• Journal of Preventive Medicine and Public Health
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• v.38 no.2
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• pp.132-140
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• 2005

Objectives: To propose a risk-adjustment model from insurance claims data, and analyze the changes in cesarean section rates of healthcare organizations after adjusting for risk distribution. Methods: The study sample included delivery claims data from January to September, 2003. A risk-adjustment model was built using the 1st quarter data, and the 2nd and 3rd quarter data were used for a validation test. Patients' risk factors were adjusted using a logistic regression analysis. The c-statistic and Hosmer-Lemeshow test were used to evaluate the performance of the risk-adjustment model. Crude, predicted and risk-adjusted rates were calculated, and compared to analyze the effects of the adjustment. Results: Nine risk factors (malpresentation, eclampsia, malignancy, multiple pregnancies, problems in the placenta, previous Cesarean section, older mothers, bleeding and diabetes) were included in the final risk-adjustment model, and were found to have statistically significant effects on the mode of delivery. The c-statistic (0.78) and Hosmer-Lemeshow test ($x^2$=0.60, p=0.439) indicated a good model performance. After applying the 2nd and 3rd quarter data to the model, there were no differences in the c-statistic and Hosmer-Lemeshow $x^2$. Also, risk factor adjustment led to changes in the ranking of hospital Cesarean section rates, especially in tertiary and general hospitals. Conclusion: This study showed a model performance, using medical record abstracted data, was comparable to the results of previous studies. Insurance claims data can be used for identifying areas where risk factors should be adjusted. The changes in the ranking of hospital Cesarean section rates implied that crude rates can mislead people and therefore, the risk should be adjusted before the rates are released to the public. The proposed risk-adjustment model can be applied for the fair comparisons of the rates between hospitals.

• Journal of Preventive Medicine and Public Health
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• v.39 no.1
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• pp.13-20
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• 2006

Objectives: To propose a risk-adjustment model with using insurance claims data and to analyze whether or not the outcomes of non-emergent and isolated coronary artery bypass graft surgery (CABG) differed between the low- and high-volume hospitals for the patients who are at different levels of surgical risk. Methods: This is a cross-sectional study that used the 2002 data of the national health insurance claims. The study data set included the patient level data as well as all the ICD-10 diagnosis and procedure codes that were recorded in the claims. The patient's biological, admission and comorbidity information were used in the risk-adjustment model. The risk factors were adjusted with the logistic regression model. The subjects were classified into five groups based on the predicted surgical risk: minimal (<0.5%), low (0.5% to 2%), moderate (2% to 5%), high (5% to 20%), and severe (=20%). The differences between the low- and high-volume hospitals were assessed in each of the five risk groups. Results: The final risk-adjustment model consisted of ten risk factors and these factors were found to have statistically significant effects on patient mortality. The C-statistic (0.83) and Hosmer-Lemeshow test ($x^2=6.92$, p=0.55) showed that the model's performance was good. A total of 30 low-volume hospitals (971 patients) and 4 high-volume hospitals (1,087 patients) were identified. Significant differences for the in-hospital mortality were found between the low- and high-volume hospitals for the high (21.6% vs. 7.2%, p=0.00) and severe (44.4% vs. 11.8%, p=0.00) risk patient groups. Conclusions: Good model performance showed that insurance claims data can be used for comparing hospital mortality after adjusting for the patients' risk. Negative correlation was existed between surgery volume and in-hospital mortality. However, only patients in high and severe risk groups had such a relationship.

• Health Policy and Management
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• v.26 no.4
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• pp.359-372
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• 2016

Background: The purpose of this study was to develop risk-adjustment models for acute stroke mortality that were based on data from Health Insurance Review and Assessment Service (HIRA) dataset and to evaluate the validity of these models for comparing hospital performance. Methods: We identified prognostic factors of acute stroke mortality through literature review. On the basis of the avaliable data, the following factors was included in risk adjustment models: age, sex, stroke subtype, stroke severity, and comorbid conditions. Survey data in 2014 was used for development and 2012 dataset was analysed for validation. Prediction models of acute stroke mortality by stroke type were developed using logistic regression. Model performance was evaluated using C-statistics, $R^2$ values, and Hosmer-Lemeshow goodness-of-fit statistics. Results: We excluded some of the clinical factors such as mental status, vital sign, and lab finding from risk adjustment model because there is no avaliable data. The ischemic stroke model with age, sex, and stroke severity (categorical) showed good performance (C-statistic=0.881, Hosmer-Lemeshow test p=0.371). The hemorrhagic stroke model with age, sex, stroke subtype, and stroke severity (categorical) also showed good performance (C-statistic=0.867, Hosmer-Lemeshow test p=0.850). Conclusion: Among risk adjustment models we recommend the model including age, sex, stroke severity, and stroke subtype for HIRA assessment. However, this model may be inappropriate for comparing hospital performance due to several methodological weaknesses such as lack of clinical information, variations across hospitals in the coding of comorbidities, inability to discriminate between comorbidity and complication, missing of stroke severity, and small case number of hospitals. Therefore, further studies are needed to enhance the validity of the risk adjustment model of acute stroke mortality.

• Health Policy and Management
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• v.27 no.2
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• pp.149-156
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• 2017

Background: This study was conducted to evaluate the performance of the Hierarchical Condition Category (HCC) model, identify potentially high-cost patients, and examine the effects of adding prior utilization to the risk model using Korean claims data. Methods: We incorporated 2 years of data from the National Health Insurance Services-National Sample Cohort. Five risk models were used to predict health expenditures: model 1 (age/sex groups), model 2 (the Center for Medicare and Medicaid Services-HCC with age/sex groups), model 3 (selected 54 HCCs with age/sex groups), model 4 (bed-days of care plus model 3), and model 5 (medication-days plus model 3). We evaluated model performance using $R^2$ at individual level, predictive positive value (PPV) of the top 5% of high-cost patients, and predictive ratio (PR) within subgroups. Results: The suitability of the model, including prior use, bed-days, and medication-days, was better than other models. $R^2$ values were 8%, 39%, 37%, 43%, and 57% with model 1, 2, 3, 4, and 5, respectively. After being removed the extreme values, the corresponding $R^2$ values were slightly improved in all models. PPVs were 16.4%, 25.2%, 25.1%, 33.8%, and 53.8%. Total expenditure was underpredicted for the highest expenditure group and overpredicted for the four other groups. PR had a tendency to decrease from younger group to older group in both female and male. Conclusion: The risk adjustment models are important in plan payment, reimbursement, profiling, and research. Combined prior use and diagnostic data are more powerful to predict health costs and to identify high-cost patients.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.665-680
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• 2009

This study aims at analyzing the performance of Real Estate Investment Trusts(REITs) by Risk Adjustment Model. The main results are as follows. Firstly, most property types of REITs gain positive(+) excess overall returns at first and second period. On the contrary, most property types of REITs gain negative(-) excess overall returns and their standard deviations are larger at financial crisis period. Secondly, lodging, regional mall and commercial mortgage show lower risk-lower return, and freestanding, apartment and specialty show higher risk-higher return than average REITs, according to the CAPM results of . Moreover CAPM results of show the characteristics of REITs as investment commodities changes into higher risk-higher return for financial crisis period. Lastly, risk adjusted demanded returns of REITs are affected positively(+) by systemic risks and negatively(-) by unsystemic risks, according to the Risk Adjustment Model results of both and . Comparing risk adjusted demanded returns of REITs with their realized returns, healthcare reveals the largest outperformance.

• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.757-764
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• 2011

Objective: The aim of this study was to provide a method to improve the compliance and reduce the time by reducing the workload during the model change work. Background: The enterprises are constructing the small quantity batch production system by increasing the number of model change and reducing model-changing. However, the compliance is low because the work is strenuous and high skills are needed, so the system management is facing with many difficulties. Method: After classifying the model change work according to the purposes(preparation, change and adjustment) with the target of mascara filling machine, element tasks time were measured and the motion analysis(therblig symbol) and REBA analysis were performed. The study incorporated 3 independent variables as the number of motion, REBA score and the element time. The dependent variable is the type of element work as preparation, change and adjustment. The statistical test was performed by one-way ANOVA(${\alpha}$ < 0.05). Results: For the preparation, the number of motions appeared in the order of Use(U), Transport Loaded(TL), and Position(P). The order appeared in change is Use(U), Release Load(RL), and Grasp (G). The adjustment appeared in the order of Position(P) and Use(U). The results of average motion time as the element work times divided by the number of motion appeared in the order of adjustment(1.85sec/motion), preparation(1.11sec/motion), and change(0.62sec/motion). The results of REBA showed that the average risk level of change and adjustment were medium, but 53.1% of change and 42.9% of adjustment were evaluated as high. Conclusion: Reducing the avoidance and improving the compliance of work could be expected if the job autonomy were improved by improving the working postures with high risk level. Application: It is expected to solve the problem of reducing the time of model change work in the small quantity batch production system. The future work is to carry out the improvement directions found in the results and compare the results after improvement.

• Journal of Preventive Medicine and Public Health
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• v.34 no.4
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• pp.337-346
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• 2001

Objective : To determine the clinical risk factors associated with the mode of delivery decision and to compare cesarean section rates after adjusting for risk factors identified among Korean hospitals. Methods Data were collected from 9 general hospitals in two provincial regions by medical record abstraction during February 2000. A total of 3,467 cases were enrolled and analyzed by stepwise logistic regression. Performance of the risk-adjustment model (discrimination and calibration) was evaluated by the C statistic and the Hosmer-Lemeshow test. Crude rates, predicted rates with 95% confidence intervals, and adjusted rates of cesarean section were calculated and compared among the hospitals. Results : The average crude cesarean section rate was 53.2%, ranging from 39.4% to 65.7%. Several risk factors such as maternal age, previous history of cesarean section, placenta previa, placental abruption, malpresentation, amniotic fluid abnormality, gestational anemia, infant body weight, pregnancy-induced hypertension, and chorioamnionitis were found to have statistically significant effects on the mode of delivery. It was confirmed that information about most of these risk factors was able to be collected through the national health insurance claims database in Korea. Performance of the risk-adjustment model was good (c statistic=0.815, Hosmer-Lemeshow test=0.0621). Risk factor adjustment did lead to some change in the rank of hospital cesarean section rates. The crude rates of three hospitals were beyond 95% confidence intervals of the predicted rates. Conclusions : Considering that cesarean section rates in Korean hospitals are too high, it is apparent that some policy interventions need to be introduced. The concept and methodology of risk adjustment should be used in the process of health policy development to lower the cesarean section rate in Korea.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.575-586
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• 2011

In this paper, we study an asymptotic behavior of the finite-time ruin probability of the compound Poisson model in the case that the initial surplus is large. To compare an exact ruin probability with an approximate one, we place the focus on the exact calculation for the ruin probability when the claim size distribution is regularly varying tailed (i.e. exponential claims and inverse Gaussian claims). We estimate an adjustment coefficient in these examples and show the relationship between the adjustment coefficient and the safety premium. The illustration study shows that as the safety premium increases so does the adjustment coefficient. Larger safety premium means lower "long-term risk", which only stands to reason since higher safety premium means a faster rate of safety premium income to offset claims.

• Health Policy and Management
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• v.17 no.3
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• pp.87-105
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• 2007

The purpose of this study was to analyze whether nonemergency, isolated coronary artery bypass graft (CABG) surgery for high- or low-risk patients biases the assessment of the risk-adjusted mortality rates of hospitals. This study used 2002 National Health Insurance claims data for tertiary hospitals in Korea. The study sample consisted of 1,959 patients from 23 tertiary hospitals. The risk-adjustment model used the patients' biological, admission, and comorbidity data identified in the claims. The subjects were classified into high- and low-risk groups based on predicted surgical risk. The crude mortality rates and risk-adjusted mortality rates for low-risk, high-risk, and all patients in a hospital were compared based on the rank and the four intervals defined by quartile. Also, the crude mortality rates of the three groups were compared with their 95% confidence intervals of predicted mortality rates. The C-statistic (0.83) and Hosmer-Lemeshow test ($X^2$=11.47, p=0.18) indicated that the risk-adjustment model performed well. Presenting crude mortality rates with their 95% confidence intervals of predicted rates showed higher agreements among the three groups than using the rank or intervals of mortality rates defined by quartile in the hospital performance assessment. The crude mortality rates for the low-risk patients in 21 of the 23 hospitals were located on the same side of their 95% confidence intervals compared to that for all patients. High-risk patients and all patients differed at only one hospital. In conclusion, the impact of risk selection by hospital on the assessment results was the smallest when comparing the crude inpatient mortality rates of CABG patients with the 95% confidence intervals of predicted mortality rates. Given the increasing importance of quality improvements in Korean health policy, it will be necessary to use the appropriate method of releasing the hospital performance data to the public to minimize any unwanted impact such as risk-based hospital selection.

• Korea Journal of Hospital Management
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• v.19 no.4
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• pp.21-33
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• 2014

This study was conducted to propose an insight into the appropriateness of hospital length of stay(LOS) by developing a severity-adjusted LOS model for patients with pneumonia, organism unspecified. The pneumonia risk-adjustment model developed in this paper is based upon the 2006-2010 the Korean National Hospital Discharge in-depth Injury Survey. Decision tree analysis revealed that age, admission type, insurance type, and the presence of additional disorders(pleural effusion, respiratory failure, sepsis, congestive heart failure etc.) were major factors affecting the severity-adjusted model using the Clinical Classifications Software(CCS). Also there was a difference in LOS among the regional hospitals, especially the hospital LOS has not been efficiently managed in Gyeongsangbuk-do, Jeollanam-do, Jeollabuk-do, Daejeon, and Busan. To appropriately manage hospital LOS, reliable statistical information about severity-adjusted LOS should be generated on a national level to make sure that hospitals voluntarily reduce excessive LOS and manage main causes of delayed discharge.