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Background <p>Lifestyle factors, such as smoking, alcohol consumption, and physical activity, are among the most critical modifiable risk factors for morbidity and mortality; however, their representation in health data remains inconsistent and unstructured. Although electronic medical record (EMR) systems are widely adopted in Korea, interoperability remains limited due to heterogeneous formats and unstandardized content. This study aimed to establish a standardized framework for lifestyle data aligned with international standards.</p> Methods <p>Items were collected from national initiatives, tertiary hospital questionnaires, and clinical reports. Overlapping items were prioritized and supplemented with clinically meaningful additions to develop a draft framework. The draft was refined through focus group interviews (FGIs) with healthcare professionals and hospital information system experts to ensure clinical relevance and technical feasibility. Values of the finalized framework were standardized by mapping to internationally recognized terminologies in collaboration with the Systematized Nomenclature of Clinical Terms (SNOMED CT) Korean National Release Center (NRC) at the Korea Health Information Service.</p> Results <p>The final framework comprised three classes: smoking class (5 elements, 15 values), alcohol consumption class (4 elements, 12 values), and physical activity class (7 elements, 41 values). All components were mapped to internationally recognized terminologies, such as SNOMED CT and Logical Observation Identifiers Names and Codes (LOINC).</p> Conclusions <p>This proposed standardized lifestyle data framework harmonizes heterogeneous data sources and provides a foundation for integration into Korea’s interoperability roadmap. By enabling structured and interoperable representation of lifestyle factors, it may support patient-centered care, facilitate secondary data use, and strengthen epidemiological and health informatics research.</p>

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A structured framework for standardizing lifestyle data: core data interoperability (CDI) model with expert validation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Yoon Ji Kim,
  • Jiwon Choi,
  • AeKyung Kwon,
  • Ahjung Byun,
  • Hahyun You,
  • Ye Seul Bae,
  • Jae-Heon Kang

摘要

Background

Lifestyle factors, such as smoking, alcohol consumption, and physical activity, are among the most critical modifiable risk factors for morbidity and mortality; however, their representation in health data remains inconsistent and unstructured. Although electronic medical record (EMR) systems are widely adopted in Korea, interoperability remains limited due to heterogeneous formats and unstandardized content. This study aimed to establish a standardized framework for lifestyle data aligned with international standards.

Methods

Items were collected from national initiatives, tertiary hospital questionnaires, and clinical reports. Overlapping items were prioritized and supplemented with clinically meaningful additions to develop a draft framework. The draft was refined through focus group interviews (FGIs) with healthcare professionals and hospital information system experts to ensure clinical relevance and technical feasibility. Values of the finalized framework were standardized by mapping to internationally recognized terminologies in collaboration with the Systematized Nomenclature of Clinical Terms (SNOMED CT) Korean National Release Center (NRC) at the Korea Health Information Service.

Results

The final framework comprised three classes: smoking class (5 elements, 15 values), alcohol consumption class (4 elements, 12 values), and physical activity class (7 elements, 41 values). All components were mapped to internationally recognized terminologies, such as SNOMED CT and Logical Observation Identifiers Names and Codes (LOINC).

Conclusions

This proposed standardized lifestyle data framework harmonizes heterogeneous data sources and provides a foundation for integration into Korea’s interoperability roadmap. By enabling structured and interoperable representation of lifestyle factors, it may support patient-centered care, facilitate secondary data use, and strengthen epidemiological and health informatics research.