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Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies
OBJECTIVES: An increasing emphasis has been placed on the integration of clinical data and patient-generated health data (PGHD), which are generated outside of hospitals. This study explored the possibility of using standard terminologies to represent PGHD for data integration. METHODS: We chose the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Korean Society of Medical Informatics
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654331/ https://www.ncbi.nlm.nih.gov/pubmed/34788909 http://dx.doi.org/10.4258/hir.2021.27.4.287 |
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author | Hwang, Ji Eun Park, Hyeoun-Ae Shin, Soo-Yong |
author_facet | Hwang, Ji Eun Park, Hyeoun-Ae Shin, Soo-Yong |
author_sort | Hwang, Ji Eun |
collection | PubMed |
description | OBJECTIVES: An increasing emphasis has been placed on the integration of clinical data and patient-generated health data (PGHD), which are generated outside of hospitals. This study explored the possibility of using standard terminologies to represent PGHD for data integration. METHODS: We chose the 2020 general health checkup questionnaire of the Korean Health Screening Program as a resource. We divided every component of the questionnaire into entities and values, which were mapped to standard terminologies—Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) version 2020-07-31 and Logical Observation Identifiers Names and Codes (LOINC) version 2.68. RESULTS: Eighty-nine items were derived from the 17 questions of the 2020 health examination questionnaire, of which 76 (85.4%) were mapped to standard terms. Fifty-two items were mapped to SNOMED CT and 24 items were mapped to LOINC. Among the items mapped to SNOMED CT, 35 were mapped to pre-coordinated expressions and 17 to post-coordinated expressions. Forty items had one-to-one relationships, and 17 items had one-to-many relationships. CONCLUSIONS: We achieved a high mapping rate (85.4%) by using both SNOMED CT and LOINC. However, we noticed some issues while mapping the Korean general health checkup questionnaire (i.e., lack of explanations, vague questions, and overly narrow concepts). In particular, items combining two or more concepts into a single item were not appropriate for mapping using standard terminologies. Although it is not the case that all items need to be expressed in standard terminology, essential items should be presented in a way suitable for mapping to standard terminology by revising the questionnaire in the future. |
format | Online Article Text |
id | pubmed-8654331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Korean Society of Medical Informatics |
record_format | MEDLINE/PubMed |
spelling | pubmed-86543312021-12-20 Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies Hwang, Ji Eun Park, Hyeoun-Ae Shin, Soo-Yong Healthc Inform Res Original Article OBJECTIVES: An increasing emphasis has been placed on the integration of clinical data and patient-generated health data (PGHD), which are generated outside of hospitals. This study explored the possibility of using standard terminologies to represent PGHD for data integration. METHODS: We chose the 2020 general health checkup questionnaire of the Korean Health Screening Program as a resource. We divided every component of the questionnaire into entities and values, which were mapped to standard terminologies—Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) version 2020-07-31 and Logical Observation Identifiers Names and Codes (LOINC) version 2.68. RESULTS: Eighty-nine items were derived from the 17 questions of the 2020 health examination questionnaire, of which 76 (85.4%) were mapped to standard terms. Fifty-two items were mapped to SNOMED CT and 24 items were mapped to LOINC. Among the items mapped to SNOMED CT, 35 were mapped to pre-coordinated expressions and 17 to post-coordinated expressions. Forty items had one-to-one relationships, and 17 items had one-to-many relationships. CONCLUSIONS: We achieved a high mapping rate (85.4%) by using both SNOMED CT and LOINC. However, we noticed some issues while mapping the Korean general health checkup questionnaire (i.e., lack of explanations, vague questions, and overly narrow concepts). In particular, items combining two or more concepts into a single item were not appropriate for mapping using standard terminologies. Although it is not the case that all items need to be expressed in standard terminology, essential items should be presented in a way suitable for mapping to standard terminology by revising the questionnaire in the future. Korean Society of Medical Informatics 2021-10 2021-10-31 /pmc/articles/PMC8654331/ /pubmed/34788909 http://dx.doi.org/10.4258/hir.2021.27.4.287 Text en © 2021 The Korean Society of Medical Informatics https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Hwang, Ji Eun Park, Hyeoun-Ae Shin, Soo-Yong Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies |
title | Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies |
title_full | Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies |
title_fullStr | Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies |
title_full_unstemmed | Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies |
title_short | Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies |
title_sort | mapping the korean national health checkup questionnaire to standard terminologies |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654331/ https://www.ncbi.nlm.nih.gov/pubmed/34788909 http://dx.doi.org/10.4258/hir.2021.27.4.287 |
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