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Sung S, Park HA, Jung H, Kang H. A SNOMED CT Mapping Guideline for the Local Terms Used to Document Clinical Findings and Procedures in Electronic Medical Records in South Korea: Methodological Study. JMIR Med Inform 2023; 11:e46127. [PMID: 37071456 PMCID: PMC10155087 DOI: 10.2196/46127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/06/2023] [Accepted: 03/30/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND South Korea joined SNOMED International as the 39th member country. To ensure semantic interoperability, South Korea introduced SNOMED CT (Systemized Nomenclature of Medicine-Clinical Terms) in 2020. However, there is no methodology to map local Korean terms to SNOMED CT. Instead, this is performed sporadically and independently at each local medical institution. The quality of the mapping, therefore, cannot be guaranteed. OBJECTIVE This study aimed to develop and introduce a guideline to map local Korean terms to the SNOMED CT used to document clinical findings and procedures in electronic health records at health care institutions in South Korea. METHODS The guidelines were developed from December 2020 to December 2022. An extensive literature review was conducted. The overall structures and contents of the guidelines with diverse use cases were developed by referencing the existing SNOMED CT mapping guidelines, previous studies related to SNOMED CT mapping, and the experiences of the committee members. The developed guidelines were validated by a guideline review panel. RESULTS The SNOMED CT mapping guidelines developed in this study recommended the following 9 steps: define the purpose and scope of the map, extract terms, preprocess source terms, preprocess source terms using clinical context, select a search term, use search strategies to find SNOMED CT concepts using a browser, classify mapping correlations, validate the map, and build the final map format. CONCLUSIONS The guidelines developed in this study can support the standardized mapping of local Korean terms into SNOMED CT. Mapping specialists can use this guideline to improve the mapping quality performed at individual local medical institutions.
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Affiliation(s)
- Sumi Sung
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyeoun-Ae Park
- College of Nursing, Seoul National University, Seoul, Republic of Korea
| | - Hyesil Jung
- Department of Nursing, Inha University, Incheon, Republic of Korea
| | - Hannah Kang
- Kakao Healthcare Corp, Seongnam-si, Gyeonggi-do, Republic of Korea
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Torres FBG, Gomes DC, Ronnau L, Moro CMC, Cubas MR. ISO/TR 12300:2016 for clinical cross-terminology mapping: contribution to nursing. Rev Esc Enferm USP 2020; 54:e303569. [PMID: 32696939 DOI: 10.1590/s1980-220x2018052203569] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 08/22/2019] [Indexed: 11/21/2022] Open
Abstract
This theoretical and reflective study aimed to assess the contribution of the ISO/TR 12300:2016 document for the mapping of nursing terminology. The referred document and related articles were used as an empirical framework. The study analyzed the content of the document, highlighting cardinality and equivalence principles. The standard presents conceptual and operational basis for mapping, with cardinality and equivalence as the support for the categorization of cross-terminology mapping in the area of nursing. Cardinality verifies candidate target terms to represent the source term, while the equivalence degree scale checks semantic correspondence. Among the principles included in the ISO/TR 12300:2016, cardinality and equivalence contribute to the accurate representation of the results of the cross-terminology mapping process and its use should decrease inconsistencies.
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Affiliation(s)
| | - Denilsen Carvalho Gomes
- Programa de Pós-Graduação em Tecnologia em Saúde, Pontifícia Universidade Católica do Paraná, Curitiba, PR, Brazil
| | - Lucas Ronnau
- Programa de Pós-Graduação em Tecnologia em Saúde, Pontifícia Universidade Católica do Paraná, Curitiba, PR, Brazil
| | - Cláudia Maria Cabral Moro
- Programa de Pós-Graduação em Tecnologia em Saúde, Pontifícia Universidade Católica do Paraná, Curitiba, PR, Brazil
| | - Marcia Regina Cubas
- Programa de Pós-Graduação em Tecnologia em Saúde, Pontifícia Universidade Católica do Paraná, Curitiba, PR, Brazil
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Pearce C, McLeod A, Patrick J, Ferrigi J, Bainbridge MM, Rinehart N, Fragkoudi A. Coding and classifying GP data: the POLAR project. BMJ Health Care Inform 2020; 26:bmjhci-2019-100009. [PMID: 31712272 PMCID: PMC7252962 DOI: 10.1136/bmjhci-2019-100009] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND Data, particularly 'big' data are increasingly being used for research in health. Using data from electronic medical records optimally requires coded data, but not all systems produce coded data. OBJECTIVE To design a suitable, accurate method for converting large volumes of narrative diagnoses from Australian general practice records to codify them into SNOMED-CT-AU. Such codification will make them clinically useful for aggregation for population health and research purposes. METHOD The developed method consisted of using natural language processing to automatically code the texts, followed by a manual process to correct codes and subsequent natural language processing re-computation. These steps were repeated for four iterations until 95% of the records were coded. The coded data were then aggregated into classes considered to be useful for population health analytics. RESULTS Coding the data effectively covered 95% of the corpus. Problems with the use of SNOMED CT-AU were identified and protocols for creating consistent coding were created. These protocols can be used to guide further development of SNOMED CT-AU (SCT). The coded values will be immensely useful for the development of population health analytics for Australia, and the lessons learnt applicable elsewhere.
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Affiliation(s)
| | - Adam McLeod
- Outcome Health, East Burwood, Victoria, Australia
| | - Jon Patrick
- Health Language Analytics, Eveleigh, New South Wales, Australia
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Gibaud B, Forestier G, Feldmann C, Ferrigno G, Gonçalves P, Haidegger T, Julliard C, Katić D, Kenngott H, Maier-Hein L, März K, de Momi E, Nagy DÁ, Nakawala H, Neumann J, Neumuth T, Rojas Balderrama J, Speidel S, Wagner M, Jannin P. Toward a standard ontology of surgical process models. Int J Comput Assist Radiol Surg 2018; 13:1397-1408. [PMID: 30006820 DOI: 10.1007/s11548-018-1824-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 07/05/2018] [Indexed: 12/15/2022]
Abstract
PURPOSE The development of common ontologies has recently been identified as one of the key challenges in the emerging field of surgical data science (SDS). However, past and existing initiatives in the domain of surgery have mainly been focussing on individual groups and failed to achieve widespread international acceptance by the research community. To address this challenge, the authors of this paper launched a European initiative-OntoSPM Collaborative Action-with the goal of establishing a framework for joint development of ontologies in the field of SDS. This manuscript summarizes the goals and the current status of the international initiative. METHODS A workshop was organized in 2016, gathering the main European research groups having experience in developing and using ontologies in this domain. It led to the conclusion that a common ontology for surgical process models (SPM) was absolutely needed, and that the existing OntoSPM ontology could provide a good starting point toward the collaborative design and promotion of common, standard ontologies on SPM. RESULTS The workshop led to the OntoSPM Collaborative Action-launched in mid-2016-with the objective to develop, maintain and promote the use of common ontologies of SPM relevant to the whole domain of SDS. The fundamental concept, the architecture, the management and curation of the common ontology have been established, making it ready for wider public use. CONCLUSION The OntoSPM Collaborative Action has been in operation for 24 months, with a growing dedicated membership. Its main result is a modular ontology, undergoing constant updates and extensions, based on the experts' suggestions. It remains an open collaborative action, which always welcomes new contributors and applications.
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Affiliation(s)
| | | | - Carolin Feldmann
- Division of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Paulo Gonçalves
- Instituto Politécnico de Castelo Branco, Castelo Branco, Portugal.,IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Tamás Haidegger
- Antal Bejczy Center for Intelligent Robotics, Óbuda University, Budapest, Hungary.,Austrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria
| | - Chantal Julliard
- Inserm, LTSI - UMR_S 1099, Univ Rennes, Rennes, France.,LIRMM, Université de Montpellier, Montpellier, France.,Stryker GmbH, Freiburg, Germany
| | - Darko Katić
- Karlsruhe Institute of Technology, Institute for Anthropomatics and Robotics, Karlsruhe, Germany.,ArtiMinds Robotics GmbH, Karlsruhe, Germany
| | - Hannes Kenngott
- Department of General, Abdominal and Transplantation Surgery, University of Heidelberg, Heidelberg, Germany
| | - Lena Maier-Hein
- Division of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Keno März
- Division of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Dénes Ákos Nagy
- Antal Bejczy Center for Intelligent Robotics, Óbuda University, Budapest, Hungary.,Austrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria
| | | | - Juliane Neumann
- Innovation Center Computer Assisted Surgery, Leipzig University, Leipzig, Germany
| | - Thomas Neumuth
- Innovation Center Computer Assisted Surgery, Leipzig University, Leipzig, Germany
| | | | | | - Martin Wagner
- Department of General, Abdominal and Transplantation Surgery, University of Heidelberg, Heidelberg, Germany
| | - Pierre Jannin
- Inserm, LTSI - UMR_S 1099, Univ Rennes, Rennes, France
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