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Objective:To construct an evaluation indicator system for the efficiency of nursing human resources in integrated medical and elderly care institutions using Data Envelopment Analysis (DEA) and subsequently evaluate its effectiveness.Methods:This cross-sectional survey utilized literature review and investigative methods to initially establish a library of evaluation indicators for nursing human resource efficiency. The Delphi method was employed in two rounds of consultations with 17 experts from various fields, including nursing management, elderly care institution management, integrated medical and elderly care institution management, health economics management, and public health. The reliability of the indicator system was assessed based on factors such as expert enthusiasm, authority, concentration of opinions, and coordination. Adjustments, modifications, and improvements were made to the indicators based on expert opinions to establish the final indicator system. From August to December 2022, the DEA model was applied to evaluate the efficiency of 12 integrated medical and elderly care institutions in Haikou city based on this indicator system.Results:The constructed evaluation indicator system comprised 68 items divided into three levels: 9 primary indicators, 19 secondary indicators, and 40 tertiary indicators. The positive coefficients of the two rounds of expert consultations were 100% and 94.1%, with authority coefficients of 0.88 and 0.92, Kendall harmony coefficients of 0.471 and 0.348, and mean coefficients of variation of 0.16 and 0.12 ( P<0.001). DEA evaluation results for the 12 integrated medical and elderly care institutions showed that 5 were DEA effective institutions with comprehensive efficiency (OE), technical efficiency (TE), and scale efficiency (SE) values all equal to 1.000, while 7 were non-DEA effective institutions, including 4 with SE <1.000 but TE=1.000 and 3 with both SE and TE<1.000. Conclusions:The constructed evaluation indicator system demonstrates high enthusiasm, authority coefficients, and coordination in expert consultations, indicating high acceptability and comprehensive content with distinct levels and strong specialty characteristics. The DEA model′s evaluation results objectively and effectively reflect the efficiency of nursing human resources in integrated medical and elderly care institutions, demonstrating practical utility.
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Objective:To analyze the resilience level and influencing factors of tertiary public general hospitals in Hunan province under the background of major emergencies, so as to provide reference for the construction of resilient hospital and improvement of emergency response ability.Methods:Fifty tertiary public general hospitals in Hunan province that participated in the performance evaluation of national tertiary public hospitals were selected as research samples. The data was sourced from the performance evaluation management platform of public hospitals from 2019 to 2021. The DEA-Malmquist model was used to analyze the static and dynamic efficiency, hospital resilience index model was constructed based on the efficiency indicators, the entropy weight TOPSIS method was used for comprehensive evaluation, and the influencing factors of hospital resilience were analyzed by one-way ANOVA and logistic stepwise regression method.Results:From 2019 to 2021, the average technical efficiency values of tertiary public general hospitals in Hunan province were 0.861, 0.749 and 0.810. The total factor productivity in 2020 decreased by 12.3% compared with that in 2019, the total factor productivity in 2021 increased by 8.3% compared with 2020, and the total factor productivity in 2021 decreased by 5.7% compared with that in 2019. In the context of major emergencies, the hospital resilience index of tertiary public general hospitals in Hunan province was 0.557, and the hospital resilience index of super-scale hospitals and hospitals under the National Health Commission was relatively high, with indexes of 0.647 and 0.715, respectively. The logistic stepwise regression model included three indicators: the number of medical staff with senior professional titles, the proportion of minimally invasive surgery and the average length of stay, and the OR values were 1.005, 1.261 and 0.406, respectively. Conclusions:The efficiency of tertiary public general hospitals in Hunan province needs to be improved, and the resilience level of hospitals under the background of major emergencies is not enough. The hospital resilience index is a useful attempt to evaluate the resilience of hospitals, and can be used as a policy management tool for continuous improvement of health emergency. It is suggested that the tertiary public general hospitals in Hunan province should promote the construction of resilient hospitals from the aspects of emergency talent reserve, research and application of key core technologies, and optimization of operational efficiency management concepts and mechanisms.
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Objective To analyze the equity and efficiency of resource allocation for management and treatment of severe mental disorders in Shanghai in 2020, and to provide a foundation for making relevant policies. Methods Data on resource allocation for the management and treatment of severe mental disorders in 17 district-level mental health institutions in 2020 were collected. The Gini coefficient was used to evaluate the equity of resource allocation by population and geographic area, and data envelopment analysis was carried out to analyze the equity of resource allocation. Results The Gini coefficients of special funds, psychiatric medical staff and actual open beds according to population were 0.24, 0.25 and 0.27, respectively. The Gini coefficients according to area were 0.54, 0.62 and 0.64, respectively. The average efficiency of resource allocation was 0.865. There were 5 institutions where DEA was effective, accounting for 29.41%. There were 12 institutions where DEA was non-effective, accounting for 70.59%. Conclusion The equity of resources allocation for the management and treatment of severe mental disorders according to population is good, but the equity of allocation based on geographic area is not high. The efficiency of resource allocation needs to be further improved. It is suggested that the resource allocation should be optimized to promote the fairness and efficiency of resource allocation for the management and treatment of severe mental disorders.
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Objective:To understand the overall development and dynamic change trend of scientific research input-output efficiency of a tertiary hospital in Xinjiang from 2016 to 2021, so as to provide reference and basis for optimizing hospital scientific research configuration and improving scientific research efficiency.Methods:The BCC model of Data Envelopment Analysis (DEA) method is combined with Malmquist index method for analysis and evaluation.Results:Within 6 years, the average value of comprehensive technical efficiency of nursing units has not reached 1.The total factor productivity of nursing units was unstable, showing an alternating rise and fall trend, with a fluctuation range of 0.749~1.140.Conclusions:The overall efficiency of scientific research in clinical departments of hospitals is not high. Hospitals should implement multi-level scientific research evaluation, form a dynamic evaluation system for scientific research, reasonably optimize and supervise the investment and utilization of scientific research resources, improve the scientific research management system, enhance the scientific research awareness of researchers, promote technological progress of researchers, and thereby improve scientific research efficiency.
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Objective:To analyze the supply efficiency and influencing factors of medical and health services in 31 provinces in China from 2011 to 2020, providing reference for rational allocation of medical and health resources and improving service efficiency.Methods:The data related to the input-output indicators of China′s medical and health services from 2011 to 2020 were collected from China Health Statistical Yearbook, China Statistical Yearbook and China Social Statistical Yearbook. Data envelopment analysis was used to calculate the static efficiency of China′s medical and health service supply, the Malmquist index method was used to analyze the dynamic efficiency of China′s medical and health service supply, and the Tobit model was used to analyze the factors affecting the efficiency. Results:In 2020, the comprehensive efficiency of medical and health service supply in 15 provinces (Tianjin, Shanghai, Zhejiang, etc.) was 1.000, and the scale benefit remained unchanged. The comprehensive efficiency in 16 provinces (Heilongjiang, Jilin, Inner Mongolia, etc.) was less than 1.000. Among them, 15 provinces showed a decreasing scale benefit, while 1 province showed an increasing scale benefit.From 2011 to 2020, the total factor production efficiency index of China′s healthcare service supply increased from 0.988 to 1.036. The factors affecting the efficiency included number of people with a college degree or above per 10 000 people, the utilization rate of hospital bed rate, population density, asset liability ratio, and average length of stay ( P<0.05). Conclusions:In recent years, the efficiency of healthcare service supply in China showed a growth trend featuring regional differences and multiple influencing factors. It is suggested to further narrow the regional differences of the efficiency, reasonable control the scale of medical institutions, optimize medical service technology and management levels, shorten the average transfer day and improve bed utilization to improve the overall efficiency of medical and health service supply.
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Objective:To analyze the efficiency of medical resource utilization in public traditional Chinese medicine (TCM) hospitals in Gansu province from 2016 to 2020, so as to provide decision-making reference.Methods:The number of in-service staff, actual number of open beds, number of diagnosis and treatment, and number of discharge from TCM hospitals in Gansu province from 2016 to 2020 were extracted, and their technical efficiency, pure technical efficiency, scale efficiency, and returns to scale were analyzed by data envelopment analysis.Results:From 2016 to 2020, the average technical efficiency, pure technical efficiency, and scale efficiency of the sample hospitals were 0.647, 0.680, and 0.952, respectively. Among them, 213 hospitals (48.2%) were in a decreasing state of returns to scale, 54 hospitals (12.2%) were in a constant state of returns to scale, and 175 hospitals (39.6%) were in an increasing state of returns to scale; Out of the 45 tertiary hospitals, 42 (93.3%) were in the stage of diminishing returns to scale, while 226 (56.9%) of 397 secondary and lower hospitals were in a state of constant or increasing returns to scale.Conclusions:The utilization efficiency of medical resources in public TCM hospitals in Gansu province is relatively low, and there is a significant gap between different levels of TCM hospitals.
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Objective:To analyze the input and output status of health resources in primary medical and health institutions and their allocation efficiency in different regions of China, and to provide an empirical basis for optimizing the allocation of primary medical and health resources in China among regions.Methods:The input index data (number of beds and number of health personnel) and output index data (number of primary medical and health institutions visits, number of family health services, number of hospital admissions) of primary medical and health institutions in China in 2020 were extracted from the China Health Statistical Yearbook 2021. Based on the BCC ( Banker, Charnes, Cooper) model of data envelopment analysis ( DEA), the Bootstrap- DEA method was used to correct bias, the allocation efficiency of primary medical and health resources in 31 provinces was calculated and the regional differences were analyzed. Results:After bias correction, the technical efficiency (TE) of resource allocation in primary medical and health institutions decreased by 0.102. The average TE score of all 31 primary medical and health institutions was 0.669, indicating a serious problem of ineffective use of technology. The TE of the eastern, central and western regions was 0.694, 0.663, and 0.649 respectively. There was obvious polarization in the central regions.Further analysis of the efficiency improvement of non DEA efficient provinces showed that 2 DEA weakly efficient provinces and 16 DEA ineffective provinces had several reference provinces for efficiency configuration improvement; The provinces that have been referenced more than 10 times were Zhejiang, Chongqing, Sichuan, and Ningxia, while the provinces that were listed as the first reference by other provinces were Ningxia, Chongqing, Zhejiang, and Tibet.Conclusions:The resource allocation efficiency of primary medical and health institutions in China is relatively low, and regional differences are obvious. The balance between different inputs and outputs should be considered when allocating the resources. Non DEA effective provinces can use DEA analysis to find the most suitable reference object and make reference improvements in the short term.
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Objective:To establish a calculation model for the operational efficiency and resource allocation of clinical departments in hospitals, for references for hospitals to optimize resource allocation.Methods:The informations including hospitalization time, nursing grade, etc. of inpatients admitted by 32 clinical departments in a tertiary public hospital from January to December in 2021 were extracted. A data envelopment analysis method was conducted on the operation efficiency and input edundancy of the departments. The K-means algorithm was used to divide inpatients into 3 categories according to the level of medical workload. Taking the numbers of doctors, nurses and beds as the input indicators, and the numbers of patients in the 3 categories as the output indicators, a BCC model 1 was established to evaluate the efficiency of resources invested by clinical departments into professional human value. At the same time, a BCC model 2 was established with the total number of patients admitted and medical income as the output indicators to evaluate the efficiency of resources invested by clinical departments into economic benefits.Results:A total of 38 147 inpatients were enrolled. There were 14 departments with overall technical efficiency (OTE) =1.000 in the BCC model 1, 10 departments with OTE=1.000 in the BCC model 2, and 8 departments with OTE=1.000 in the 2 models. As for the input redundancy, 6 departments had high input redundancy in the BCC model 1, 11 departments had high input redundancy in the BCC model 2, and 4 departments had high input redundancy in both models.Conclusions:The model established by this study could effectively evaluate the operational efficiency and input redundancy of clinical departments, identify departments with high workload and low economic benefits, and provide reference for the rational allocation of medical resources in hospitals.
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Objective:To investigate the static and dynamic trends of scientific research efficiency of the critical care medicine in hospitals affiliated S university during the 13th Five-Year Plan period.Methods:Based on the scientific research data of 16 hospitals affiliated to Beijing S University from 2014 to 2020, the scientific research investment funds and the number of physicians involved in scientific research were selected as input evaluation indexes, and the number of science citation index (SCI) papers, Chinese science citation database (CSCD) papers, and the number of masters and doctors trained were selected as output evaluation indexes, and the evaluation index system of scientific research efficiency of critical care medicine was constructed. SPSS version 23.0 software was used for descriptive data statistics, and data envelopment analysis (DEA)-BCC model and DEA-Malmquist index model of DEAP 2.1 software were used for static and dynamic evaluation of its scientific research efficiency from 2016 to 2020, respectively.Results:① The scientific research technical efficiency (TE) of critical care medicine in 16 hospitals affiliated with S universities varied greatly from 2016 to 2020, but pure technical efficiency (PTE) and scale efficiency (SE) were at a good level, and 6-11 affiliated hospitals in critical care medicine kept DEA effective for 5 consecutive years. ② Dynamic analysis of their total factor productivity (TFP) of scientific research from 2016 to 2020 showed a trend of rising and then falling and then rising again. The mean value was 0.985. The technical efficiency change (TEC) showed a decreasing and then increasing trend, and the technical progress change (TC) showed a slow increasing and then decreasing trend, with a mean value of 0.953. While the mean values of TEC, pure technical efficiency change (PTEC) and scale efficiency change (SEC) were above 1, which showed that the growth of total factor productivity index of research and innovation depended more on the technical efficiency index.Conclusions:The "gain effect" and "catch-up effect" of scientific research efficiency in the specialty of critical care medicine in hospitals affiliated S universities are obvious, but the "growth effect" is not obvious. "Although the research efficiency of the 13th Five-Year Plan period has been significantly improved, there is still much room for improvement in scientific and technological innovation and international academic influence.
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OBJECTIVE To evaluate the efficiency of drug safet y supervision in China after issuing a series of new policies in 2018，and to provide the suggestions for optimizing the construction of drug safety supervision system in China and narrowing regional differences. METHODS The panel data of input-output indexes of 18 provinces in 7 administrative regions were collected from the official website of provincial drug regulatory departments ，the open platform of budget and final accounts and the official website of National Bureau of Statistics. Data envelopment analysis （DEA）model and Malmquist index were adopted to conduct an empirical analysis on the efficiency of drug safety supervision in China during 2019-2020. RESULTS & CONCLUSIONS DEA analysis showed that during 2019-2020，the overall technical efficiency （TE）of drug safety supervision in China was lower than 1.000，which didn ’t meet effective DEA. Only Liaoning ，Guangdong and Guangxi had TE of 1.000，indicating significant differences in efficiency of drug safety supervision in different regions. The results of Malmquist index analysis showed that the overall efficiency of drug safety regulation in China was declining ，among which insufficient regulatory capacity supported by technology and large loss of professional personnel were the main factors ，and the improvement of drug regulatory departments ’ management and supervision level could ensure the improvement of overall regulatory efficiency. The current scale of drug supervision in nearly half of provinces （8/18）was close to the optimal state. It is necessary to strengthen the infrastructure construction and pay attention to the training of professional talents to optimize the drug regulatory team ；strengthen the innovation of supervision technology and improve the construction of technological support system ；rationally allocate regulatory resources and balance regional regulation according to local conditions to improve the construction of drug safety supervision system in China.
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Objective:To analyze the operation efficiency of hospitals in 31 provinces in China from 2009 to 2019 based on the three-stage data envelopmeni analysis(DEA) model, for references to improve the operation efficiency of hospitals in China and promote the high-quality development of public hospitals.Methods:The data came from such sources as China health statistics yearbook and China general hospital ranking list of Fudan university.The number of hospitals, health technicians and beds in 31 provinces of China from 2009 to 2019 were used as input indicators, while that of hospital patients, discharged patients, hospitalized patients, reputational scoring of superior specialties and academic scoring of scientific research were used as output indicators.Government health expenditure, per capita GDP, population density and the proportion of tertiary hospitals were used as environmental variables.The three-stage DEA model was used to calculate the hospital operation efficiency and scale reward.Results:The environmental variables affected the operation efficiency of hospitals in China( P<0.05). After removing the impact, the average of comprehensive efficiency, pure technical efficiency and scale efficiency of hospitals in 31 provinces from 2009 to 2019 were 0.703, 0.961 and 0.726, respectively.Among them, the scale benefit of hospitals in 4 provinces remained unchanged, while those in 26 provinces increased progressively and 1 province decreased progressively. Conclusions:Pure technical efficiency could be the main factor to improve the operation efficiency of hospitals in China, while the low scale efficiency will affect the improvement of the operation efficiency of hospitals.The scale efficiency of hospitals in most provinces had great room for improvement.In order to improve the overall hospital operation efficiency in China, the authors suggested to expand hospital scale based on the precondition of quality, promote balanced distribution of high-quality medical resources, and play the positive role of the social, economic and environment variables.
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Objective:To analyze the nursing efficiency of clinical departments in general hospitals under the background of medical insurance payment reform, and to explore the methods of rational allocation of resources and improvement of service efficiency.Methods:The relevant data of Qingdao Municipal Hospital from January to December 2020 were selected. The nursing efficiency of 20 clinical departments in the hospital was evaluated by data envelopment analysis (DEA) and Malmquist index. The input indexes were the number of nurses, the number of hours, the number of open beds. The output indicators were number of discharges, average length of stay, and case mix index.Results:In 2020, the average comprehensive efficiency, pure technical efficiency and scale efficiency of 20 clinical departments in the hospital were 0.845, 0.913 and 0.923, respectively.The total factor production efficiency index of the hospital from February to April, from May to September and from October to November were all greater than 1, but the total factor production efficiency index from January to December was less than 1. There were 5 departments with total factor production efficiency index greater than 1.Conclusions:The comprehensive nursing efficiency of clinical departments needs to be improved, and diagnosis related groups (DRG) poses a more severe challenge to the efficiency management of internal medicine nursing. The COVID-19 epidemic has a great impact on the efficiency of nursing services.It is feasible to evaluate the nursing efficiency with DEA model and DRG related indicators.Combined with the disease characteristics and nursing work characteristics of patients admitted to the department, the deep causes should be explored, comprehensive measures should be taken to improve nursing efficiency, precise nursing service transformation based on the reform of medical insurance payment mode should be explored, Internet + nursing service should be promoted, and the improvement of nursing resource efficiency under the status of normal epidemic prevention and control should be paid attention to.
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This research analyzed the efficiency situation of corn farms operating in the Adana province of Turkey. In this context, required farm management data were collected from 111 corn farmers by using face to face survey method during the 2019-2020 cultivation season. To determine the technical efficiency (TE) levels of corn farms, Data Envelopment Analysis (DEA) was applied. Furthermore, factors that cause the inefficiency in corn farms were detected by using the Tobit regression model. According to research results, the average TE levels of corn farms in the research area under the variable return to scale conditions are reported as 0.887 (111 farms). These results suggested that if farms reduced their input use by 11.3% on average, they can achieve the same output level and be able to reach full technical efficiency. The most ineffective source in terms of farms performance is machine expenditures with 68.2% of excessive use followed by labor use. In this regard, mechanization modernization, education and training of the labor force and more sensitive fertilizers and pesticide use can increase the efficiency of corn farms. Results of the Tobit regression model indicated that factors such as experience, education, number of tractors and size of the irrigated area positively influenced the TE, whereas family size in corn farming has a negative effect.
Esta pesquisa tem como objetivo analisar a situação de eficiência das fazendas de milho operando na província de Adana, na Turquia. Neste contexto, os dados necessários de gestão da fazenda foram coletados de 111 produtores de milho usando o método de pesquisa frente a frente durante a temporada de cultivo de 2019-2020. Para determinar os níveis de eficiência técnica (TE) das fazendas de milho, foi aplicada a Análise Envoltória de Dados (DEA). Além disso, os fatores que causam a ineficiência nas fazendas de milho foram detectados por meio do modelo de regressão Tobit. De acordo com os resultados da pesquisa, os níveis médios de TE das fazendas de milho na área de pesquisa sob as condições de retorno variável à escala são encontrados em 0,887 (111 fazendas). Esses resultados sugerem que, se as fazendas reduzirem o uso de insumos em 11,3% em média, podem atingir o mesmo nível de produção e alcançar eficiência técnica plena. A fonte mais ineficaz em termos de desempenho das fazendas são os gastos com máquinas, com 68,2% do uso excedente continuado com o uso de mão de obra. Nesse sentido, a mecanização, a modernização, a educação e o treinamento da força de trabalho e o uso de fertilizantes e pesticidas mais sensíveis podem ser sugeridos para aumentar a eficiência das fazendas de milho. Os resultados do modelo de regressão Tobit indicam que fatores como experiência, escolaridade, número de tratores e tamanho da área irrigada influenciaram positivamente no TE, enquanto o tamanho da família na cultura do milho tem efeito negativo.
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Cost Efficiency Analysis , Crop Production , Zea mays , Turkey , Regression AnalysisABSTRACT
Objective:To evaluate the scientific research efficiency and its change in a tertiary hospital, provide recommendations for improvement based on evaluation findings.Methods:We evaluated the scientific research efficiency of 35 disciplines in a tertiary hospital from 2015 to 2017 with BCC model of Data Envelopment Analysis and Malmquist index.Results:The number of DEA valid disciplines was 10, 16, 10 respectively. The average efficiency value from 2015 to 2017 of internal medicine, surgery department and supportive department was 0.62, 0.71, 0.74. The Total Factor Productivity of 2017, compared with 2015, was 1.30, which was mainly attributed to increase of Pure Technical Efficiency. There were 23 disciplines which efficiency increased and 12 disciplines which efficiency decreased.Conclusions:The scientific research efficiency of the tertiary hospital showed a rising trend. The efficiency of disciplines varied largely. For disciplines of DEA invalid or with downward trend, it is important to improve efficiency by improving management, cultivating talents, improving technology and increasing scientific output, and so on.
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Objective:To evaluate the Scientific Research Efficiency in A Tertiary Hospital, and provide recommendations for further improvement based on evaluation results.Methods:We evaluated the scientific research efficiency of 35 disciplines in a Tertiary Hospital from 2015 to 2017 with Slack Based Measure based Data envelopment analysis(DEA).Results:The average efficiency value from 2015 to 2017 is 0.44-0.65.The efficiency of Scientific Research from high to low was: supportive department, surgery department, internal medicine.There are 13 disciplines (37.14%) which efficiency increases gradually and 7 discipline (20%) which efficiency decreases.9 disciplines are DEA valid (efficiency=1) for at least 2 years and 9 is DEA invalid (efficiency<1) in three years.The average slack variable of researchers is 18.6 while average slack variable of input scientific research fund is 23.38 million RMB.Conclusions:The average efficiency of the tertiary hospital shows a rising trend.However, the efficiency of disciplines varies largely.For DEA invalid disciplines, it is important to improve efficiency by increasing output and improving efficiency of scientific research funding.
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Objective:By calculating the efficiency of the scientific research laboratory, which reflects the level of the scientific research input and output capacity, provide reference for the evaluation and decision-making of its scientific research sustainable development capacity.Methods:20 scientific research laboratories in a hospital were selected as the research subjects, annual input data were used as the input index. Weighted quantitative scores of the performance of each laboratory in research capacity and contribution, research team construction, discipline development and personnel training, operation management, papers and monographs, patents and transfer, awards, graduate-student training, standards and norms, and academic conferences. All these factors mentioned above were used as output indicators. Then Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) are used to evaluate the scientific efficiency of each laboratory.Results:The performance of the laboratory is weak in the aspects of patent transfer, awards, standardization. The technical efficiency of laboratory 20 is the lowest, and the scale efficiency of laboratory 12 is the lowest.Conclusions:Scientific Research Laboratories should enhance the effectiveness through input adjustment and output enhancement, meanwhile each laboratory should pay attention to the transformation of scientific achievements and also the optimization of construction system.
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Objective:Data envelopment analysis (DEA) was applied to evaluate the evaluation index system of scientific research performance of hospital departments.Methods:Based on the three-level index assignment of the constructed evaluation index system of scientific research performance of hospital departments, the relevant data of departments are calculated. Scientific research projects, talent team and scientific research platform are taken as input indexes, papers, awards, patents, published monographs/papers, professional standards and guidelines, as well as achievements transformation are taken as output indexes, and departments are taken as evaluation units. Data envelopment analysis software is used to evaluate departments Results of scientific research performance evaluation, redundant input and insufficient output.Results:The average comprehensive technical efficiency of 25 departments is 0.404 8, the average pure technical efficiency is 0.837 1, and the average scale efficiency is 0.428 3. Among them, 7 departments are generally efficiency, 17 departments obtained pure technical efficiency, 7 departments obtained scale efficiency.Conclusions:The analysis results can provide a reference for the scientific research development of the department. On the basis of building a reasonable talent echelon and assure the sufficient scientific research time, the hospital further optimizes and improves the scientific research performance evaluation index system and hierarchical management method of the Department.
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Objective:To comprehensively evaluate the input and output efficiency of scientific research in hospital by bootstrap data envelopment analysis, to provide useful information for optimization of scientific performance appraisal and hospital discipline development strategy.Methods:37 disciplines were included as decision making unit, input variables include research expenditure and number of research personnel, and output variables include number of science and technology awards, research projects, patent transfer, paper, composition, and academic influence. The bootstrap-DEA method was used to evaluate the efficiency of all DMUs.Results:The main of overall efficiency and pure technical efficiency in basic DEA model are 0.858 and 0.909, but are 0.804 and 0.853 in Bootstrap DEA model, the differences between two models have statistically significant ( P<0.001). There are 11 DMUs with an overall efficiency in 0.9~1.0, 14 DMUs in 0.8~0.9, 7 DMUs in 0.6~0.8, 5 DMUs lower than 0.6. There are 3 DMUs are increasing return to scale, 16 DMUs are constant return to scale, 18 DMUs are decreasing return to scale. No statistically significance was observed between different types of DMUs( P>0.05). There are 4 DMUs reveal input slacks in number of research staffs and 10 DMUs reveal output slacks. Conclusions:The results of Bootstrap-DEA are more accurate than the basic methods for the evaluation of the input-output efficiency of hospital scientific research, so that it is worth popularizing and applying. According to the evaluation results, the hospital management department and disciplines could optimize their discipline development strategies and put forward targeted improvement measurements.
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Objective:To provide strategic suggestions for optimizing children′s diagnosis and treatment services in the communities, by means of analyzing the overall efficiency of children′s diagnosis and treatment services in the sample community health service centers, and learning the current input and output of children′s diagnosis and treatment resources.Methods:In April 2020, a total of 27 community health service centers in 14 cities were selected by random sampling. Data such as the number of medical visits by children aged 0 to 18 years and the area of pediatric diagnosis and treatment departments in the sample centers in 2019 were collected by self-filling questionnaires. Excel was used for data sorting. Data envelopment analysis(DEA) was used for data processing. The data processing tool was DEAP 2.1.Results:The average comprehensive efficiency, the average technical efficiency and the average scale efficiency of the 27 sample community health service centers were 0.445, 0.865 and 0.494 respectively. There were five DEA efficient centers, 4 DEA weak inefficiency centers and 18 inefficient centers. Six out of 18 DEA inefficient centers had redundant input of healthcare professionals capable pediatrics; 12 centers were short of children visits, and 15 were short of visits by children aged 0-6 years.The centers where DEA was inefficient were concentrated in the central region, the suburbs and " centers with independent pediatric clinics but without pediatric wards" .Conclusions:The comprehensive efficiency of children′s diagnosis and treatment services in the sample community health service centers is relatively low. Currently, the sample community health service centers are faced with such problems as small and insufficient input of children′s diagnosis and treatment resources in the community, unbalanced development of children′s diagnosis and treatment services in the region among others. It is suggested that on the basis of making full use of the existing resources to create the maximum output value, we should consider appropriately expanding the scale of resource input to improve the efficiency of children′s diagnosis and treatment services at the primary level and further give play to the value of the " gatekeepers" at the primary level in children′s diagnosis and treatment.
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RESUMEN En esta investigación se desarrolla un método que integra herramientas de análisis multivariado con el objetivo de identificar perfiles característicos de las pequeñas y medianas empresas exportadoras pequeñas y evaluar su eficiencia empresarial, de manera que se apoyen procesos de mejora en sus resultados. Para lo anterior se revisaron elementos teóricos asociados a la eficiencia empresarial y el cálculo estadístico multivariado, lo que permitió desarrollar una metodología que integra el análisis de conglomerados, análisis discriminante y análisis envolvente de datos para evaluar la eficiencia empresarial. Se analizaron 45 empresas pequeñas y medianas exportadoras de Cartagena-Colombia, en las que se identificaron 3 perfiles característicos con niveles promedio de eficiencia de 71,89% el uno, 70,93% el dos y 51,25% tres. El análisis discriminante mostró la pertinencia y relevancia de los perfiles identificados lográndose un 95,6% de clasificación correcta del modelo discriminante. Se concluye que las herramientas de cálculo multivariado analizadas en esta investigación son significativas para clasificar y evaluar la eficiencia de grupos empresariales. CLASIFICACIÓN JEL C19, O11, O32
ABSTRACT This research develops a method that integrates multivariate analysis tools with the aim of identifying characteristic profiles of small and medium-sized small exporting companies and evaluating their business efficiency so that improvement processes are supported in their results. For the above, theoretical elements associated with business efficiency and multivariate statistical calculation were reviewed, which develops a methodology that integrates cluster analysis, discriminant analysis and data envelopment analysis to evaluate business efficiency. 45 small and medium exporting companies from Cartagena-Colombia were analyzed, in which 3 characteristic profiles were identified with average efficiency levels of 71.89% for one, 70.93% for two and 51.25% for three. The discriminant analysis selected the relevance and relevance of the identified profiles, registering 95.6% of correct classification of the discriminant model. It is concluded that the multivariate calculation tools analyzed in this research are analyzed to classify and evaluate the efficiency of business groups. JEL CLASSIFICATION C19, O11, O32
RESUMO Nesta investigação é desenvolvido um método que integra ferramentas de análise multivariada com o objectivo de identificar perfis característicos das pequenas e médias empresas exportadoras e avaliar a sua eficiência empresarial, de forma a apoiar os processos de melhoria dos seus resultados. Para o efeito, foram revistos elementos teóricos associados à eficiência empresarial e ao cálculo estatístico multivariado, o que permitiu desenvolver uma metodologia que integra análise de clusters, análise discriminante e análise envolvente de dados para avaliar a eficiência empresarial. Foram analisadas quarenta e cinco pequenas e médias empresas de exportação em Cartagena-Colômbia, identificando três perfis característicos com níveis médios de eficiência de 71,89% para uma, 70,93% para duas e 51,25% para três. A análise discriminante mostrou a pertinência e relevância dos perfis identificados, atingindo 95,6% da classificação correcta do modelo discriminante. Conclui-se que as ferramentas de cálculo multivariado analisadas nesta investigação são significativas para classificar e avaliar a eficiência dos grupos empresariais. CLASSIFICAÇÃO JEL C19, O11, O32