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.

Purpose
Information value is created by providing care for specific medical conditions. To assess the appropriate content and time of delivery, a research framework to examine information values at different stages of the care continuum is needed. This study identified essential information to recommend for different stages of Systemic Lupus Erythematosus (SLE) management.
Methods
Using Porter's value chain analysis, we conducted a content analysis of the research literature, clinical practice guidelines, and patient education materials in an education-enabled environment regarding patient with SLE. We also used a natural language processing technique to automatically map the essential information identified into authorized concepts in the National Library of Medicine’s Unified Medical Language Systems.
Results
The essential contents in the diagnosis stage pertained to a general understanding of disease manifestation such as SLE definition, pathophysiology, etiology, prognosis, and progress. The intervening stage highlights information about prominent spheres of therapeutic regimens and administration as well as diverse care providers with relevance to their specific roles. While screening information, such as self-awareness of SLE signs, is valued prior to a clinical visit, the monitoring information follows clinical visits to avoid flaring events. The key concepts identified were "butterfly rash" (C0277942), "anti-inflammatory drugs" (C0003211), "SLE" (C0024141), and "antinuclear antibodies" (C0151480).
Conclusion
Communication of essential information identified at appropriate care stages can increase patient knowledge and reduce anxiety levels to improve self-care.

Citations

• Application of Information Value Chain in Gout Management
  Maranda Russell, Sujin Kim
  Korean Journal of Adult Nursing.2022; 34(4): 351.     CrossRef

This descriptive study was conducted between October 1, and December 31, 1998 in order to provide basic data for understanding the emotional states of patients with systemic lupus erythematosus and their compliance with a medical regimen. Data was collected by using questionnaires administered to 100 lupus inpatients and outpatients at the Kangnam St. Mary's Hospital. Frequencies, percentage, average, standard deviation, t-test, ANOVA, Duncan's multiple range test, Pearson correlation coefficients, and stepwise multiple regression were applied to the data using the SAS program. The results of study are summarized below. The mean compliance score was 91.21. The highest compliance score was found in "risk factor management", followed by "taking medicine", "follow-up care", "daily life management", "stress management", "diet", "activity and rest" in that sequence. The mean depression score was 43.58. 24% for subjects who showed more than mild depression. The compliance score of depressed subjects was significantly lower than that of the subjects without depression. The mean score of anxiety was 44.01. 36% for subjects who had scores lower than 40 points, 37% for those between 41-50 points, and 27% for those with more than 51 points. As for compliance scores according to anxiety levels, the compliance scores for those with anxiety scores of below 40 significantly higher than that of those of the above 51 group. There was a negative correlation between compliance and depression and between compliance and anxiety. In addition, a strong positive relationship was found between depression and anxiety. The major variable affecting compliance was anxiety, accounting for 13.6%. We concluded that when we care for the patients with lupus, we have to consider the outcomes of this study because emotional status affects the lupus patients' compliance. In addition, it is necessary to develop nursing interventions in order to alleviate the lupus patient's depression and anxiety.