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Application of Information Value Chain in Gout Management

성인간호학회지 2022년 34권 4호 p.351 ~ 359
KMID : 0367020220340040351
 ( Russell Maranda ) - University of Kentucky Department of Internal Medicine

김수진 ( Kim Su-Jin ) - University of Kentucky College of Medicine Division of Biomedical Informatics

Abstract

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. The findings can be used as a starting point for filtering the vast amounts of information patients must go through and identifying the most valuable information for patient with gout to adequately manage their symptoms.
KeyWords

Gout, Lupus erythematosus, systemic, Patient education as topic, Natural language processing
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학술진흥재단(KCI) KoreaMed