Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies

Hwang, Ji Eun; Park, Hyeoun-Ae; Shin, Soo-Yong

doi:10.4258/hir.2021.27.4.287

Cited by 5 publications

(4 citation statements)

References 8 publications

(8 reference statements)

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“…It allows adopters to either endorse existing models as is or to highlight their missing elements, thus maximizing the added value of their information [25]. In current FAIRification initiatives [26,27], interoperability is considered to be achieved simply by employing standards, international terminologies, and classifications that unambiguously define the meaning of concepts [28]. Although this may enhance data FAIRness, it does not necessarily guarantee the consistent understanding of concepts from different systems.…”

Section: Introductionmentioning

confidence: 99%

Semantic interoperability: ontological unpacking of a viral conceptual model

2022

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Background Genomics and virology are unquestionably important, but complex, domains being investigated by a large number of scientists. The need to facilitate and support work within these domains requires sharing of databases, although it is often difficult to do so because of the different ways in which data is represented across the databases. To foster semantic interoperability, models are needed that provide a deep understanding and interpretation of the concepts in a domain, so that the data can be consistently interpreted among researchers. Results In this research, we propose the use of conceptual models to support semantic interoperability among databases and assess their ontological clarity to support their effective use. This modeling effort is illustrated by its application to the Viral Conceptual Model (VCM) that captures and represents the sequencing of viruses, inspired by the need to understand the genomic aspects of the virus responsible for COVID-19. For achieving semantic clarity on the VCM, we leverage the “ontological unpacking” method, a process of ontological analysis that reveals the ontological foundation of the information that is represented in a conceptual model. This is accomplished by applying the stereotypes of the OntoUML ontology-driven conceptual modeling language.As a result, we propose a new OntoVCM, an ontologically grounded model, based on the initial VCM, but with guaranteed interoperability among the data sources that employ it. Conclusions We propose and illustrate how the unpacking of the Viral Conceptual Model resolves several issues related to semantic interoperability, the importance of which is recognized by the “I” in FAIR principles. The research addresses conceptual uncertainty within the domain of SARS-CoV-2 data and knowledge.The method employed provides the basis for further analyses of complex models currently used in life science applications, but lacking ontological grounding, subsequently hindering the interoperability needed for scientists to progress their research.

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Section: Introductionmentioning

confidence: 99%

Semantic interoperability: ontological unpacking of a viral conceptual model

2022

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“…In 2020, to achieve semantic interoperability in healthcare, South Korea joined the SNOMED International as the 39 th member country. As a way to promote the use of SNOMED CT in Korea, there have been various national initiatives such as mapping Korean Classification of Disease-7 (KCD-7), national health insurance claims codes which is also called electronic data interchange (EDI) codes for procedures, pharmaceutical products, narrative medical records of gastrectomy patients, and Korean national health checkup questionnaire to SNOMED CT [4][5][6][7][8]. However, the national health insurance claims codes for laboratory tests were not mapped to SNOMED CT, limiting the use of laboratory tests information for patient care or clinical research.…”

Section: Introductionmentioning

confidence: 99%

Mapping Korean National Health Insurance Claim Codes for Laboratory Test to SNOMED CT

Sung

Park

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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The aim of this study was to map Korean national health insurance claims codes for laboratory tests to SNOMED CT. The mapping source codes were 4,111 claims codes for laboratory test and mapping target codes were the International Edition of SNOMED CT released on July 31, 2020. We used rule-based automated and manual mapping methods. The mapping results were validated by two experts. Out of 4,111 codes, 90.5% were mapped to the concepts of procedure hierarchy in SNOMED CT. Of them, 51.4% of the codes were exactly mapped to SNOMED CT concepts, and 34.8% of the codes were mapped to SNOMED CT concepts as one-to-one mapping.

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“…The South Korean government, aiming to encourage the use of standard terminology in health care institutions, joined SNOMED International as the 39th member country and introduced SNOMED CT in 2020 to ensure interoperability. Subsequently, various efforts have mapped terms used to document clinical findings and procedures in EMRs or national health checkup questionnaires, classification systems including KCD-7, and EDI codes to the SNOMED CT [12][13][14][15][16][17]. Furthermore, these results are used for research purposes in the Common Data Model [17,18].…”

Section: Introductionmentioning

confidence: 99%

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 (Preprint)

Sung¹,

Park²,

Jung³

et al. 2023

Preprint

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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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Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies

Cited by 5 publications

References 8 publications

Semantic interoperability: ontological unpacking of a viral conceptual model

Semantic interoperability: ontological unpacking of a viral conceptual model

Mapping Korean National Health Insurance Claim Codes for Laboratory Test to SNOMED CT

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 (Preprint)

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