Purpose: This study introduces information value chain analysis by identifying essential information for use in gout care management. Part I reviews the essential concepts of information value chain analysis first introduced by Porter. Part II applies the analysis to determine the information values of patient health information and explores ways in which health information technologies can be best utilized to provide that information to patients with gout. Methods: We combined value chain analysis with natural language processing and machine learning techniques to develop algorithms that can identify patients with gout flares using clinical notes. As one of the first signs that the disease was not being controlled, variables found to be associated with gout flares were considered valuable information for patients with gout. Results: The best performing model, in terms of both gout flare prediction and association identification, was the comprehensive model that not only included concepts from all stages of the value chain but also designated natural language processing concepts from every care stage as surrogate variables. Additionally, all administrative codes traditionally associated with gout and its treatment were included as surrogate outcome variables. Conclusion: This study introduced information value chain analysis and applied it to develop a computer-based method with theoretical underpinnings to identify the concepts associated with gout flares.

Original languageEnglish
Pages (from-to)351-359
Number of pages9
JournalKorean Journal of Adult Nursing
Issue number4
StatePublished - Aug 2022

Bibliographical note

Publisher Copyright:
© 2022 Korean Society of Adult Nursing


  • Gout
  • Lupus erythematosus
  • Natural language processing
  • Patient education as topic
  • Systemic

ASJC Scopus subject areas

  • General Nursing


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