| 1. Research purposes and significanceIn mind the existing expansion of beds in general hospital, the economies of scale in efficiency of county general hospital are measured to clarify the existence of economies of scale in general hospitals, and the impacts of hospital size were explored. This Study is important:on the one hand from a theoretical point of view, it help to provide theoretical support for of the hospital's development; the other hand; from a practical point of view, the scale factors research could provide direct policy and references for the health administration in the health planning and supervising for hospitals.2. Research objectives and content2.1 The present status of general hospital beds was decrypted by statistical analysis to explore the characteristics and trends of the scale of Chinese general hospital.2.2 The correlation between of bed size and financial risk of general hospital was quantitatively analyzed to reveal the potential risk for the scale expansion of hospital.2.3 An empirical study of efficiency by DEA analysis with different sizes hospitals was conducted to test the significant differences of scale between them.2.4 The qualitative method and quantitative measurement were employed to certain the internal and external factors of general hospital size, their weights and their relationships.2.5 On the basis of the empirical study, one theory mode was put forward to explain the supply behaviors of general hospital, and the countermeasures were proposed to achieve economies of scale in the development of hospital.3 Research programs and methods3.1 Sampling and CasesThe typical investigation and the cluster sampling were employed to determine the study sample. As the research cases, all 61 county People's Hospital in Hubei province(including county-level city) was divided into five groups(small, medium and small scale, medium scale, medium scale, large-scale group) according to the bed scale.3.2 Data source(1) Literature method:Use secondary sources to collect research data (including the external factor indicators, the data years are 2004,2005 and 2006 (Panel Data). In order to ensure the accuracy and authenticity, the data are mainly from official statistics.(2) Field survey:Collect the panel data and information of 2004,2005 and 2006 including basic situation, the internal factors indicators through field investigation.(3) Delphi expert consultation:The nationwide 15 experts were selected to develop the system of influencing factors for the bed scale of general hospital.(4) Insider interviews:The sociology of insider interviews and other investigative methods were used to gather potential information for the study.3.3 Survey and measurement tools(1) Questionnaire for Basic information of hospital;(2) Questionnaire for the external environment of hospital;(3) Expert consultancy List of factors for scale of Hospital;(4) Questionnaire for supply behavior of medical services in hospital;3.4 Data analysis(1) The literature content analysis and secondary analysis were used to summarize the theory of economies of scale in hospital(2) The descriptive statistical analysis was employed to describe the tendency of beds scale in general hospital for exploring its characteristics and tendency in software SPSS (V15).(3) On the base of quantitative measurement of hospital financial risk by factor analysis, the regression method was used to the correlation between hospital beds and financial risk in software SPSS (V15).(4) The empirical study of the scale efficiency in general hospital:first, the use of literature-optimum-seeking to establish an index system of efficiency measurement; second, using data envelopment analysis (DEA) of the relative efficiency to calculate the efficiency score; and finally, the analysis of variance was used to test the significance different among different groups. This was finished in DEAP (V2.1) and SPSS (V15).(5) The study of factors affecting hospital scale:on the one hand, the method of Delphi was employed to establish the influencing factors system for bed scale of the hospital to determent the factors dimensions and their weights; the other hand, with the hospital beds as the dependent variable, the internal and external factors as measured variables, the structural equation modeling (SEM) of scale factors was built to quantitatively descript the main factors, their degree and their relationship. The application tool is AMOS (V7.0).4. Results4.1 The overall level of hospital beds per capital is equivalent to the world average, but the average number of beds in general hospital was significantly higher than the world average. By 2008, the number of hospitals over 800 beds to 418 and the average number of beds at County General Hospital was also higher than the hospital of other countries4.2 The relation between the financial status and beds size of County General Hospital could be estimated with cubic regression curve, and the regression equation is: Y=-1.19×10-7×X3+0.046×X-4.8674.3 The findings of DEA efficiency analysis in hospitals:(1) Hospital efficiency increased as a growing trend with the scale of beds, but when the scale reached a certain size, the efficiency started to decline.(2) Based on the scores of DEA results and combined with regression equation between the hospital size and financial risk, the range of optimal beds size has been determined as 250 to 300 in county general hospital.(3) The majority of large general hospitals at county level (greater than 335 beds) in a state of decreasing returns to scale, while the vast majority of small hospitals (less than 200 beds) showed increasing returns to scale status.(4) DEA input and output projection analysis showed that county general hospital beds and staff in are actual surplus, especially in large-scale hospital surplus as high as 22.7%, the average can be reduced by 97 beds.4.4 The factors analysis of Hospital-scale by qualitative methodThe factors of Hospital-scale include 5 first-level factors and 30 second-level factors. The first-level factors and their impact weights are: hospital internal factors (22.03%), regional economic level (20.53%), health resources (19.77%), social status (19.02%) and the competitive environment (19.02%). The top 10 indicators of second-level factors and their weights are: the region's total population, the hospital leadership decision-making type, per capita disposable income of urban residents, per capita net income of rural residents, hospitals per capita income, the ratio of urban population, the total expenditure of rural residents per capita cost of living, the proportion of the whole society practitioners and regional health planning implementation.4.5 The factors analysis of Hospital-scale by Quantitative study(1) The demographic and economy situation in the region is the most important factors for beds size, and its standardized regression coefficient was 0.477. In addition, its representative indicator is the net income of rural residents.(2) Hospital performance is also a major factor for scale, and its regression coefficient was 0.324. The per capita revenue and per capita expenditure has common effects on hospital performance.(3) The hospital's competitive environment is the moderate factors for hospital beds, which shows the competition is a factor but not yet play an important influence to the scale of the hospital scale.(4) As the stock level of health resources on the scale of the development of county general hospital, there is lesser degree of negative impact, that is, with the allocation of health resources within the region raise the level of beds decreased slightly.(5) There are close relationships among the ratio of inpatient per 100-outpatient, occupancy rate of beds and hospital income per capita. The ratio of inpatient per 100-outpatient can be used as a sensitive indicator to judge the existence and the degree of induced demand in hospital.(6) 93.3%of the consulting experts believe that there exists the phenomenon of supplier-induced demand in China's non-profit hospitals, which is very serious and more serious that the proportions were 28.6%and 64.3%respectively, and the cumulative percentage of them reached 92.9%; the proportion of model that can explain the supply behavior of non-profit hospitals are:the model of hospital profit maximization (42.86%), two groups (28.57%), and social utility maximization only (14.29%).5. Conclusions5.1 The beds size of general hospital in China at different levels significantly exceeded the average level of the world's major countries, and the explosion has continued further.5.2 There is nonlinear parabola regression relationship between financial status and open beds of county general hospital of Hubei Province. Therefore, from a financial point of view to reduce the risk, have to control the size of county-level hospital.5.3 The economies of scale has been confirmed in county general hospital of Hubei Province:the efficiency of hospital increased with the beds growing trend, but reached a certain scale, the overall efficiency and scale efficiency began to decline.5.4 The overall size of county surplus of hospital beds is redundant and the optimal size is in the range of 250-3005.5 The most important factor foe beds scale of county general hospital include three aspects:the hospital internal factors, the level of regional economic and social conditions. The market competition is a moderate factor but can not yet a significant impact, which shows the county people's hospital is in leading and monopoly status in local health care market. The stock levels of health resource has little impact and hinted that the development of county hospitals do not take account of local health resources stock levels.5.5 The intrinsic theoretical explanation for the redundant in scale of beds and the serious induce demand in China's general hospital is:hospital in pursuit of revenue maximization, utilization rate of bed increasing by inducing demand, hospital decision-makers further expand the scale of bed based on the reason and illusion of bed occupying rate, and the induced demand behavior is more serious, thus a vicious circle.5.6 The following policy measures were recommended to curb the scale expansion of China's general hospital t:(1) establish the hospital's reasonable compensation mechanism to eliminate its profit motive; (2) cancel the hospital's independent right of sharing economic benefits; (3) strict medical institutions planning to set "hard" constraints; (4) improve the internal decision-making and management of hospitals; (5) establish medical information disclosure system to increase the cost of hospital's behaviour of induce demand; (6) reform of health care delivery to promote non-profit hospital management pattern.6. Innovations and merits6.1 Theoretical InnovationThe empirical research with all 61 county general hospital of Hubei province (including county-level city) confirms the existence of economies of scale in general hospital, initially clarifies whether there are economies of scale in Chinese hospitals, which is an important theoretical issue. The study also explored factors that influence the size of the hospital from both qualitative and quantitative research, explained the motivation for the expansion of hospital size, and further developed an explain model theory for supply behavior of medical services in China.6.2 Innovation of methodsFrom an interdisciplinary perspective, the multi-disciplinary methods, such as management, economics, sociology, accounting, econometrics and statistical method, are employed to research the economies of scale in hospital and its impact factors. Particularly, the comprehensive utilization of methods with qualitative Delphi method and quantitative structural equation model was used to exploring the factors influencing the size of bed. This method overcomes the limitations of the single methods, the results can be verified and mutual complemented, and then the conclusions are more credible.6.3 The merits of researchFrom the theoretical point of view, this study confirmed the existing of economies of scale in hospital bed, not only providing theoretical support for the development of hospital to avoid blind expansion, but also helping the Government provide health care resource allocation decisions to avoid the waste of health resources. From the application point of view, the quantitative results and the conclusions of this study, including nonlinear regression equation between hospital size and financial risks, the Delphi system and structural equation models of scale factors, can be used as basis of decision making for hospital administrators and health administrative departments7. Research limitations7.1 There is not sufficient evidence to proof the existence of economies of scale in hospital, for this study is under the background of county general hospital in Hubei Province. The future research cases need to be expanded to the provincial, municipal general hospitals for confirming the existence of economy of scale in China general hospitals.7.2 The factors for bed scale of hospital in this study is not yet complete, so in future research should focus on more potential factor to explore the impacts of hospital scale.
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