Yoo-Mi Kim

A Study on analysis of severity-adjustment length of stay in hospital for community-acquired pneumonia

Abstract Our study was carried out to develop the severity-adjustment model for length of stay in hospital for community-acquired pneumonia so that we analysed the factors on the variation in length of stay(LOS). The subjects were 5,353 community-acquired pneumonia inpatients of the Korean National Hospital Discharge In-depth Injury Survey data from 2004 through 2006. The data were analyzed using t-test and ANOVA and the severity-adjustment model was developed using data mining technique. There are differences according to gender, age, type of insurance, type of admission, but there is no difference of whether patients died in hospital. After yielding the standardized value of the difference between crude and expected length of stay, we analysed the variation of length of stay for community-acquired pneumonia. There was variation of LOS in regional differences and insurance type, though there was no variation according whether patients receive their care in their residences. The variation of length of stay controlling the case mix or severity of illness can be explained the factors of provider. This supply factors in LOS variations should be more studied for individual practice style or patient management practices and healthcare resources or environment. We expect that the severity-adjustment model using administrative databases should be more adapted in other diseases in practical. Key words : Community-Acquired Pneumonia, Length of stay, Severity-Adjustment, Data mining, Administrative Database

Comparison of Hospital Standardized Mortality Ratio Using National Hospital Discharge Injury Data

This study was to develop the assessment of medical service outcome using administration data through compared with hospital standardized mortality ratios(HSMR) in various hospitals. This study analyzed 63,664 cases of Hospital Discharge Injury Data of 2007 and 2008, provided by Korea Centers for Disease Control and Prevention. We used data mining technique and compared decision tree and logistic regression for developing risk-adjustment model of in-hospital mortality. Our Analysis shows that gender, length of stay, Elixhauser comorbidity index, hospitalization path, and primary diagnosis are main variables which influence mortality ratio. By comparing hospital standardized mortality ratios(HSMR) with standardized variables, we found concrete differences (55.6-201.6) of hospital standardized mortality ratios(HSMR) among hospitals. This proves that there are quality-gaps of medical service among hospitals. This study outcome should be utilized more to achieve the improvement of the quality of medical service.

Analysis of Utilization Characteristics, Health Behaviors and Health Management Level of Participants in Private Health Examination in a General Hospital

Abstract This study aims to analyze characteristics, health behaviors and health management level related to private health examination recipients in one general hospital. To achieve this, we analyzed 150,501 cases of private health examination data for 11 years from 2001 to 2011 for 20,696 participants in 2011 in a Dae-Jeon general hospital health examination center. The cluster analysis for classify private health examination group is used z-score standardization of K-means clustering method. The logistic regression analysis, decision tree and neural network analysis are used to periodic/ non-periodic private health examination classification model. 1,000 people were selected as a customer management business group that has high probability to be non-periodic private health examination patients in new private health examination. According to results of this study, private health examination group was categorized by new, periodic and non-periodic group. New participants in private health examination were more 30~39 years old person than other age groups and more patients suspected of having renal disease. Periodic participants in private health examination were more male participants and more patients suspected of having hyperlipidemia. Non-periodic participants in private health examination were more smoking and sitting person and more patients suspected of having anemia and diabetes mellitus. As a result of decision tree, variables related to non-periodic participants in private health examination were sex, age, residence, exercise, anemia, hyperlipidemia, diabetes mellitus, obesity and liver disease. In particular, 71.4% of non-periodic participants were female, non-anemic, non-exercise, and suspicious obesity person. To operation of customized customer management business for private health examination will contribute to efficiency in health examination center.

A Study on Factors of Management of Diabetes Mellitus using Data Mining

The Objectives: The purpose of this study is to identify the factors related to management of DM in Korea. Methods: The subjects selected by using data of National Health and Nutrition Survey(NHANS) in 2005 were 415 adults, aged 20 and older, and diagnosed with DM. This study used data mining algorithms. This study validated the predictive power of data mining algorithms by comparing the performance of logistic regression, decision tree, and Neural Network on the basic of validation, it was found that the model performance of decision tree was the best among the above three techniques. Result: First, awareness of DM was positively associated with age, residential area, and job. The most important factor of DM awareness is age. Awareness rate of DM with 52 age over is 76.1%. Among the ≥52 age group, an important factor is family history. Among patients who are 52 years or over with family history of DM, an important factor is job. The awareness rate of patients who are 52 age over, family, history of DM, and professionals is 95.0%. Second, treatment of DM was also positively associated with awareness, region, and job. The most important factor of DM treatment is DM awareness. Treatment rate of patients who are aware of DM is 84.8%. Among patients who have awareness of DM, an important factor is region. The awareness rate of patients who are aware of DM in rural area is 10.4%. Conclusion: Finally, the result of analysis suggest that DM management programs should consider group characteristic of DM patients.

A Study on Regional Variations for Disease-specific Cardiac Arrest

The purpose of this study was to examine how region-specific characteristics affect the occurrence of cardiac arrest. To analyze, we combined a unique data set including key indicators of health condition and cardiac arrest occurrence at the 244 small administrative districts. Our data came from two main sources in Korea Center For Disease Control and Prevention (KCDC): 2010 Out-of-Hospital Cardiac Arrest Surveillance and Community Health Survey. We analyzed data by using multiple regression, geographically weighted regression and decision tree. Decision tree model is selected as the final model to explain regional variations of cardiac arrest. Factors of regional variations of cardiac arrest occurrence are population density, diagnosis rates of hypertension, stress level, participating screening level, high drinking rate, and smoking rate. Taken as a whole, accounting for geographical variations of health conditions, health behaviors and other socioeconomic factors are important when regionally customized health policy is implemented to decrease the cardiac arrest occurrence.

A Study on the Determinants of Rescreening for Using the Private Health Screening Program

Abstract This study aims to identify factors related to rescreening the private health screening center. Data from private health screening center with seven district centers were analyzed. The number of subjects was 70,250 from 2008 through 2015. Socio-demographic characteristics, morbidity, history, physical measurement, laboratory test, and health behavior factors were analyzed the association with health rescreening using chi-square test and negative binominal regression. 19.2% of subjects were recheck their health status on each year from 2008 to 2012. The socio-demographic characteristics related to reuse private health screening center were male, 30-40 aged, Daegu, Gwangju, Jeonnam and Gyeongnam region residents. And overweight and pre-hypertension were also factors association with number of using the private health screening program. People who control their health risk factors such as alcohol intake, smoking and mental health and execute physical exercise tend to use health screening program regularly. Our finding suggest that customer relationship management can execute and private health screening center enable their strategic plan based on data-evidence.Key Words : health screening program on private sector, reuse, health behavior, customer relationship management