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JPMPH : Journal of Preventive Medicine and Public Health

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Kwang Soo Lee 3 Articles
The Trend of Risk-adjusted Hospital Mortality Rates of Coronary Artery Bypass Graft Patients from 2001 to 2003.
Kwang Soo Lee
J Prev Med Public Health. 2007;40(1):29-35.
DOI: https://doi.org/10.3961/jpmph.2007.40.1.29
  • 2,972 View
  • 23 Download
AbstractAbstract PDF
OBJECTIVES
To assess whether the risk-adjusted inhospital mortality rates for non-emergent and isolated coronary artery bypass graft surgery (CABG) patients exhibited a consistent trend from 2001 to 2003. METHODS: The data used in this study came from CABG claims that were submitted to a Korean Health Insurance Review Agency (HIRA) in 2001, 2002, and 2003. Study datasets included data from 17 tertiary hospitals, which had at least 25 claims each year over 3 years. The interhospital differences in patients' risk-factors were identified and controlled in the risk-adjustment model. Actual and predicted mortality rates for each hospital were calculated in 2001, 2002, 2003, and 2001+2002, and were then examined to identify consistent rate patterns over time. Kappa analysis was applied to assess the agreements between rates. RESULTS: Hospitals with lower-than-expected inpatient mortality rates showed more consistent rates than those with higher-than-expected mortality rates. The mortality rates that were calculated based on data obtained over multiple years had less variation among hospitals than rates based on single year data. Based on the Kappa score, the highest agreement was found when the rates were compared between the 2-year combined data (2001+2002) and 2003. CONCLUSIONS: Consistent patterns over 3 years were most evident for hospitals which had lower-than expected mortality rates. Policy makers can use this information to identify the degree of outcomes in hospitals and help motivate or channel the behaviors of providers.
Summary
Does a Higher Coronary Artery Bypass Graft Surgery Volume Always have a Low In-hospital Mortality Rate in Korea?.
Kwang Soo Lee, Sang Il Lee
J Prev Med Public Health. 2006;39(1):13-20.
  • 2,102 View
  • 30 Download
AbstractAbstract PDF
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 (x2=6.92, p=0.55) showed that the model's performance was good. A total of 30 low-volume hospitals (971patients) and 4 high-volume hospitals (1,087patients) were identified. Significantdifferences 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.
Summary
Impact of Risk Adjustment with Insurance Claims Data on Cesarean Delivery Rates of Healthcare Organizations in Korea.
Kwang Soo Lee, Sang Il Lee, Kyung Seo, Young Mi Do
J Prev Med Public Health. 2005;38(2):132-140.
  • 1,935 View
  • 31 Download
AbstractAbstract PDF
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 riskadjustment model, and were found to have statistically significant effects on the mode of delivery. The c-statistic (0.78) and Hosmer-Lemeshow test (chi2=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 chi2. 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.
Summary

JPMPH : Journal of Preventive Medicine and Public Health