• Korea Journal of Hospital Management
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• v.16 no.3
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• pp.1-18
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• 2011

In this study, population census(2005 & 2008) from Statistics Korea and the statistical data of the number of hospital beds by healthcare facilities classification from Ministry of Health and Welfare were used. For analyzing distribution of hospital beds, hospital beds were classified as acute care beds, long-term care beds and all hospital beds, which is including acute and long-term care beds. Regional areas, which are city(si), county(goon) for the study and district(gu) were reclassified as metropolitan city, city(si) and county(goon). Because there were 165 regional areas in 2005 and 2008, 84 and 81 areas were classified as metropolitan city and/or city and county, respectively. Gini index were calculated for hospital beds from each year, and Lorenz curves were drawn. The following summary presents the findings of this study. Compared to the year 2005 and 2008, the Gini index was 0.24472, and hospital bed numbers increased slightly by 0.80% than in 2005. In case of acute care beds, the Gini index was 0.23797(0.13%), and there was no big difference; however, the Gini index for long-term care beds was 0.41091, and there was a 30.25% decrease, which shows improvement to reduce disparities. It might result from an increase in long-term care beds up to 476.2%. For geographical equality of hospital beds, the Gini index and Lorenz curve, which can be compared the degree of inequality in the distribution of hospital beds reasonably and possibly show statistical data, should be used. Through this study, the distribution policy of hospital beds should be established.

• Korea Journal of Hospital Management
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• v.13 no.4
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• pp.1-26
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• 2008

This study examines the statistical relationship between medical specialists and managerial performance, using regression analysis with the number of medical specialists per 100 beds as the independent variable and the managerial performance index as the dependent variable. Managerial performance index incorporated the number of out-patients per specialist, the number of in-patients per specialist, the volume of revenue per specialist, the number of beds per specialist, and the average length of stay. To compare different groups of hospitals, dummy variable was applied to five groups of hospitals according to size: 100-299 beds, 300-599 beds, 600-899 beds, 900-1199 beds, and more than 1200 beds. The data consisted of 181 general hospitals with more than 100 beds, which included 28 public hospitals, 73 corporate hospitals, 64 university hospitals and 16 private hospitals. Of those, 87 hospitals were located in big cities and 94 hospitals in medium to small cities. This study used hospitals from the Korean Hospital Association, and data published in 2004. The collected data sample was analyzed using the SPSSWIN 12.0 version, and the study hypothesis was tested using regression analysis. The findings of this study are summarized as follows: Hypothesis 1 predicting a negative effect of the number of medical specialists on the number of out-patients per specialist was supported with statistical significance. The analysis of dummy variable showed causality in all the hospital groups larger than the group of 100-299 beds. Hypothesis 2 predicting a negative effect of the number of medical specialists on the number of in-patients per specialist was supported with statistical significance. The analysis of dummy variable showed causality in the hospital group of 300-599 beds when compared to the group of 100-299 beds. Hypothesis 3 predicting a negative effect of the number of medical specialists on the volume of revenue per specialist was not supported. However, the analysis of dummy variable showed that the volume of revenue per specialist increased in the hospital groups of 600-899 beds, 900-1199 beds, and over 1200 beds, when compared to the group of 100-299 beds. Hypothesis 4 predicting a negative effect of the number of medical specialists on the average length of stay was supported with statistical significance. The analysis of dummy variable showed causality in the hospital group of 300-599 beds, when compared to the group of 100-299 beds. Results of this study show that the number of the medical specialists per 100 beds is an important factor in hospital managerial performance. Most hospitals have tried to retain as many medical specialists as possible to keep the number of patients stable, to ensure adequate revenue, and to maintain efficient managerial performance. Especially, the big hospitals with greater number of beds and medical specialists have shown greater revenue per medical specialist despite the smaller number of patients per medical specialist. Findings of this study explains why hospitals in Korea are getting bigger.

• Journal of Preventive Medicine and Public Health
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• v.20 no.1 s.21
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• pp.56-66
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• 1987

Information on productivity of hospital personnel is required for optimum staffing and hospital management. This study deals with the quantitative aspects of workload of medical personnel in training hospitals by their specific characteristics. Specifically this study attempted to find relevant determinants of the productivity of medical personnel using multiple stepwise regression analysis based on data obtained from 135 training hospitals. The findings of this study were as follows: 1) Daily average number of outpatients and inpatients treated by a physician were 20.4 and 10.2, respectively. 2) Daily average number of patients cared by a nurse was 8.2. Daily average number of tests performed by pathologic technician and radiologic technician were 83.2 and 21.5, respectively. 3) Productivity of medical personnel were significantly different for the three groups of factors: hospital sire (number of beds, number of medical personnel per 100 beds): institutional characteristics (medical school affiliation, training type, profit status); and environmental factors (location, number of physician and beds per 1,000 population in the region). 4) The factors a(footing the productivity varied according to the types of medical profession: the number if beds, the number of physicians per 100 beds, training type, and profit status for physicians; the number of nurses per 100 beds, the number of beds, medical school affiliation for nurses; the number of physicians per 100 beds, the number of technicians per 100 beds, and ownership for pathologic technicians; the number o( technicians, training type, and the number of physicians per 100 beds for radiologic technician.

• Nuclear Engineering and Technology
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• v.50 no.6
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• pp.842-848
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• 2018

Motivated by reducing the uncertainties in quantification of debris bed coolability, this paper reports an experimental study on two-phase flow resistances and interfacial drag in packed porous beds. The experiments are performed on the DEBECO-LT (DEbris BEd COolability-Low Temperature) test facility which is constructed to investigate the adiabatic single and two phase flow in porous beds. The pressure drops are measured when air-water two phase flow passes through the porous beds packed with different size particles, and the effects of interfacial drag are studied especially. The results show that, for two phase flow through the beds packed with small size particles such as 1.5 mm and 2 mm spheres, the contribution of interfacial drag to the pressure drops is weak and ignorable, while the significant effects are conducted on the pressure drops of the beds with bigger size particles like 3 mm and 6 mm spheres, where the interfacial drag in beds with larger particles will result in a descent-ascent tendency in the pressure drop curves along with the fluid velocity, and the effect of interfacial drag should be considered in the debris coolability analysis models for beds with bigger size particles.

• The Journal of Fisheries Business Administration
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• v.47 no.3
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• pp.53-69
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• 2016

The purpose of this study is to explore the ecosystem service and benefit indicators of natural seaweed beds. Ecosystems of natural seaweed beds provide a wide range of services and benefits to human society including provisioning services, regulating services, supporting services, and cultural services. Indicators for each of the ecosystem services are chosen by marine plants ecologists and as follows. Ecosystem indicators of natural seaweed beds for provisioning services are well-being food(amount of seaweed harvested/amount of fish landed, fish biomass, area of natural seaweed beds, the number of species, contribution to the second production), raw materials(amount of biomass by breed, amount of aquaculture feed), genetic resources(amount of genetic material extracted, amount of genetic material contained by age and habitat), and medicinal resources(amount of medicinal material extracted). Ecosystem indicators of natural seaweed beds for regulating services are air purification(amount of fine dust/NOx or $SO_2$ captured), climate regulation(amount of $CO_2$ sequestered), waste treatment(amount of N, P stored, biochemical degradation capacity COD), and costal erosion prevention(length and change of natural coast line, amount of sediment prevented). Ecosystem indicators of natural seaweed beds for supporting services are lifecycle and maintenance(primary production, contribution to the second production) and gene pool protection(amount of compositional factors in ecosystem, introduced species). Ecosystem indicators of natural seaweed beds for cultural services are recreation and tourism(the number of visits of an area) and information for cognitive development(amount of time spent in education, research and individual learning about ecosystem of natural seaweed beds).

• Korea Journal of Hospital Management
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• v.4 no.1
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• pp.41-65
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• 1999

This study was attempted to identify the factors affecting profitability of general hospital in Kyung-In Region. Operating profit to gross revenues and net profit to gross revenues were used as a proxy indicator for profitability of hospitals. The unit of analysis was hospital, and the data were collected 5 years data from 20 hospitals. The major findings are as follows; (1) The average operating profit rate was 1.03% and the net profit rate was -5.00% in twenty hospitals in the Kyung-In Region for the last five years. In terms of maximum surplus, the operating profit rate was 14% and net profit rate was 3.40%. In terms of maximum loss revenue, the operating profit rate was -16.56% and the net profit rate was -22.83%. (2) Since the year 1993, which was the starting year of this study, the operating profits and the net profits consistently decreased. (3) Analyzing the difference in profits among various hospital groups, the tertiary hospital group and the 501-1000 beds group exhibited the highest in operating profit rate. Also, among the higher grade number of beds in hospital group, per 100 beds group, the 41-50 beds group exhibited the highest in operating profit rate. There is a statistically significant difference in those groups(p<0.05, p<0.01). (4) In the health care delivery system, the profit gain in the secondary hospital was 51.5% and in the tertiary hospital was 72.4%. Based on the number of beds in each hospital group, the highest profit gain was 75.0% in the over 1001 beds group, and 71.4% in the 501-1000 beds group. Also, among the higher grade number of beds in hospital group, per 100 beds group, the 41-50 beds group exhibited 88.6% surplus. (5) According to the surplus difference based on the analysis of health care utilization, a group with over 31 patients in bed turnover rate, a group with over 96% in bed occupancy rate and group with over 9% in emergency cases to outpatient visits exhibited the highest profit gains. In addition, a group with over 301 patients in daily outpatient visits per 100 beds and group with 11-12 days average length of stay exhibited the highest profit gains. These results are statistically significant(p<0.05, p<0.01). (6) According to a stepwise regression analysis, the variables measuring the bed turnover rate, number of licensed beds, and number of outpatient visits per specialist explain 34.1% of the variation in operating profits. In terms of net profits, the new outpatient visits, the bed turnover rates and the number of general bed variables explain 30.6%. These results are statistically significant(p<0.01).

• Journal of The Korea Institute of Healthcare Architecture
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• v.23 no.2
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• pp.27-35
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• 2017

Purpose: This study analyzed the status of general hospitals as an expanded concept of medical resources including medical staff and equipment. The purpose of this study is to provide a basic for the feasibility study of the scale and establishment of facility guidelines at the planning stage of general hospitals. Methods: The subjects of this study were limited to general hospitals. The status of medical resources was based on the data of the Health Insurance Review and Assessment Service. The number of beds, doctors, nursing grades and major medical equipment were surveyed in 335 general hospitals. Results: 1) The characteristic of general hospitals varies depending on the number of inpatient beds. To be concrete, there were differences in the number of medical staffs and equipments in general hospitals based on 300 500 800 1,000 beds. 2) As the number of hospital beds increases, the number of medical staff increases more than medical equipment and facilities. Medical equipment and facilities remain constant, even when the number of beds increases. On the other hand, the number of medical staff increased about 1.5 times in each level. Implications: Architectural plans for medical staff should be considered differently depending on the number of beds. In particular, architectural planning and facility guidelines should be applied differently based on 300 and 500 beds.

• Health Policy and Management
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• v.4 no.1
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• pp.107-122
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• 1994

Many alternatives have been discussed to reduce the medical expenditure and to use the medical resources effectively. Many studies about the economies of scale have been done for the last several decades. This study has analyzed the relationship between the number of beds and the mean expense per hospitalization day in Korea. A Cost Function Model was identified and we wanted to see the minimum optimal size with the cheapest mean expense per hospitalization day. The result is as follows; 1. In the Cost Function Mode, (the number of beds)$^{2}$, the number of personnel, productivity and training institutions are the factors that statisticaly influence the mean expenses. 2. By the univariate analysis the mean expense proved to be the smallest as the level of 150-200bed, The breaked down of the components of expenses shows that the mean labor cost is much different from the mean value of material and administration costs, and that hospital with 150-200 beds also have the minimal expense. The mean expense goes up dramatically in hospitals of 450 beds or more. 3. When the other conditions are constant, according to the multiple regression analysis of the mean expense per adjusted hospitalization day the minimum optimal size with the cheapest expense is a hospital with 191 beds and the hospital with 230 beds takes the lowest mean labor cost. The material or administration costs are not influenced by hospital size. This research has limitation in measuring the variables that influence hospital xpenses, in estimating hospital output by the number of beds in considering outpatient cost and in securing representativeness of hospitals because many hospitals made no responses to the research questionnare. But it is valuable and helpful for development of health policy to figure out the number of beds with the cheapest expense per hospitalization day.

• Quality Improvement in Health Care
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• v.12 no.1
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• pp.62-76
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• 2006

Background : There were so many patients who are waiting for admission in Emergency room in spite of more than one hundred empty beds everyday. This study was conducted to evaluate admission-discharge module system by OCS which reduce empty beds. Methods : The data of bed utilization in general beds from 2004 were reviewed. For evaluation of performance at admission-discharge module system by OCS, the change of Occupancy of bed were calculated. Results : The percentage of Average Bed Emptiness was changed from 13.8% to 9.2%. The residents in surgery(100%) and in internal medicine(75.5%) approved this system. Conclusion : The personnel in hospital recognized that it was very important to manage bed. The management of beds by OCS was helpful to reduce empty beds and was important.

• Journal of the Korea Furniture Society
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• v.17 no.3
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• pp.45-55
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• 2006

The change of consumers' lifestyles causes frequent study on beds along with growth of economy, improvement of education and increase of dwelling in new towns and nuclear family. Beds are frequently developed in recent years since consumers' lifestyles increasingly change caused by transformation of types of dwelling, influence of western lifestyle, and increase uses of a bed. Also, this transformation causes needs for research on bed designs. A Bed is an essential item in a life since people spend one third of a day in Also, in contemporary, the ages of consumers are varied from infants to seniors. The study examines the importance of beds which playa major role in households. Moreover, the purpose of this study of beds focused on surface materials, colors, and designs is suggesting bases for future developments.