Mistakes in medical ontologies: Where do they come from and how can they be detected?

We present the details of a methodology for quality assurance in large medical terminologies and describe three algorithms that can help terminology developers and users to identify potential mistakes. The methodology is based in part on linguistic criteria and in part on logical and ontological principles governing sound classifications. We conclude by outlining the results of applying the methodology in the form of a taxonomy different types of errors and potential errors detected in SNOMED-CT

SNOMED CT standard ontology based on the ontology for general medical science

Background: Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is acomprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic healthdata. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but theseefforts have been hampered by the size and complexity of SCT. Method: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the termsin SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks ofdefinitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-levelSCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS). Results: The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. Theapproach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundryontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-levelontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555annotations. It is publicly available through the bioportal athttp://bioportal.bioontology.org/ontologies/SCTO/. Conclusion: The resulting ontology can enhance the semantics of clinical decision support systems and semanticinteroperability among distributed electronic health records. In addition, the populated ontology can be used forthe automation of mobile health applications

The integration of WHO classifications and reference terminologies to improve information exchange and quality of electronic health records: the SNOMED\u2013CT ICF harmonization within the ICD-11 revision process

Introduction The Family of International Classifications (WHO-FIC) is a suite of integrated classification products of the World Health Organization (WHO) that can be used to provide information on different aspects of health and the health-care system. These tools and their national modifications allow, together with the related classifications of health interventions, full representation of the volumes of health services provided in the various countries that adopt case mix systems. The use of standardized terminologies in classifications, for the definition of the descriptive characteristics of the disease, is a necessary step to allow full integration between different information systems, making available information about the diagnosed diseases, the performed health procedures and the level of functioning of the person, for very different uses such as, for example, public health, safety of care and quality control. Materials and methods Within the WHO and International Health Terminology Standards Development Organization (IHTSDO) collaboration agreement, a work of independent review was carried out on all the Activities and Participation categories (A&P) of the WHO International Classification of Functioning, Disability and Health (ICF), in order to identify equivalence and gaps to the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) concepts in terms of lexical, semantic (content) and hierarchical matching, to harmonize WHO classifications and SNOMED CT. Results and conclusions The performed mapping suggests that the ICF A&P categories are semantically and hierarchically different from the terms of SNOMED CT thus confirming the high value of the WHO-IHTSDO synergy aiming to frame together, in a joint effort, their respective unique contribution. Recommendations were formulated to WHO and IHTSDO in order to better frame together, in a joint effort, their respective unique contribution ensuring that SNOMED CT and ICF can interoperate in electronic health records

A method for encoding clinical datasets with SNOMED CT

<p>Abstract</p> <p>Background</p> <p>Over the past decade there has been a growing body of literature on how the Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) can be implemented and used in different clinical settings. Yet, for those charged with incorporating SNOMED CT into their organisation's clinical applications and vocabulary systems, there are few detailed encoding instructions and examples available to show how this can be done and the issues involved. This paper describes a heuristic method that can be used to encode clinical terms in SNOMED CT and an illustration of how it was applied to encode an existing palliative care dataset.</p> <p>Methods</p> <p>The encoding process involves: identifying input data items; cleaning the data items; encoding the cleaned data items; and exporting the encoded terms as output term sets. Four outputs are produced: the SNOMED CT reference set; interface terminology set; SNOMED CT extension set and unencodeable term set.</p> <p>Results</p> <p>The original palliative care database contained 211 data elements, 145 coded values and 37,248 free text values. We were able to encode ~84% of the terms, another ~8% require further encoding and verification while terms that had a frequency of fewer than five were not encoded (~7%).</p> <p>Conclusions</p> <p>From the pilot, it would seem our SNOMED CT encoding method has the potential to become a general purpose terminology encoding approach that can be used in different clinical systems.</p

Terminology Services: Standard Terminologies to Control Medical Vocabulary. “Words are Not What they Say but What they Mean”

Data entry is an obstacle for the usability of electronic health records (EHR) applications and the acceptance of physicians, who prefer to document using “free text”. Natural language is huge and very rich in details but at the same time is ambiguous; it has great dependence on context and uses jargon and acronyms. Healthcare Information Systems should capture clinical data in a structured and preferably coded format. This is crucial for data exchange between health information systems, epidemiological analysis, quality and research, clinical decision support systems, administrative functions, etc. In order to address this point, numerous terminological systems for the systematic recording of clinical data have been developed. These systems interrelate concepts of a particular domain and provide reference to related terms and possible definitions and codes. The purpose of terminology services consists of representing facts that happen in the real world through database management. This process is named Semantic Interoperability. It implies that different systems understand the information they are processing through the use of codes of clinical terminologies. Standard terminologies allow controlling medical vocabulary. But how do we do this? What do we need? Terminology services are a fundamental piece for health data management in health environment

Towards the semantic enrichment of Computer Interpretable Guidelines: a method for the identification of relevant ontological terms

Ponència presentada a 2018 The American Medical Informatics Association Annual Symposium (AMIA 2018) celebrat a San Francisco, Estats Units de l'Amèrica del Nord, el 3 de novembre de 2018Clinical Practice Guidelines (CPGs) contain recommendations intended to optimize patient care, produced based on a systematic review of evidence. In turn, Computer-Interpretable Guidelines (CIGs) are formalized versions of CPGs for use as decision-support systems. We consider the enrichment of the CIG by means of an OWL ontology that describes the clinical domain of the CIG, which could be exploited e.g. for the interoperability with the Electronic Health Record (EHR). As a first step, in this paper we describe a method to support the development of such an ontology starting from a CIG. The method uses an alignment algorithm for the automated identification of ontological terms relevant to the clinical domain of the CIG, as well as a web platform to manually review the alignments and select the appropriate ones. Finally, we present the results of the application of the method to a small corpus of CIGs

Concepts and Synonymy in the UMLS Metathesaurus

This paper advances a detailed exploration of the complex relationships among terms, concepts, and synonymy in the UMLS Metathesaurus, and proposes the study and understanding of the Metathesaurus from a model-theoretic perspective. Initial sections provide the background and motivation for such an approach, and a careful informal treatment of these notions is offered as a context and basis for the formal analysis. What emerges from this is a set of puzzles and confusions in the Metathesaurus and its literature pertaining to synonymy and its relation to terms and concepts. A model theory for a segment of the Metathesaurus is then constructed, and its adequacy relative to the informal treatment is demonstrated. Finally, it is shown how this approach clarifies and addresses the puzzles educed from the informal discussion, and how the model-theoretic perspective may be employed to evaluate some fundamental criticisms of the Metathesaurus