SNOMED-CT como modelo de sistema de linguagem padronizada à enfermagem: revisão integrativa

RESUMOObjetivo: Descrever a utilização do Systematized Nomenclature of Medicine – Clinical Terms (SNOMED-CT) como modelo de interoperabilidade das terminologias da enfermagem no contexto nacional e internacional.Metodologia: Trata-se de revisão integrativa da literatura segundo Cooper, que buscou artigos em português, inglês e espanhol, publicados entre setembro de 2011 a novembro de 2018 nas bases de dados BVS, PubMed, SCOPUS, CINAHL, EMBASE e Web of Science, finalizando em uma amostra de 15 artigos.Resultados: O SNOMED-CT é uma nomenclatura multiprofissional utilizada pela enfermagem em diferentes contextos de cuidado, sendo associada com outras linguagens padronizadas da disciplina, como CIPE®, NANDA-I e Omaha System.Conclusão: Esta revisão mostrou que o uso do SNOMED-CT é incipiente no contexto nacional, justificando a necessidade de desenvolvimento de estudos visando o mapeamento dos sistemas de linguagem padronizadas existentes, especialmente a NANDA-I, CIPE® e Omaha System, para fins de adequar a implementação do SNOMED-CT.Palavras-chave: Informática em enfermagem. Terminologia padronizada em enfermagem. Systematized Nomenclature of Medicine. Classificação. Interoperabilidade da informação em saúde

Thesauri on the Web: current developments and trends

This article provides an overview of recent developments relating to the application of thesauri in information organisation and retrieval on the World Wide Web. It describes some recent thesaurus projects undertaken to facilitate resource description and discovery and access to wide-ranging information resources on the Internet. Types of thesauri available on the Web, thesauri integrated in databases and information retrieval systems, and multiple-thesaurus systems for cross-database searching are also discussed. Collective efforts and events in addressing the standardisation and novel applications of thesauri are briefly reviewed

Multicenter Breast Cancer Collaborative Registry

The Breast Cancer Collaborative Registry (BCCR) is a multicenter web-based system that efficiently collects and manages a variety of data on breast cancer (BC) patients and BC survivors. This registry is designed as a multi-tier web application that utilizes Java Servlet/JSP technology and has an Oracle 11g database as a back-end. The BCCR questionnaire has accommodated standards accepted in breast cancer research and healthcare. By harmonizing the controlled vocabulary with the NCI Thesaurus (NCIt) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), the BCCR provides a standardized approach to data collection and reporting. The BCCR has been recently certified by the National Cancer Institute’s Center for Biomedical Informatics and Information Technology (NCI CBIIT) as a cancer Biomedical Informatics Grid (caBIG®) Bronze Compatible product

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

A methodology for biomedical ontology reuse

The abundance of biomedical ontologies is beneficial to the development of biomedical related systems. However, existing biomedical ontologies such as the National Cancer Institute Thesaurus (NCIT), Foundational Model of Anatomy (FMA) and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) are often too large to be implemented in a particular system and cause unnecessary high usage of memory and slow down the system’s processing time. Developing a new ontology from scratch just for the use of a particular system is deemed as inefficient since it requires additional time and causes redundancy. Thus, a potentially better method is by reusing existing ontologies. However, currently there are no specific methods or tools for reusing ontologies. This paper aims to provide readers with a step by step method in reusing ontologies together with the tools that can be used to ease the process

LESS Can Indeed Be More: Linguistic and Conceptual Challenges in the Age of Interoperability

International audienceThe advent of the Semantic Web and, more recently, of the Linked Data initiative, has paved the way for new perspectives and opportunities in Terminology , namely regarding the operationalization of terminological products. Within the biomedical domain, changes have been substantial in the past decades and at their heart stand the current challenges regarding the production, use, storage and dissemination of medical data, information, and knowledge. In a context where biomedical terminological resources are becoming increasingly concept-oriented, terminology work should reflect a double dimension (both linguistic and conceptual) that may, in turn, support the aspired operationalization and in-teroperability in this field. Therefore, the purpose of this paper is to present a case study, based around the concept of , in which a methodology anchored in Terminology's double dimension aims to contribute to the enrichment of the Systematized Nomenclature of Medicine-Clinical Terms (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

Int J Tuberc Lung Dis

OBJECTIVE:To measure the frequency of diseases related to latent tuberculosis infection (LTBI) and tuberculosis (TB), we assessed the agreement between diagnosis codes for TB or LTBI in electronic health records (EHRs) and insurance claims for the same person.METHODS:In a US population-based, retrospective cohort study, we matched TB-related Systematized Nomenclature of Medicine\u2013Clinical Terms (SNOMED CT) EHR codes and International Statistical Classification of Diseases, 10thRevision, Clinical Modification (ICD-10-CM) claims codes. Furthermore, LTBI was identified using a published ICD-based algorithm and all LTBI- and TB-related SNOMED CT codes.RESULTS:Of people with the 10 most frequent TB-related claim codes, 50% did not have an exact-matched EHR code. Positive tuberculin skin test was the most frequent unmatched EHR code and people with the 10 most frequent TB EHR codes, 40% did not have an exact-matched claim code. The most frequent unmatched claim code was TB screening encounter. EHR codes for LTBI matched to claims codes for TB testing; pulmonary TB; and nonspecific, positive or adverse tuberculin reaction.CONCLUSION:TB-related EHR codes and claims diagnostic codes often disagree, and people with claims codes for LTBI have unexpected EHR codes, indicating the need to reconcile these coding systems.CC999999/ImCDC/Intramural CDC HHS/United States2020-10-26T00:00:00Z32718404PMC75867228583vault:3611