Semantic Alignment between ICD-11 and SNOMED CT.

Due to fundamental differences in design and editorial policies, semantic interoperability between two de facto standard terminologies in the healthcare domain--the International Classification of Diseases (ICD) and SNOMED CT (SCT), requires combining two different approaches: (i) axiom-based, which states logically what is universally true, using an ontology language such as OWL; (ii) rule-based, expressed as queries on the axiom-based knowledge. We present the ICD-SCT harmonization process including: a) a new architecture for ICD-11, b) a protocol for the semantic alignment of ICD and SCT, and c) preliminary results of the alignment applied to more than half the domain currently covered by the draft ICD-11.

What's in a class? Lessons learnt from the ICD - SNOMED CT harmonisation.

The upcoming ICD-11 will be harmonized with SNOMED CT via a common ontological layer (CO). We provide evidence for our hypothesis that this cannot be appropriately done by simple ontology alignment, due to diverging ontological commitment between the two terminology systems. Whereas the common ontology describes clinical situations, ICD-11 linearization codes are best to be interpreted as diagnostic statements. For the binding between ICD codes and classes from the ontological layer, a query-based approach is favoured.

ICD-11 and SNOMED CT Common Ontology: circulatory system.

The improvement of semantic interoperability between data in electronic health records and aggregated data for health statistics requires efforts to carefully align the two domain terminologies ICD and SNOMED CT. Both represent a new generation of ontology-based terminologies and classifications. The proposed alignment of these two systems and, in consequence, the validity of their cross-utilisation, requires a specific resource, named Common Ontology. We present the ICD-11 SNOMED CT Common Ontology building process including: a) the principles proposed for aligning the two systems with the help of a common model of meaning, b) the design of this common ontology, and c) preliminary results of the application to the diseases of the circulatory system.

Sharing ontology between ICD 11 and SNOMED CT will enable seamless re-use and semantic interoperability.

In order to support semantic interoperability in eHealth systems, domain terminologies need to be carefully designed. SNOMED CT and the upcoming ICD-11 represent a new generation of ontology-based terminologies and classifications. The proposed alignment of these two systems and, in consequence, the validity of their cross-utilisation requires a thorough analysis of the intended meaning of their representational units. We present the ICD11 SNOMED CT harmonization process including: a) the clarification of the interpretation of codes in both systems as representing situations rather than conditions, b) the principles proposed for aligning the two systems with the help of a common ontology, c) the high level design of this common ontology, and d) further ontology-driven issues that have arisen in the course of this work.

Auditing complex concepts of SNOMED using a refined hierarchical abstraction network.

Auditors of a large terminology, such as SNOMED CT, face a daunting challenge. To aid them in their efforts, it is essential to devise techniques that can automatically identify concepts warranting special attention. "Complex" concepts, which by their very nature are more difficult to model, fall neatly into this category. A special kind of grouping, called a partial-area, is utilized in the characterization of complex concepts. In particular, the complex concepts that are the focus of this work are those appearing in intersections of multiple partial-areas and are thus referred to as overlapping concepts. In a companion paper, an automatic methodology for identifying and partitioning the entire collection of overlapping concepts into disjoint, singly-rooted groups, that are more manageable to work with and comprehend, has been presented. The partitioning methodology formed the foundation for the development of an abstraction network for the overlapping concepts called a disjoint partial-area taxonomy. This new disjoint partial-area taxonomy offers a collection of semantically uniform partial-areas and is exploited herein as the basis for a novel auditing methodology. The review of the overlapping concepts is done in a top-down order within semantically uniform groups. These groups are themselves reviewed in a top-down order, which proceeds from the less complex to the more complex overlapping concepts. The results of applying the methodology to SNOMED's Specimen hierarchy are presented. Hypotheses regarding error ratios for overlapping concepts and between different kinds of overlapping concepts are formulated. Two phases of auditing the Specimen hierarchy for two releases of SNOMED are reported on. With the use of the double bootstrap and Fisher's exact test (two-tailed), the auditing of concepts and especially roots of overlapping partial-areas is shown to yield a statistically significant higher proportion of errors.

Competing interpretations of disorder codes in SNOMED CT and ICD.

Under ontological scrutiny we have identified two competing interpretations of disorder concepts in SNOMED. Should codes be interpreted as representing pathological conditions themselves or the situations in which a patient has those conditions? This difference has significant implications for the proposed harmonization between SNOMED CT and the new ICD-11 disease classification and indeed for any systematic review of the correctness of the SNOMED CT hierarchies. Conditions themselves are distinct, whereas in any given situation a patient may have more than one condition. In such cases, SNOMED codes that represent combinations of conditions - which can be regarded as "additive" - are evidence for interpreting the codes as referring to situations. There are clearly some such codes. We conducted a survey to determine the extent of this phenomenon. Three criteria were used - analysis of the SNOMED CT fully specified name, the corresponding logical definition, and the children of the concept under scrutiny. All three showed that at least 11% of concepts met our criteria for representing situations rather than conditions, with a satisfactory inter-rater reliability for the first two. We, therefore, conclude that if a uniform interpretation of SNOMED disorder codes is desired, they should be interpreted as representing situations.

Scalable representations of diseases in biomedical ontologies.

BACKGROUND: The realm of pathological entities can be subdivided into pathological dispositions, pathological processes, and pathological structures. The latter are the bearer of dispositions, which can then be realized by their manifestations - pathologic processes. Despite its ontological soundness, implementing this model via purpose-oriented domain ontologies will likely require considerable effort, both in ontology construction and maintenance, which constitutes a considerable problem for SNOMED CT, presently the largest biomedical ontology. RESULTS: We describe an ontology design pattern which allows ontologists to make assertions that blur the distinctions between dispositions, processes, and structures until necessary. Based on the domain upper-level ontology BioTop, it permits ascriptions of location and participation in the definition of pathological phenomena even without an ontological commitment to a distinction between these three categories. An analysis of SNOMED CT revealed that numerous classes in the findings/disease hierarchy are ambiguous with respect to process vs. disposition. Here our proposed approach can easily be applied to create unambiguous classes. No ambiguities could be defined regarding the distinction of structure and non-structure classes, but here we have found problematic duplications. CONCLUSIONS: We defend a judicious use of disjunctive, and therefore ambiguous, classes in biomedical ontologies during the process of ontology construction and in the practice of ontology application. The use of these classes is permitted to span across several top-level categories, provided it contributes to ontology simplification and supports the intended reasoning scenarios.

Auditing SNOMED relationships using a converse abstraction network.

In SNOMED CT, a given kind of attribute relationship is defined between two hierarchies, a source and a target. Certain hierarchies (or subhierarchies) serve only as targets, with no outgoing relationships of their own. However, converse relationships-those pointing in a direction opposite to the defined relationships-while not explicitly represented in SNOMED's inferred view, can be utilized in forming an alternative view of a source. In particular, they can help shed light on a source hierarchy's overall relationship structure. Toward this end, an abstraction network, called the converse abstraction network (CAN), derived automatically from a given SNOMED hierarchy is presented. An auditing methodology based on the CAN is formulated. The methodology is applied to SNOMED's Device subhierarchy and the related device relationships of the Procedure hierarchy. The results indicate that the CAN is useful in finding opportunities for refining and improving SNOMED.

Auditing complex concepts in overlapping subsets of SNOMED.

Limited resources and the sheer volume of concepts make auditing a large terminology, such as SNOMED CT, a daunting task. It is essential to devise techniques that can aid an auditor by automatically identifying concepts that deserve attention. A methodology for this purpose based on a previously introduced abstraction network (called the p-area taxonomy) for a SNOMED CT hierarchy is presented. The methodology algorithmically gathers concepts appearing in certain overlapping subsets, defined exclusively with respect to the p-area taxonomy, for review. The results of applying the methodology to SNOMED's Specimen hierarchy are presented. These results are compared against a control sample composed of concepts residing in subsets without the overlaps. With the use of the double bootstrap, the concept group produced by our methodology is shown to yield a statistically significant higher proportion of error discoveries.

Complexity measures to track the evolution of a SNOMED hierarchy.

SNOMED CT is an extensive terminology with an attendant amount of complexity. Two measures are proposed for quantifying that complexity. Both are based on abstraction networks, called the area taxonomy and the partial-area taxonomy, that provide, for example, distributions of the relationships within a SNOMED hierarchy. The complexity measures are employed specifically to track the complexity of versions of the Specimen hierarchy of SNOMED before and after it is put through an auditing process. The pre-audit and post-audit versions are compared. The results show that the auditing process indeed leads to a simplification of the terminology's structure.

Representation of disorders of the newborn infant by SNOMED CT.

SNOMED CT is the most sophisticated reference terminology currently available for the representation of healthcare. An unforeseen consequence of the opportunistic evolutionary process for SNOMED CT may be that some terms for disorders of specialised clinical domains are not represented within the terminology. The SNOMED CT July 2006 release was systematically examined using the CliniClue terminology browser to determine whether 434 terms for disorders of the newborn infant are represented within the terminology. There was complete representation for 90.8% of the terms for disorders of the newborn infant, partial representation for 6.4% of the terms, and no representation for 2.8% of the terms. Complete representation is achieved with a single, pre-coordinated SNOMED expression for 96.2% of the terms for disorders of the newborn infant that have complete representation within SNOMED CT. Nearly ninety percent of the SNOMED CT concepts that completely represent these terms have the current Concept Status but less than 40% of these concepts are fully defined SNOMED concepts. Nearly 50% of these SNOMED CT concepts have one or more synonyms. SNOMED CT provides structured representation for the majority of this set of terms that are used for disorders of the newborn infant.

Structural methodologies for auditing SNOMED.

SNOMED is one of the leading health care terminologies being used worldwide. As such, quality assurance is an important part of its maintenance cycle. Methodologies for auditing SNOMED based on structural aspects of its organization are presented. In particular, automated techniques for partitioning SNOMED into smaller groups of concepts based primarily on relationships patterns are defined. Two abstraction networks, the area taxonomy and p-area taxonomy, are derived from the partitions. The high-level views afforded by these abstraction networks form the basis for systematic auditing. The networks tend to highlight errors that manifest themselves as irregularities at the abstract level. They also support group-based auditing, where sets of purportedly similar concepts are focused on for review. The auditing methodologies are demonstrated on one of SNOMED's top-level hierarchies. Errors discovered during the auditing process are reported.

SNOMED CT as the clinical terminology for the representation of the clinical care of the newborn infant.

Clinically relevant concepts of specialized clinical domains may not yet have been represented in SNOMED CT(R). The July 2006 release was examined with CliniClue browser to determine whether 881 terms for the clinical care of the newborn infant are represented in SNOMED CT. There was complete representation for 86.4% of terms drawn from the categories of diagnosis, intervention, drug or observation. There was partial representation for 10.2% and no representation for 3.4% of the terms.

Would SNOMED CT benefit from realism-based ontology evolution?

If SNOMED CT is to serve as a biomedical reference terminology, then steps must be taken to ensure comparability of information formulated using successive versions. New releases are therefore shipped with a history mechanism. We assessed the adequacy of this mechanism for its treatment of the distinction between changes occurring on the side of entities in reality and changes in our understanding thereof. We found that these two types are only partially distinguished and that a more detailed study is required to propose clear recommendations for enhancement along at least the following lines: (1) explicit representation of the provenance of a class; (2) separation of the time-period during which a component is stated valid in SNOMED CT from the period it is (or has been) valid in reality, and (3) redesign of the historical relationships table to give users better assistance for recovery in case of introduced mistakes.

Analysis of error concentrations in SNOMED.

Two high-level abstraction networks for the knowledge content of a terminology, known respectively as the "area taxonomy" and "p-area taxonomy," have previously been defined. Both are derived automatically from partitions of the terminology's concepts. An important application of these networks is in auditing, where a number of systematic regimens have been formulated utilizing them. In particular, the taxonomies tend to highlight certain kinds of concept groups where errors are more likely to be found. Using results garnered from applications of our auditing regimens to SNOMED CT, an investigation into the concentration of errors among such groups is carried out. Three hypotheses pertaining to the error distributions are put forth. The results support the fact that certain groups presented by the taxonomies show higher error percentages as compared to other groups. The bootstrap is used to assess their statistical significance. This knowledge will help direct auditing efforts to increase their impact.

SNOMED representation of explanatory knowledge in veterinary clinical pathology.

BACKGROUND: The Systematized Nomenclature of Medicine (SNOMED) is an established standard nomenclature for the expression of human and veterinary medical concepts. Nomenclature standards ease sharing of medical information, create common points of understanding, and improve data aggregation and analysis. OBJECTIVES: The objective of this study was to determine whether SNOMED adequately represented concepts relevant to veterinary clinical pathology. METHODS: Concepts were isolated from 3 different types of clinical pathology documents: 1) a textbook (Textbook), 2) the Results sections of industry pathology reports (Findings), and Discussion sections from industry pathology reports (Discussion). Concepts were matched (mapped) by 2 reviewers to semantically-equivalent SNOMED concepts. A quality score of 3 (good match), 2 (problem match), or 1 (no match) was recorded along with the SNOMED hierarchical location of each mapped concept. Results were analyzed using Cohen's Kappa statistic to assess reviewer agreement and chi-square tests to evaluate association between document type and quality score. RESULTS: The percentage of good matches was 48.3% for the Textbook, 45.4% for Findings, and 47.5% for Discussion documents, with no significant difference among documents. Of remaining concepts, 40% were partially expressed by SNOMED and 14% did not match. Mean reviewer agreement on quality score assignments was 76.8%. CONCLUSIONS: Although SNOMED representation of veterinary clinical pathology content was limited, missing and problem concepts were confined to a relatively small area of terminology. This limitation should be addressed in revisions of SNOMED to optimize SNOMED for veterinary clinical pathology applications.

Rates of change in a large clinical terminology: three years experience with SNOMED Clinical Terms.

The Systematized Nomenclature of Medicine, Clinical Terms (SNOMED CT) was produced by merging SNOMED Reference Terminology (RT) with Clinical Terms version 3 (CTV3). It was first released in January 2002. This paper summarizes the overall size of the terminology and its rates of change over a period of three calendar years, comprising six subsequent releases each occurring at six month intervals. Rates of change in raw table size are reported for the concepts, descriptions, and relationships tables. Other measures of change are the number of identifiers made inactive and the reasons for this, as well as the number and rate of changes in the subsumption hierarchies and defining relationships. Awareness of the rate of change in the terminology can help terminology developers focus attention on needed infrastructure support and capacity for handling updates and refinements, and can help application developers by highlighting the need for managing terminology change in applications.

The use of SNOMED CT simplifies querying of a clinical data warehouse.

The usefulness of digital clinical information is limited by difficulty in accessing that information. Information in electronic medical records (EMR) must be entered and stored at the appropriate level of granularity for individual patient care. However, benefits such as outcomes research and decision support require aggregation to clinical data -- "heart disease" as opposed to "S/P MI 1997" for example. The hierarchical relationships in an external reference terminology, such as SNOMED, can facilitate aggregation. This study examines whether by leveraging the knowledge built into SNOMED's hierarchical structure, one can simplify the query process without degrading the query results.