• 한국병원경영학회지
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• 제11권4호
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• pp.19-36
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• 2006

This study aims to examine the influence of growth rate, profitability and current ratio, which are confronted with static trade-off theory and pecking order theory, on capital structure of superior hospital and bankrupt hospital. Firstly, superior hospitals show positive correlation between growth rate and short-term loans, long-term loans, and short-term liabilities while bankrupt hospitals represent negative correlation. Superiority hospital and bankruptcy hospital show different financing behaviors, especially, short-term loan is the significant characteristic that discriminates between superior hospital and bankrupt hospital. Secondly, this paper studied the correlation between profitability and short-term loan, which the superior hospitals shows negative correlation, to contrast, bankrupt hospital have positive correlation. Consequently, the short-term loan is the most distinguishable factor between the superior hospital and bankrupt hospitals in terms of profitability. To conclude, this study shows that excess short-term loans can be the most important cause for hospital's bankrupt. Accordingly, strategic and effective policy about the short-term loan will be required in order to protect hospital's bankrupt.

• 한국병원경영학회지
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• 제4권2호
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• pp.219-241
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• 1999

This study used the Data Envelopment Analysis, a mathematical linear programming method, to evaluate cost efficiency of hospitals in Korea. DEA method was applied to 244 hospitals: 31 bankrupt hospitals and 213 survived hospitals. Among the 213 sound hospitals, 11 hospitals showed efficiency score 100, but more than 40 hospitals recorded efficiency scores lower than 60. This result implies that more hospitals can be bankrupt in the restructuring process of the industry within 1-2 years. Among the 31 bankrupt hospitals, the highest technical efficiency score was 0.821 and 11 hospitals showed technical efficiency lower than 0.6. This implies that selective financial support based on cost efficiency by the government will be valuable to prevent bankruptcy of these hospitals. The logistic analysis showed statistically significant relationship between bankruptcy and efficiency of hospitals in Korea.

• 보건행정학회지
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• 제12권3호
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• pp.54-76
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• 2002

This study investigated how much EVA which evaluate firm's value can explain hospital bankruptcy prediction as a explanatory variable including financial indicators in Korea. In this study, artificial neural network and logit regression which are traditional statistical were used as the model for bankruptcy prediction. Data used in this study were financial and economic value added indicators of 34 bankrupt and -:4 non-bankrupt hospitals from the Database of Korean Health Industry Development Institute. The main results of this study were as follows: First, there was a significant difference between the financial variable model including EVA and the financial variable model excluding EVA in pre-bankruptcy analysis. Second, EVA could forecast bankruptcy hospitals up to 83% by the logistic analysis. Third, the EVA model outperformed the financial model in terms of the predictive power of hospital bankruptcy. Fourth, The predictive power of neural network model of hospital bankruptcy was more powerful than the legit model. After all the result of this study will be useful to future study on EVA to evaluate bankruptcy hospitals forecast.

• 보건행정학회지
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• 제9권2호
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• pp.1-20
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• 1999

This study was conducted to investigate the predictors of hospital bankruptcy in Korea and to examine the predictive power for 3 types of statistical models of hospital bankruptcy. Data on 17 financial and 4 non-financial indicators of 30 bankrupt and 30 profitable hospitals in 1. 2, and 3 years before bankruptcy were obtained from the hospital performance databank of Korea Institute of Health Services Management. Significant variables were identified through mean comparison of each indicator between bankrupt and profitable hospitals, and the predictive power of statistical models of hospital bankruptcy were compared. The major findings are as follows. 1. Nine out of 21 indicators - fixed ratio, quick ratio, operating profit to total assets, operating profit to gross revenue, normal profit to total assets,normal profit to gross revenue, net profit to gross revenue, inventories turnrounds, and added value per adjusted patient - were found to be significantly predictitive variables in Logit and Probit models. 2. The predicdtive power of discriminant model of hospital bankruptcy in 1. 2, and 3 years before bankruptcy were 85.4, 79.0, and 83.8% respectively. With regard to the predictive power of the Logit model of hospital bankruptcy, they were 82.3, 75.8, and 80.6% respectively, and of the Probit model. 87.1. 80.6, and 88.7% respectively. 3. The predictive power of the Probit model of hospital bankruptcy is better than the other two predictive models.

• 보건행정학회지
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• 제15권4호
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• pp.81-109
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• 2005

According to many recent studies suggesting that cash flow analysis method tends to be more effective than traditional financial index analysis method to predict corporate bankruptcy, this study applies the cash flow analysis method to hospital business to identify the significant variables which can distinguish between superior hospitals and bankruptcy hospitals. The author analyzed recent 3 years, i.e. from the year of 2000 to the year of 2002, financial statements of 31 bankrupt hospitals In 2003, and the same number of superior hospitals through using Multiple Discriminant Analysis and Logit Analysis. The results are belows; First, the study releases that Logit Analysis is more likely to be effective than Multiple Discriminant Analysis. Second, this research also shows that traditional financial index analysis method is more superior compare to cash flow analysis method for hospital bankruptcy predict model. Finally, this study suggest that the significant variables, which can distinguish superior hospitals from bankrupt hospitals, are Operating/Current Liabilities$(Y_2)$, CFO/Equity$(Y_5)$ for cash flow analysis method and Net Worth to Total Assets Ratio$(X_1)$, Quick Ratio $(X_3)$, Return on Assets$(X_6)$, Growth Rate of Patient Revenues$(X_{16})$ for traditional financial index analysis method.

• 한국병원경영학회지
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• 제6권2호
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• pp.22-36
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• 2001

The hospital bankruptcy rate is increasing, therefore it is very important to predict the bankruptcy using the existing hospital management information. The hospital bankruptcy is often measured in year intervals, called grouped duration data, not by the continuous time elapsed to the bankruptcy. This study introduces a hierarchical generalized linear model(HGLM) for analysis of hospital bankruptcy data. The hazard function for each hospital may be influenced by unobservable latent variables, and these unknown variables are usually termed as random effects or frailties which explain correlations among repeated measures of the same hospital and describe individual heterogeneities of hospitals. Practically, the data of twenty bankrupt and sixty profitable hospitals were collected for five years, and were fitted to HGLM. The results were compared with those of the logit model. While the logit model resulted only in the effects of explanatory variables on the bankruptcy status at specific period, the HGLM showed variables with significant effects over all observed years. It is concluded that the HGLM with a fixed ratio and a period of total asset turnrounds was justified, and could find significant within and between hospital variations.

• Journal of Preventive Medicine and Public Health
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• 제31권3호
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• pp.490-502
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• 1998

본 연구는 우리 나라 병원도산 예측모형을 도출하기 위한 연구로 1992년에서 1997년 사이 5년간의 전국 병원 경영통계 자료를 이용하여 1995년부터 1997년 사이에 도산한 병원중도산전 3년까지의 연속된 자료가 있는 31개 병원을, 비교군 병원은 도산병원과 유사한 병상규모를 가지고 당기순이익이 발생한 31개 우량병원을 선정하여 단계적 판별분석에 의한 실증연구를 시행하였다. 본 연구의 구체적 연구결과는 다음과 같다. 첫째, 도산전 각 연도별로 도산병원과 우량병원간에 연구변수의 단순 평균치분석 결과, 자본구조 지표인 자기자본비율과 수익성지표인 총자본의료이익을, 의료수익의료이익을, 총자본경상이익을, 의료수익경상이익율, 총자본순이익을 등은 도산 1, 2, 3년전 모두에서 도산병원과 우량병원간에 유의한 차이를 보였다. 자본고정성지표는 도산 1년전에 고정비율이 유의한 차이를 보였고, 유동성지표는 도산 1년전에는 유동비율과 당좌비율이 유의한 차이를 보였고 도산 2년전에는 당좌비율만이 유의한 차이를 보였다. 활동성지표로는 도산 1년전에 총자본회전율과 재고자산회전율이 유의한 차이를 보였고 도산 2년전에는 총자본회전율과 의료미수금회전율이, 도산 3년전에는 의료미수금회전율만이 유의한 차이를 보였다. 생산성지표로는 도산 2년전에 총자본투자효율이, 도산 3년전에는 조정환자1인당 부가가치가 유의한 차이를 보였다. 진료실적지표로는 도산 3년전 일평균재원환자수가 유의한 차이를 보였다. 둘째, 도산 1, 2, 3년전 판별함수는 각각 도산 1년전 Z=($0.0166\times$당좌비율)-($0.1356\times$총자본경상이익을)-($1.545\times$총자본회전을), 도산 2년전 Z=($0.0119\times$당좌비율)-($0.1433\times$총자본의료이익율)-($0.0227\times$총자본투자효율), 도산 3년전 Z=($0.3533\times$총자본순이익율)-($0.1336\times$의료미수금회전율)-($0.04301\times$조정환자1인당부가가치)+($0.000119\times$일평균재원환자수)이었다. 셋째, 도출된 도산 1, 2, 3년전 각 판별함수의 예측력은 77.42%, 79.03%, 82.25% 이었다.