, 30]. It is unfair, for example, to say
that the concept “Myocardial Infarction” is ambiguous because it could
mean “Right Ventricular Infarction”,
“Left Ventricular Infarction” and so
on. Any concept, no matter how finegrained, will always subsume some
finer-grained concepts. But “Myocardial Infarction” has a meaning which
can be expressed in terms of a particular
pathophysiologic process which affects
a particular anatomic site. Now, if we
use this concept to encode patient data,
the meaning of the data will vary with
the context (“Myocardial Infarction”,
“Rule Out Myocardial Infarction”,
“History of Myocardial Infarction”,
“Family History of Myocardial Infarction”, “No Myocardial Infarction”,
etc.).
This context-sensitive ambiguity is a
different phenomenon from contextindependent ambiguity that might be
found in a controlled vocabulary [31].
For example, the term “Diabetes” does
not subsume “Diabetes Mellitus” and
“Diabetes Insipidus”; it has no useful
medical meaning (vague). The concept
“MI” might mean “Myocardial Infarction”, “Mitral Insufficiency”, or “Medical Informatics”; before it even appears
in a context, it has multiple meanings (ambiguous). Concept orientation,
therefore, dictates that each concept in
the vocabulary has a single, coherent
meaning, although its meaning might
vary, depending on its appearance in a
context (such as a medical record) [29].
lI
hierarchies.
.. Different vocabulary users may
aeJ:Ill111U different, equally valid, arranof concepts. It seems unlikely
there can ever be agreement on a
arrangement that will satisfy all;
lience the popular demand for multiple
. hierarchies [31, 34-36]. Zweigenbaum
and his colleagues believe that concept
classification should be based on the
essence of the concepts, rather than
arbitrary descriptive knowledge [37].
They argue quite rightly that arbitrary,
user-specific ad hoc classes can still
be available using additional semantic
information. However, unless there can
be agreement on what the essence of
concepts should be, there can never be
agreement on what the appropriate
hierarchy should be. Furthermore, if
the essence of a concept is defined by
its being the union of the essence of
two other concepts, its classification becomes problematic.· For example, until
medical knowledge advances to provide
abetter definition, we must define the
essence of “hepatorenal syndrome” as
the occurrence of renal failure in patients with severe liver disease. If our
vocabulary has the concepts “liver disease” and “renal disease” (which seem
desirable or at least not unreasonable),
“hepatorenal syndrome” must be a
descendant of both.
There can be little argument that
strict hierarchies are more manageable
and manipulable, from a computing
standpoint, than polyhierarchies. This is
small consolation, however, if the vocabUlary is unusable. General consensus,
seems to favor allowing mUltiple hierarchies to coexist in a vocabulary without
arguing about which particular tree is
the essential one. It is certainly possible
that if a single hierarchy is needed for
computational purposes, one could be
so designated with the others treated
as nonhierarchical (but nevertheless
directed and acyclic) relationships.
2.6 Formal Definitions
Many researchers and developers
have indicated a desire for controlled
vocabularies to have formal definitions
in one form or another [23, 25-27, 36,
38-50]. Usually, these definitions are
expressed as some collection of relationships to other concepts in the vocabulary. For example, the concept
“Pneumococcal Pneumonia” can be
defined with a hierarchical (“is a”) link
to the concept “Pneumonia” and a
“caused by” link to the concept “Streptococcus pneumoniae”. If “Pneumonia”
has a “site” relationship with the
concept “Lung”, then “Pneumococcal
Pneumonia” will inherit this relationship as well. This information can be
expressed in a number of ways, including frame-based semantic networks
[40], classification operators [51], categorical structures [52], and conceptual
graphs [53-55]. The important thing to
realize about these definitions is that
they are in a form which can be manipulated symbolically (i.e., with a computer), as opposed to the unstructured
narrative text variety, such as those
found in a dictionary. Many researchers
have included in their requests that
the definitional knowledge be made
explic-itly separated from assertional
knowledge which may also appear in
the vocabulary [25, 41, 43, 46, 56]. For
example, linking “Pneumococcal Pneumonia”, via the “caused by” relationship, to “Streptococcus pneumoniae” is
definitional, while linking it, via a “treated with” relationship, to “Penicillin”
would be assertional. Similarly, the
inverse relationship (“causes”) from
“Streptococcus pneumoniae” to “Pneumococcal Pneumonia” would also be
considered assertional, since it is not
part of the definition of “Streptococcus
pneumoniae” .
The creation of definitions places
additional demands on the creators of
controlled vocabularies. However, with
careful planning and design, these
demands need not be onerous. For
example, the definition given for
“Pneumococcal Pneumonia”, given
above, only required one additional
“caused by” link to be added, assuming
that it would be made a child of “Pneumonia” in any case and that the concept
pneumoniae”
was
“Streptococcus
already included in the vocabulary.
Many of the required links can be generated through automatic means, either
by the processing of the concept names
directly [18] or through extraction from
medical knowledge bases [57]. Also, the
effort required to include definitions
may help not only the users of the vocabulary, but the maintainers as well: formal definitions can support automated
vocabulary management [58], collaborative vocabulary development [59],
and methods for converging distributed
development efforts [60,61].
2.7 Reject “Not Elsewhere Classified”
Since no vocabulary can guarantee
domain completeness all of the time, it
is tempting to include a catch-all term
which can be used to encode information that is not represented by other
existing terms. Such terms often appear
in vocabularies with the phrase “Not
Elsewhere Classified”, or “NEC” (this
is not to be confused with “Not Otherwise Specified”, or “NOS”, which simply means that no modifiers are included
with the base concept). The problem
with such terms is that they can never
have a formal definition other than one
of exclusion – that is, the definition can
only be based on knowledge of the rest
of concepts in the vocabulary. Not only
is this awkward, but as the vocabulary
evolves, the meaning of NEC concepts
will change in subtle ways. Such
“semantic drift” will lead to problems,
such as the proper interpretation of
historical data. Controlled vocabularies
should therefore reject the use of “not
elsewhere classified” terms.
2.8 Multiple Granularities
Each author who expresses a need
for a controlled vocabulary, does so
with a particular purpose in mind.
Associated with that purpose, usually
implicitly, is some preconception of a
level of granularity at which the
concepts must be expressed. For
example, the concepts associated with
a diabetic patient might be (with
increasingly finer granularity): “Diabetes Mellitus”, “Type II Diabetes Mellitus”, and “Insulin-Dependent Type II
Diabetes Mellitus” (note that the simpler term “Diabetes” is so coarse-grained
as to be vague). A general practitioner
might balk at being required to select a
diagnosis from the fine-grained end of
this spectrum of concepts, while an endocrinologist might demand nothing less.
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to a single taxonomy (strict
or if multiple classifications
can be allowed. Most
[63].
It is essential that medical vocabularies be capable of handling concepts as
fine-grained as “insulin molecule” and
as general as “insulin resistance”.
However, we must differentiate between the precision in medical knowledge and the precision in creating
controlled concepts to represent that
knowledge. While uncertainty in medical language is inevitable [64], we must
strive to represent that uncertainty with
precision.
2.9 Multiple Consistent Views
If a vocabulary is intended to serve
multiple functions, each requiring a
different level of granularity, there will
be a need for providing multiple views
of the vocabulary, suitable for different
purposes [30]. For example, if an application restricts coding of patient diagnoses to coarse-grained concepts (such
as “Diabetes Mellitus”), the more finegrained concepts (such as “InsulinDependent Type II Diabetes Mellitus”)
could be collapsed into the coarse
concept and appear in this view as
synonyms (see Figs. la and Ib).
Alternatively, an application may wish
to hide some intermediate classes in a
hierarchy if they are deemed irrelevant
(see Fig. lc). Similarly, although the
vocabulary may support mUltiple hierarchies, a particular application may
wish to limit the user to a single, strict
hierarchy (see Fig. Id).
We must be careful to confine the
ability to provide multiple consistent
views, such that inconsistent views do
not result. For example, if we create a
view in which concepts with multiple
parents appear in several places in a
single hierarchy, care must be taken
that each concept has an identical
appearance within the view (see Figs.
Ie and If) [31].
2.10 Beyond Medical Concepts:
Representing Context
Part of the difficulty with using a
standard controlled vocabulary is that
the vocabulary was created independent of the specific contexts in which it
is to be used. This helps prevent the
vocabulary from including too many
implicit assumptions about the meanings of concepts and allows it to stand
on its own. However, it can lead to
confusion when concepts are to be
recorded in some specific context, for
example, in an electronic patient
record. Many researchers have expressed a need for their controlled vocabulary to contain context representation
through formal, explicit information
about how concepts are used [21, 65,
66].
A decade ago, Huff and colleagues
argued that a vocabulary could never be
truly flexible, extensible and comprehensive without a grammar to define
how it should be used [67]. Campbell
and Musen stated that, in order to
provide systematic domain coverage,
they would need both a patient-description vocabulary and rules for manipula-
-”
tion of the vocabulary [68]. Rector et aL
add an additional requirement: not only
is there a grammar for manipulation,
but there is concept-specific informa_
tion about “what is sensible to say” that
further limits how concepts can be
arranged [43]. Such limitations are
needed in order for the vocabulary to
support operations such as predictive
data entry, natural language processing,
and aggregation of patient records;
Rector (and others in the Galen Project)
simply request that such information be
included as part of the vocabulary, in the
form of constraints and sanctions [69].
If drawing the line between concept
and context can become difficult [41],
drawing the line between the vocabulary and the application becomes even
more so. After all, the ultimate context
for controlled medical vocabulary
concepts is some external form such as
a patient record. Coping with such
contexts may be easier if such contexts
are modeled in the vocabulary [70]. A
schematic of how such contexts fit
together is shown in Fig. 2. The
figure differentiates between levels of
concept interaction: what’s needed to
define the concepts, what’s desired to
show expressivity of the vocabulary,
and how such expressiveness is channeled for recording purposes (e.g., in a
patient record).
Of course, patient records vary a
great deal from institution to institution
Lc
Fig. 1 Multiple views of a polyhierarchy. a) Internal arrangements of nine concepts in a
polyhierarchy, where E has two parents; b) Hierarchy has been collapsed so that specific
concepts serve as synonyms of their more general parents; c) Intermediate levels in the
hierarchy have been hidden; d) Conversion to a strict hierarchy; e) Strict hierarchy with
mUltiple contexts for term E; f) Multiple contexts for E are shown, but are inconsistent
(different children).
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In reviewing the various writings
on the subject, it becomes clear that
multiple granularities are needed for
mUltipurpose vocabularies. Vocabularies which attempt to operate at one
level of granularity will be deemed
inadequate for application where finer
grain is needed and will be deemed
cumbersome where coarse grain is
needed. Insistence on a single level of
detail within vocabularies may explain
why they often are not reusable [62]. It
also conflicts with a very basic attribute
of medical information: the more
macroscopic the level of discourse, the
coarser the granularity of the concepts
Assertional; Knowledge
(how concepts combine)
Contextual Knowledge
(how concepts are used)
name changes, code reuse, and changed
codes) can be avoided.[74]
I
Progress Note
Rndlng
Iprogress Note I
Fig.2 Definitional, assertional, and contextual information in the vocabulary showing how
concepts can be combined and where they will appear in a clinical record.
and, if we have difficulty standardizing
on a vocabulary, what hope is there for
standardizing on a record structure?
One possible solution is to view the
recording of patient information from
an “event” standpoint, where each
event is constitutes some action, including the recording of data, occurring
during an episode of care which, in turn
occurs as part of a patient encounter
[71, 72]. These add more levels to the
organization of concepts in contexts,
but can be easily modeled in the vocabulary, as in Fig. 2.
2.11 Evolve Gracefully
It is an inescapable fact that controlled vocabularies need to change with
time. Even if there were a perfect vocabulary that “got it right the first time”,
the vocabulary would have to change
Finding
IPneumonia I
ILeft Lower Lobe I
–
with the evolution of medical knowledge. All too often, however, vocabularies change in ways that are for the
convenience of the creators but wreak
havoc with the users [32]. For example,
if the name of a concept is changed in
such a way as to alter its meaning, what
happens to the ability to aggregate
patient data that are coded before and
after the change? An important desideratum is that those charged with maintaining the vocabulary must accommodate graceful evolution of their content
and structure. This can be accomplished
through clear, detailed descriptions of
what changes occur and why [73], so
that good reasons for change (such as
simple addition, refinement, precoordination, disambiguation, obsolescence,
discovered redundancy, and minor
name changes) can be understood and
bad reasons (such as redundancy, major
Left Lower Lobe Pneumonia
is-a: Pneumonia
has-site: Left Lower Lobe
participates-in: Finding
Fig.3 Interchangability of redundant data representations. The structure on the left
depicts the post coordination of a disease concept (Pneumonia) and a body location (Left
Lower Lobe) to create a finding in an electronic medical record. The structure on the right
shows a precoordinated term for the same finding (Left Lower Lobe Pneumonia). Because
this latter term includes formal, structured definitional information (depicted by the is-a,
has-site, and participates-in attributes), it is possible to recognize, in an automated way, that
data coded in these two different ways are equivalent.
In controlled vocabulary parlance,
redundancy is the condition in which
the same information can be stated
in two different ways. Synonymy is a
type of redundancy which is desirable: it
helps people recognize the terms they
associate with a particular concept and,
since the synonyms map to the same
concept (by definition), then the coding
of the information is not redundant.
On the other hand, the ability to code
information in multiple ways is generally to be avoided. However, such redundancy may be inevitable in a good,
expressive vocabulary.
Consider an application in which the
user records a coded problem list. For
any given concept the user might wish
to record, there is always the possibility
that the user desires a more specific
form than is available in the vocabulary.
A good application will allow the user
to add more detail to the coded
problem, either through the addition of
a coded modifier, through the use of
unconstrained text, or perhaps a combination of both. For example, if a patient
has a pneumonia in the lower lobe of
the left lung, but the vocabulary does
not have such a concept, the user might
select the coded concept “Pneumonia”
and add the modifier “Left Lower
Lobe”. Suppose that, a year later, the
vocabulary adds the concept “Left
Lower Lobe Pneumonia”. Now, there
are two ways to code the concept – the
old and the new. Even if we were to
somehow prevent the old method from
being used, we still have old data coded
that way.
As vocabularies evolve, gracefully or
not, they will begin to include this kind
of redundancy. Rather than pretend it
does not happen, we should embrace
the diversity it represents while, at the
same time, provide a mechanism by
which we can recognize redundancy
and perhaps render it transparent. In
the example above, if I were to ask for
all patients with “Left Lower Lobe
Pneumonia”, I could retrieve the ones
coded with the specific concept and
those coded with a combination of
concepts. Such recognition is possible if
Meth Inform Med
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2.12 Recognize Redundancy
Body Stte
3. Discussion
The intense focus previously directed
at such issues as medical knowledge
representation and patient care data
models is now being redirected to the
issue of developing and maintaining
shareable, mUltipurpose, high-quality
vocabularies. Shareability of vocabulary has become important as system
builders realize they must rely on vocabulary builders to help them meet
the needs of representing large sets of
clinical terms. The multipurpose nature
of vocabularies refers to their ability to
be used to record data for one purpose
(such as direct patient care) and then be
used for reasoning about the data (such
as automated decision support), usually
through a variety of views or abstractions of the specific codes used in data
capture. Even the ability to support
browsing of vocabularies remains problematic [75]. “High quality” has been
difficult to define, but generally means
that the vocabulary approaches completeness, is well organized, and has terms
whose meanings are clear. The above
list of desiderata for shareable,
mUltipurpose controlled vocabularies
reflect one person’s view of the necessary priorities; however, they are based
on personal experience with attempts
to adopt vocabularies [76-79] and
gleaned from the reported experiences
of others. The solutions necessary to
meet the above list of desiderata vary
from technical to political, from simple
adoption to basic shifts in philosophy,
and from those currently in use to areas
ripe for research.
Developers of controlled vocabularies are recognizing that their products
are in demand for multiple purposes
and, as such, they must address a variety
of needs that go beyond those included
for the vocabulary’s original purpose
[80]. The simple solution of “add more
terms until they’re happy” is not satisfying vocabulary users; they want content,
but they want more. They want information about the terms, so they know
what they mean and how to use them.
They also want this information to supplement the knowledge they create for
their own purposes. These purposes are
as diverse as natural language processing, predictive data entry, automated
decision support, indexing, clinical
research, and even the maintenance of
vocabularies themselves.
Simple, technical solutions are at
hand for some characteristics, and are
already being adopted. For example,
using nonsemantic concept identifiers
and allowing polyhierarchies are
straightforward. The systematic solution for some others, such as multiple
granularities and multiple consistent
views will require more thought, but
generally should be tractable. Allowing
graceful evolution and recognized
redundancy are still areas for research,
with some promising findings. For
example, systematic approaches for
vocabulary updates are being discussed
to support evolution [73], while conceptual graphs provide a mechanism for
transforming between different synonymous (i.e., redundant) arrangements of
associated concepts [54].
Some of the desiderata will require
fundamental philosophical shifts. For
example, decisions to have a truly
concept-oriented vocabulary and avoid
the dreaded “NEC” terms are simple
ones, but can not be taken lightly. Some
of these decisions, such as formal definitions and representing context, will also
require significant development effort
to make them a reality. Several developers describe commitment to these
goals, and one group has actually provided formal, computer-manipulable
definitions of their concepts [81, 82].
But the amount of work remains formidable. Finding ways to share the burden
of vocabulary design and construction
will be challenging [83], but some approaches seem promising [59]. Finding
ways to coordinate content development and maintenance among mUltiple
groups will require sophisticated approaches [60]. Despite their perceived
infancy [84], the currently available
standards should be the starting point
for new efforts [85].
Predictions may not be difficult to
make, given the current directions in
which standards development is proceeding. It is likely that vocabularies
will become concept-oriented, using
nonsemantic identifiers and containing
semantic information in the form of a
semantic network, including mUltiple
hierarchies. Development of a standard
notation for the semantic information
may take some time, but the conceptual
graph seems to be a popular candidate.
Maintenance of vocabularies will eventually settle down into some form which
is convenient for users and concept
permanence will become the norm.
Still unclear is whether the semantic,
definitional information provided by
developers will be minimal, complete,
or somewhere in between. Some of the
other desiderata, such as context representation, multiple consistent views,
and recognition of redundancy will
probably be late in coming. However,
the knowledge and structure provided
with the vocabulary will at least facilitate development of implementationspecific solutions which have not heretofore been possible.
4. Conclusions
This list of desiderata is not intended
to be complete; rather, it is a partial list
which can serve to initiate discussion
about additional characteristics needed
to make controlled vocabularies sharable and reusable. The reader should not
infer that vocabulary developers are not
addressing these issues. In fact, these
same developers were the sources for
many of the ideas listed here. As a
result, vocabularies are undergoing
their next molt. Current trends seem to
indicate that this one will be a true
metamorphosis, as lists change to multiple hierarchies, informal descriptive
information changes to formal definitional and assertional information, and
400
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Downloaded by: University of Michigan. Copyrighted material.
we have paid sufficient attention to two
other desiderata: formal definitions and
context representation. If we know,
from context representation, that the
disease concept “Pneumonia” can
appear in a medical record together
with an anatomical concept, such as
“Left Lower Lobe”, and the definition
of “Left Lower Lobe Pneumonia”
includes named relationships to the
concepts “Pneumonia” and “Left
Lower Lobe” (“is a” and “site of’ relationships, respectively), sufficient information exists to allow us to determine
that the representation of the new
concept in the vocabulary is equivalent
to the collection of concepts appearing
in the patient database (see Fig. 3).
Acknowledgments
The ideas in this paper have been based
on the medical informatics literature and synthesized from years of work and conversations
with many researchers. In particular, the
author’s collaborators on the UMLS project, the
Canon group, and the InterMed Collaboratory
can be credited with helping to precipitate these
thoughts. In addition, since this paper has been
prepared as an accompaniment to a presentation at the IMIA Working Group 6 conference,
it is hoped that additional desiderata will be put
forth at the meeting which will find their way
into the final version of this manuscript. This
work has been supported in part by the National
Library of Medicine.
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