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Objectives: This study aims to identify the factors associated with regular medical services utilization of chronic disease patients. Methods: The research selected 4,489 adults aged over 30, diagnosed with hypertension, diabetes, hyperlipidemia, hypercholesterolemia, from the Korea health panel. We analyzed states of regular medical service utilization using descriptive statistics. Multiple regression analysis was used to examine the main factors associated with regular medical services utilization in chronic disease patients. Results: In terms of socio-demographic factors, gender, age, marital status, education level, employment, household income and disability were significantly different between hypertension, diabetes, hyperlipidemia and hypercholesterolemia. Among health status and behavioral factors, number of chronic diseases, subjective health status, smoking, high risk drinking, regular meals, physical activity, obesity were significantly different. From the multiple logistic regression analysis, age, number of chronic diseases, obesity, type of chronic diseases were associated with regular medical services utilization. Conclusions: It is necessary to develop effective health education programs and individualized approach to improve continuous management in chronic diseases patients.

This study was to develop the predictive model for severity-adjusted mortality of inpatients with multiple chronic conditions and analyse the factors on the variation of hospital standardized mortality ratio(HSMR) to propose the plan to reduce the variation. We collect the data "Korean National Hospital Discharge In-depth Injury Survey" from 2008 to 2010 and select the final 110,700 objects of study who have chronic diseases for principal diagnosis and who are over the age of 30 with more than 2 chronic diseases including principal diagnosis. We designed a severity-adjusted mortality predictive model with using data-mining methods (logistic regression analysis, decision tree and neural network method). In this study, we used the predictive model for severity-adjusted mortality ratio by the decision tree using Elixhauser comorbidity index. As the result of the hospital standardized mortality ratio(HSMR) of inpatients with multiple chronic conditions, there were statistically significant differences in HSMR by the insurance type, bed number of hospital, and the location of hospital. We should find the method based on the result of this study to manage mortality ratio of inpatients with multiple chronic conditions efficiently as the national level. So we should make an effort to increase the quality of medical treatment for inpatients with multiple chronic diseases and to reduce growing medical expenses.

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