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AMIA Annu Symp Proc. 2005; 2005: 704–708. | PMCID: PMC1560617 |
Copyright This is an Open Access article: verbatim copying and redistribution of
this article are permitted in all media for any purpose An Ontology-Based Methodology for the Migration of Biomedical Terminologies
to Electronic Health Records Barry Smith, PhDab and Werner Ceusters, MDb a Department of Philosophy, University at Buffalo, Buffalo, NY 14260, USA b Institute for Formal Ontology and Medical Information Science and European Centre for Ontological Research, Saarland University, 66041 Saarbrücken, Germany Biomedical terminologies are focused on what is general, Electronic Health
Records (EHRs) on what is particular, and it is commonly assumed that
the step from the one to the other is unproblematic. We argue that
this is not so, and that, if the EHR of the future is to fulfill its
promise, then the foundations of both EHR architectures and biomedical
terminologies need to be reconceived. We accordingly describe a new framework
for the treatment of both generals and particulars in biomedical
information systems that is designed: 1) to provide new opportunities
for the sharing and management of data within and between healthcare
institutions, 2) to facilitate interoperability among different terminology
and record systems, and thereby 3) to allow new kinds of reasoning
with biomedical data. Keywords: terminologies, Relation Ontology, referent tracking, diagnostic decision support, SNOMED The General and the Particular Much effort has been invested in recent years in the development of structured
vocabularies of medical and biological terms. The resultant terminologies, such
as SNOMED-CT 1, GALEN 2, ICD-10 3, and GO 4, consist overwhelmingly of general terms A, B,... (‘cell’, ‘tumor’, ‘postoophorectomy
with pathological fracture’) linked via relational assertions
for example of the forms ‘ A is_a B ’ or ‘ A part_of B’. The Electronic Health Record (EHR), in contrast, is a record of particular
entities belonging to a wide variety of different general categories. It
is a record of particular histories, of the events and processes
described therein, of particular symptoms, disorders and associated
pathological and non-pathological anatomical structures, of particular
tests and particular measurements taken, and of much more. For all this variety, however, most existing EHR architectures allow direct
reference to just two kinds of particulars in reality: For particulars in other categories existing EHRs provide, with few exceptions, 5 merely general codes. This limited repertoire of labels for particulars
means that current EHRs have no adequate means to keep track of one
and the same particular entity (for example a tumor, a breast implant, a
shadow revealed on a succession of radiological images) over extended
periods of time. When interpreting EHR data, it is thus difficult to
distinguish clearly between multiple references to the same particular
and multiple particulars of the same general kind. 5When the need arises to refer in different contexts to some single particular
as it exists at different points in time, each such reference must
at present be created anew, via some combination of general terms
with designators for persons and times, for example in expressions like: the fever of Patient McX first noted by Patient at t1 and observed by Physician O’Z at t2. Unfortunately, the need to use such composites, and their syntactically ad hoc nature, creates logical obstacles to cross-identification of the corresponding
entities as they occur in different contexts, and thus also to
reasoning about these entities in software systems. This is so especially
when we are concerned to keep track of how such entities develop
over time, the capacity for which strikingly underdeveloped in existing
biomedical information systems. To resolve these problems, we have proposed a new type of EHR regime, in
which, in the course of data entry, explicit alphanumerical IDs would
be automatically assigned to each individual real-world entity at that
point in time when it first becomes relevant to the treatment and care
of a given patient. 5 Once assigned, such IDs would acquire the status of what we shall call
IUIs, for Instance Unique Identifiers. IUIs would be assigned (in principle) not just to each particular tumor
but also to each gland or duct in which a tumor is located, to each
biopsy taken, to each associated radiological image, and indeed to instances
in all the diagnostically salient categories recorded in the EHR. Our proposal is that the physician or other specialist entering data about
a given particular in the EHR should be provided with software tools
that will constantly check the appropriate instance-tracking database
in real time in order to establish as far as possible automatically (with
the aid of the SNOMED or other codes already entered) whether the
particular in question is new to the system or whether it has already
been allocated its own IUI. In the former case, a new IUI will be immediately
created and subjected to coding in the usual way. In the latter
case, the physician can simply add new information to the vector
associated with the already existing IUI, using further codes as appropriate. The
software will then make it easier to decide which codes to
use at each successive stage, since the user will be presented immediately
with the codes already entered in previous descriptions of the given
particular. We have outlined elsewhere both the benefits to diagnosis and treatment
within a single healthcare institution which may derive from such an
EHR architecture and also some of the practical problems involved in its
realization. 5 Here we focus on those aspects of the referent tracking system which concern
its interface with terminologies and coding systems. Each IUI stored in the system (and thus each corresponding particular in
reality) would be associated with a vector comprehending both relevant
coding assignments and also cross-references to the measured values
of medically salient attributes such as temperature or blood pressure, as
well as gene expression and other bio-assay data. Such information
would be annotated in its turn with time of entry, source, estimated
evidence, access-rights, and so forth. Importantly, the cross-referencing would be achieved by using the IUIs
of those other particulars with which the entity under scrutiny is associated (for
example the patient herself, her specific measurable attributes, family
members, earlier events in her life history). Taken together, the
vectors would thereby form a complex graph representing the
relations between such particulars as they exist in reality. This graph
would in addition contain large amounts of redundancy, of a sort which
would enable cross-checking and thus error-detection in relation to
the data entered in the associated EHRs. Because instances of disorders would receive IUIs of their own, independently
of any identifying reference to corresponding patients, the proposal
would allow automatic compilation of pseudonymized data pertaining
to specific kinds of disorders or to multiple disorders of specific
kinds of patients. The ever-growing pool of vectors could further be
managed in such a way that different kinds of associations between IUIs
could be subject to different levels of encryption, thereby allowing
new types of research collaboration based on the automatic exchange of
different kinds of instance data. In the ideal case, uniqueness of IUIs would be guaranteed by means of the
same sorts of mechanisms as are currently used for maintaining the
uniqueness of patient IDs or webpage addresses or of IDs for credit-card
transactions. The vectors pertaining to particular identifiers might
be stored locally or in some nationally or (ideally) internationally
administered pool. In each case, our approach would facilitate the gathering
of more adequate statistics on patient care and outcomes than
are currently available. 6It will be important that referent tracking software have the facility
to unravel ID assignments which have been discovered to be erroneous. When
multiple IDs are assigned to what proves to be a single particular, it
will normally be possible simply to merge the vectors associated
with each separate IUI. In the (rarer) case where a single ID has been
assigned to what turns out to be multiple particulars, software tools
would need to be provided which would help the physician or coding specialist
to decompose the corresponding vector in such a way that relevant
segments come to be assigned to their associated particulars. In
the same way, the system would need to be able to accommodate the sorts
of corrections to the codes contained in specific vectors which become
necessary when terminologies themselves change because of scientific
advance. All assignments and associated annotations would be preserved in their
original forms for the medico-legal purposes of creating an audit trail. It
is however a crucial feature of our proposal that it will allow
active manipulation – or what we might think of as tuning – of the health record in real time on the part of the physician. 7 For the same tools which allow the correction of IUI mis-assignments would
also provide the clinician with the opportunity to experiment with
alternative IUI-assignments in support of reasoning about patients and
their disorders. For example, successive clusters of symptoms of a
given patient may be manifestations either of a single or of multiple
disorders. Software could allow the physician to examine the consequences
of rebundling vector annotations through association either with one
or with a plurality of IUIs, and to allow statistical methods of pattern
matching to compare the results of such rebundling as an aid to
choice of diagnosis. Moreover, by keeping track of the ways in which IUI-assignments are corrected
in response to specific types of new information, the system could
in principle learn to associate recurring patterns of correction – for
example of the sort which arise in the early phases of diagnosis
of degenerative diseases such as multiple sclerosis – with
corresponding kinds of disorders. Even with an adequate system for tracking referents of the sort described, however, there
is an obstacle to the effective migration of biomedical
terminologies to the resulting EHR environment, which turns on the
currently predominant treatment of the terms and relations in such terminologies. In the development of almost all extant terminologies, little consideration
was given to the need for a clear link, or bridge, between terms
in terminologies and instances in reality. Rather, the relations between
terms were (and often still are) conceived primarily in the ways in
which linguists or dictionary-makers might conceive them, which is to
say: as reflecting merely certain relations between meanings of words. Consider for example the definition of the is_a relation provided by the Semantic Network (SN) of the Unified Medical
Language System (UMLS) 8: If one item ‘isa’ another item then the first item is more
specific in meaning than the second item. The nodes of the SN – called, variously, ‘concepts’ or ‘items’ or ‘entities’ or ‘Semantic
Types’ – would seem in light of this definition
to be precisely meanings. However, when we examine the bulk of the SN’s relations between
such nodes, we see that they are defined in such a way as to require
instead a conception of these nodes as entities in reality. Part_of, for example, SN defines as: composes, with one or more other physical units.... The relation contains is defined as: holds or is the receptacle for fluids or other substances. Co-occurs_with as: occurs at the same time as, together with, or jointly. Meanings, clearly, cannot serve as the relata of such relations. Our
suspicion is that there is no answer to the question what the nodes of
the SN are which would make all its relational assertions come out true
simultaneously. 7,9The SN is of course not designed to be used as a terminology in healthcare
records. But the problem of polysemy of the term ‘concept’ applies
to almost all the terminologies included in the UMLS
Metathesaurus, where relations like synonymous_with or narrower_in_meaning_than or associated_with or conceptually_related_to are used side –by side with relations like treats or causes or is_finding_site_of. Because the relations which structure these terminologies were introduced
in informal and inconsistent ways, the logical interconnections between
the corresponding assertions are left unclear. This goes far, we
believe, to explain familiar errors in coding and documentation. 10 The same shortfall has also served to block those kinds of logical reasoning
within and between terminologies and EHRs which would be possible
given clear and consistent definitions. 11A New Regime of Definitions How, then, are we to reconstruct biomedical terminologies in such a way
that they will both (1) allow the provision of clear definitions of relations
such as is_a and part_of, and at the same time (2) facilitate application to corresponding instances
and times in reality and thus to the EHR? The answer proposed in 12 consists in a treatment of the relations used in biomedical ontologies
as linking, not concepts or meanings, but rather entities in reality at the level of both universals and their instances. (The term ‘universal’ here
refers to those invariants in reality, sometimes
called ‘classes’ or ‘types’, in virtue
of which we are able to describe multiple particulars by using one
and the same general term. It is such invariants which make possible inter alia the application of standardized therapies to multiple instances of the
same disorder (universal) in different patients.) The method thus explicitly
accommodates, in addition to the familiar relations involving
universals or types (marked in italics in what follows), also relations involving their underlying instances (marked
in bold). The Relation Ontology which results from the application of this method
has been incorporated into the ontology library maintained by the Open
Biomedical Ontologies Consortium, 13 and it is being used by curators of the Foundational Model of Anatomy, 14 of GO 4, and of the ChEBI chemical entities vocabulary 13 as part of their work on ontology integration designed to support more
powerful cross-domain reasoning and data annotation. The Relation Ontology
is designed also to support more reliable curation of ontologies, since
its definitions have been formulated in such a way as to provide
an optimal combination of understandability to curators of ontologies
with the sort of formal rigor needed to support logic-based reasoning. We here provide examples of definitions from the Relation Ontology pertaining
to continuant entities such as lungs, diseases, tumors, fractures, entities that endure through
time while undergoing changes of various sorts (as contrasted with occurrent entities, which unfold themselves through time in successive phases. 12,15). We use variables c, d,..., C, D, ... to range over continuant particulars and continuant universals, respectively. Because
the former can instantiate different universals and
include different instance-level parts at different times (consider, for
example, a carcinoma, or a fetus, in its successive stages of development), our
definitions require also variables t1, t2,... to range over instants of time (assumed to be linearly ordered by
the relation earlier_than). They require also certain primitive (undefined) relations involving
continuant entities on the instance level, for example: c instance_of C at t (particular c instantiates universal C at time t); c part_of d at t (particular c is an instance-level part of particular d at time t); c located_in d at t (the spatial region occupied by c is an instance-level part of the spatial region occupied by d at time t). 16 Corresponding formal definitions of relations between continuant universals
then read as follows: C is_a D =def. for all c, t, if c instance_of C at t then c instance_of D at t; C part_of D =def. for all c, t, if c instance of C at t then there is some d such that: d instance_of D at t and c part_of d at t; C located_in D =def. for all c, t, if c instance_of C at t then there is some d such that: d instance_of D at t and c located_in d at t; C transformation_of D =def. for all c, t, if c instance_of C at t, then there is some t1 such that: c instance_of D at t1 and t1 earlier than t. The relation transformation_of serves to represent the phenomena of growth, development and pathological
change. Where this relation obtains between two universals C and D (for example adult and child), then every instance of the former was at some prior stage an instance
of the latter (so that we have some single continuant particular which
instantiates distinct universals at different times in virtue of phenotypic
changes). Note how the definitions listed ensure that the corresponding relational
assertions on the level of universals have embedded within them an automatic
reference to the corresponding instances and times. This is achieved
characteristically via an all–some structure, 17,18 as for example in the definition of part_of, above, where we have: universal C part_of universal D when all instances of C have some instance of D as part. Most existing biomedical terminologies employ an informal treatment of
relations that fails to distinguish clearly between relations on the two
levels of instances and universals. At the same time they commonly
fail also to distinguish all-some ( human has_part lung) from some-some relations ( human has_part testis), in ways which thwart the drawing of reliable inferences. 17,18 With a properly formal treatment of relations, in contrast, it becomes
possible to reason across data deriving from different sources, secure
in the knowledge that our inferences will track the underlying reality. Thus, given all-some relations R1 and R2, if we know from ontology (1) that A R1 B and from ontology (2) that B R2 C; and if we have instance-data concerning some A from some referent-tracking data-source, then we know (1) that this A stands in the instance-level counterpart of R1 to some B, and also (2) that whichever B this is, it stands in its turn in the instance-level counterpart of R2 to some instance of C. Our software can then be made to search for IUIs for the corresponding
instances of B and C within all vectors including cross-references to the IUI of our initial
instance of A.In the EHR domain, reasoning of this sort might be used to integrate data
in an EHR with information contained in a system such as the TNM (for: Tumour, Node, Metastasis) classification of cancer stages. Here ‘T1’ designates a stage where the tumor has invaded the
submucosa, ‘N1’ a stage with one to four lymph nodes, ‘M1’ a
stage where a metastasis is present in a non-contiguous
part of the body, and so forth. In a vector describing the patient, there
might figure IUIs that refer to one or several metastases, to
processes of growth, and so on, and whose vectors will incorporate
descriptors for the corresponding TNM configurations. Using the machinery
of the Relation Ontology, we can then extract from TNM a system
of assertions to the effect, for example, that the universal T2N1M1 carcinoma in colon stands in a transformation_of relation to either a T1N1M1 or a T2N0M1 structure. 19 Such assertions can then be used by our software to infer testable hypotheses
about the type of carcinoma development under scrutiny. A New Regime for Clinical Coding Suppose, against this background, that we wish to use the resources provided
by a terminology such as SNOMED in order to draw inferences concerning
a specific patient suffering from recurring breast cancer. History-taking
involves finding ways of referring to instances such as: this
present incidence of breast cancer, earlier incidences, present tumors, earlier (distinct) tumors, processes of mastectomy, and so on. How, then, can
we use standard SNOMED-coded EHR data in order to keep track
of these multiple particulars at successive points in time if (as
in present EHR architectures) we have unique IDs only for patients and
for time-points? One answer 20 holds that we can provide some of what we need to achieve this end through
inferences from statements at the general level. If, for example, a
given SNOMED term is used at t1 to describe ‘something a physician observed’ and at t2 the same general term is used again by the same physician, and if t2 is ‘close in time’ to t1, then it can be inferred that the physician referred to the same ‘something’ on
two successive occasions. We find this idea practically implausible. Indeed, while we fully support
the idea of inferencing from SNOMED-coded data, we believe that such
inferencing will become feasible only in the presence of large amounts
of instance data accumulated along the lines described above. This
is because, for the reasons explained, many of SNOMED’s existing
relational assertions cannot be used to infer further information about
particular instances, and SNOMED currently offers no way to tell
which of its assertions do support inferences of this sort. Thus its relational
organization is still best conceived as a convenient mechanism
for browsing through the terminology in order to find better descriptors, not
as a representation of how the corresponding instances are
related together in reality. When the paradigm here advanced has been
in use for some time, however, then the accumulated instance-data could
be exploited post hoc to correct SNOMED’s treatment of relations in such a way that
it would, by degrees, be in a position to support such inferences in a
reliable fashion. In this way our methodology can also lead to improvements in terminologies. It
can also further the goal of interoperability between terminologies
and other systems for recording biomedical data. For our paradigm
would allow the simultaneous use of a variety of different coding systems
within a single record. This use of multiple codes would then yield
in automatic fashion an ever-growing network of associations between
the terms in the separate coding systems – reflecting their
use in annotating common particulars – in a process which would
eventually supplant current efforts to create mappings between such
systems. There is however one remaining obstacle to the use of formal definitions
to support reasoning with instance-level data, which turns on the fact
that such reasoning is expensive in computational resources. As is
recognized in Description Logic circles, however, the right approach is
to concentrate first on those problems that allow tractable reasoning
and to focus on the hard cases later. We are currently testing a prototype
reasoner which can help us evaluate the potential of the method
along these lines. Given assertions of specific relations between instances
of given types, our prototype calculates, on the basis of definitions
in the OBO Relation Ontology, an exhaustive list of all further
relations which can hold between instances of the types in question. It
thereby becomes possible to transform reasoning with instance data into search across the corresponding relation space, which entails far fewer demands
on computational resources. A framework is hereby being built which
can, we believe, help in bringing together in dynamic fashion the distinct
ways of treating data that have evolved in the worlds of clinical
records and of medical terminology. Work on this paper was carried out under the auspices of the Wolfgang Paul
Program of the Humboldt Foundation and the Volkswagen Foundation Project “Forms
of Life”. 2. Rogers J, Rector A. The GALEN ontology. MIE. 1996:174–178. 5. Ceusters W, Smith B. Tracking referents in electronic health records. MIE 2005. In press. 7. Smith B. New desiderata for biomedical terminologies. J Biomed Inform (submitted). 9. Smith B. Beyond concepts: Ontology as reality representation. Formal Ontology and Information Systems (FOIS 2004). :73–84. 10. Farhan J, Al-Jummaa S, Al-Rajhi A, Al-Rayes H, Al-Nasser A. Documentation and coding of medical records in a tertiary care center: a
pilot study. Ann Saudi Med. 2005 Jan–Feb;25(1):46–9. [PubMed] 11. Rector AL. Clinical terminology: why is it so hard? Methods Inf Med. 1999;38(4-5):239–52. [PubMed] 12. Smith B, Ceusters W, Klagges B, Köhler J, Kumar A, Lomax J, Mungall C, Neuhaus F, Rector A, Rosse C. Relations in biomedical ontologies. Genome Biology. 2005;6:R46. [PubMed] 14. Rosse C, Mejino JLV. A reference ontology for bioinformatics: The Foundational Model of Anatomy. J Biomed Inform. 2004;36:478–500. [PubMed] 15. Rosse C, Kumar A, Mejino JLV, Cook DL, Detwiler DL, Smith B. A strategy
for improving and integrating biomedical ontologies. AMIA 2004 (in this volume). 16. Donnelly M. Layered mereotopology. Proc IJCAI. 2003:1269–1274. 17. Smith B, Rosse C. The role of foundational relations in the alignment of biomedical ontologies. Proc Medinfo. 2004:444–448. 18. Donnelly M, Bittner T, Rosse C. A formal theory for spatial reasoning in
biomedical ontologies. Artificial Intelligence in Medicine, in press. 19. Kumar A, Yip YL, Smith B, Grenon P. Bridging the gap between medical and
bioinformatics using ontological principles: A colon carcinoma case
study, Computers in Biology and Medicine, in press.
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