Unified Performance Evaluation of Health Centers with Integrated Model of Data Envelopment Analysis and Bargaining Game

This paper introduces a new integrated approach to measure unified efficiency of the healthcare systems. Health centers as an important part of the healthcare systems are considered for evaluation. For this purpose, we define two categories of inputs to measure performance of health centers based on medical human resources and characteristics of spatial information by using geographic information system (GIS). Catching the balance in the spatial distribution of populations and services is one of the main problems in health centers evaluation. On the other hand, data envelopment analysis (DEA) is widely applied for measuring efficiency of the healthcare systems. But, the conventional DEA models may fail to integrated several categories of measures. In this paper, DEA and bargaining game model are integrated for evaluation of health centers. In other words, two categories of measures are used to measure unified efficiency for each health center in the competitive environment. Two models according to constant return to scale (CRS) and variable return to scale (VRS) assumptions are developed. The case study of health centers under supervising of Tehran university of medical sciences (TMUS) is presented to show the abilities of the proposed approach.     

基于DEA模型的江苏省某医院临床科室运行效率分析

目的:分析江苏省某三甲医院临床科室的运行效率,为医院管理者合理配置资源提供参考依据。方法:利用数据包络分析方法分析临床科室运行效率,其中CCR模型计算总体效率,BCC模型计算纯技术效率和规模效率。结果:26个临床科室中,6个科室总体有效、7个科室纯技术效率有效,6个科室规模收益不变、15个科室规模收益递减、5个科室规模收益递增。结论:采取差异化发展策略,合理配置医疗卫生资源,强化医院内部精细化管理。     

Measuring Hospital Efficiency with Data Envelopment Analysis: Nonsubstitutable vs. Substitutable Inputs and Outputs

There is a conflict between Data Envelopment Analysis (DEA) theory’s requirement that inputs (outputs) be substitutable, and the ubiquitous use of nonsubstitutable inputs and outputs in DEA applications to hospitals. This paper develops efficiency indicators valid for nonsubstitutable variables. Then, using a sample of 87 community hospitals, it compares the new measures’ efficiency estimates with those of conventional DEA measures. DEA substantially overestimated the hospitals’ efficiency on the average, and reported many inefficient hospitals to be efficient. Further, it greatly overestimated the efficiency of some hospitals but only slightly overestimated the efficiency of others, thus making any comparisons among hospitals questionable. These results suggest that conventional DEA models should not be used to estimate the efficiency of hospitals unless there is empirical evidence that the inputs (outputs) are substitutable. If inputs (outputs) are not substitutes, efficiency indicators valid for nonsubstitutability should be employed, or, before applying DEA, the nonsubstitutable variables should be combined using an appropriate weighting scheme or statistical methodology.     

Comparative Study of Three Commonly Used Methods for Hospital Efficiency Analysis in Beijing Tertiary Public Hospitals,China

Background:

Tertiary hospitals serve as the medical service center within the region and play an important role in the medical and health service system. They are also the key targets of public hospital reform in the new era in China. Through the reform of health system, the public hospital efficiency has changed remarkably. Therefore, this study aimed to provide some advice for efficiency assessment of public hospitals in China by comparing and analyzing the consistency of results obtained by three commonly used methods for examining hospital efficiency, that is, ratio analysis (RA), stochastic frontier analysis (SFA), and data envelopment analysis (DEA).

Methods:

The theoretical basis, operational processes, and the application status of RA, SFA, and DEA were learned through literature analysis. Then, the empirical analysis was conducted based on measured data from 51 tertiary public hospitals in Beijing from 2009 to 2011.

Results:

The average values of hospital efficiency calculated by SFA with index screening and principal component analysis (PCA) results and those calculated by DEA with index screening results were relatively stable. The efficiency of specialized hospitals was higher than that of general hospitals and that of traditional Chinese medicine hospitals. The results obtained by SFA with index screening results and the results obtained by SFA with PCA results showed a relatively high correlation (r-value in 2009, 2010, and 2011 were 0.869, 0.753, and 0.842, respectively, P < 0.01). The correlation between results obtained by DEA with index screening results and PCA results and results obtained by other methods showed statistical significance, but the correlation between results obtained by DEA with index screening results and PCA results was lower than that between results obtained by SFA with index screening results and PCA results.

Conclusions:

RA is not suitable for multi-index evaluation of hospital efficiency. In the given conditions, SFA is a stable efficiency analysis method. In the evaluation of hospital efficiency, DEA combined with PCA should be adopted with caution due to its poor stability.     

Employing post-DEA Cross-evaluation and Cluster Analysis in a Sample of Greek NHS Hospitals

To increase Data Envelopment Analysis (DEA) discrimination of efficient Decision Making Units (DMUs), by complementing “self-evaluated” efficiencies with “peer-evaluated” cross-efficiencies and, based on these results, to classify the DMUs using cluster analysis. Healthcare, which is deprived of such studies, was chosen as the study area. The sample consisted of 27 small- to medium-sized (70–500 beds) NHS general hospitals distributed throughout Greece, in areas where they are the sole NHS representatives. DEA was performed on 2005 data collected from the Ministry of Health and the General Secretariat of the National Statistical Service. Three inputs -hospital beds, physicians and other health professionals- and three outputs -case-mix adjusted hospitalized cases, surgeries and outpatient visits- were included in input-oriented, constant-returns-to-scale (CRS) and variable-returns-to-scale (VRS) models. In a second stage (post-DEA), aggressive and benevolent cross-efficiency formulations and clustering were employed, to validate (or not) the initial DEA scores. The “maverick index” was used to sort the peer-appraised hospitals. All analyses were performed using custom-made software. Ten benchmark hospitals were identified by DEA, but using the aggressive and benevolent formulations showed that two and four of them respectively were at the lower end of the maverick index list. On the other hand, only one 100% efficient (self-appraised) hospital was at the higher end of the list, using either formulation. Cluster analysis produced a hierarchical “tree” structure which dichotomized the hospitals in accordance to the cross-evaluation results, and provided insight on the two-dimensional path to improving efficiency. This is, to our awareness, the first study in the healthcare domain to employ both of these post-DEA techniques (cross efficiency and clustering) at the hospital (i.e. micro) level. The potential benefit for decision-makers is the capability to examine high and low “all-round” performers and maverick hospitals more closely, and identify and address problems typically overlooked by first-stage DEA.     

基于数据包络分析的京沪两地三级甲等综合性医院运营效率比较研究

目的采用数据包络分析（DEA）方法比较京沪两地三级甲等综合性医院的运营效率,分析运营特点和不足并提出发展建议,探讨DEA方法在医院运营管理中的应用价值。方法采集2009年度上海市和北京市各12家三级甲等综合性医院的4项投入指标和4项产出指标,利用DEA方法的BCC模型分析两地三级甲等综合性医院的运营效率。结果京沪两地医院运营效率整体较高,运营有效的医院比例大于国内其他地区;上海市三级甲等综合性医院的整体综合效率、纯技术效率和规模效率略高于北京市三级甲等综合性医院水平,但差异无统计学意义（P〉0．05）。在上海市非DEA有效的个别医院中,存在开放床位数和固定资产投入的冗余;京沪两地三级甲等综合性医院间投入产出指标中的职工总数和固定资产总额2项指标比较,差异有统计学意义（P〈0．01和P〈0．05）。结论DEA分析是医院多投入和多产出的综合运营效率评价的有效手段,是医院管理及卫生决策有效的辅助工具。京沪两地三级甲等综合性医院综合运营效率较高,且水平相当;进一步发展的对策是严格控制医院规模和投入,加强内涵建设,降低运营成本,合理配置医疗资源,不断提高精细化、专业化和科学化管理水平。     

医院投入节省的DEA效率分析模型

本文阐述了投入节省的数据包络分析(DEA)在医院效率分析中的应用.通过对单个医院效率的分析、医院效率标杆的确定、医院规模收益的评判,对投入节省的DEA模型在医院效率分析上的应用进行了探讨.投入节省DEA模型能优化投入要素组合,较好反映单个医院的投入产出效率,其应用对我国国有医院经营模式由粗放型向效率型转变有重要指导意义.     

Trends in Department of Defense hospital efficiency

This study employs a simple cross sectional design using longitudinal data to explore the underlying factors associated with differences in hospital technical efficiency using data envelopment analysis (DEA) in the Department of Defense (DOD) sector across three service components, the Army, Air Force and Navy. The results suggest that the services do not differ significantly in hospital efficiency. Nor does hospital efficiency appear to differ over time. With respect to the efficient use of input resources, the services experienced a general decline in excessive usage of various inputs over the three years. Analysis of the returns to scale captures opportunities for planners of changing the relative mix of output to input slacks for increasing a hospital's efficiency. That is, policy makers would get more immediate “bang per buck” with emphasis on improving the efficiencies of hospitals with higher returns to scale than other hospitals. Findings also suggest a significant degree of comparability between the DEA measure and these measures often used to indicate efficiency.     

数据包络分析在评价医院技术效率及质量中的应用

目的将数据包络分析方法（Data Envelopment Analysis,DEA）应用于医院的技术效率和质量的评价,来比较纳入质量指标作为产出变量后医院绩效的变化。方法利用2003年弗吉尼亚州53家非联邦急诊医院的相关信息及肺炎治疗质量数据,以技术指标为产出指标和以技术指标结合质量指标为产出指标建立2个DEA模型。结果在模型I中,DEA有效的医院数为16所;在模型1的基础上加上质量指标作为产出变量（模型2）进行分析后,DEA有效的医院数为21所,模型2有效性高于模型1（P〈0．05）。结论医院要注重同时提高技术效率和医疗质量,以优化资源的利用,提高医疗机构绩效。     

A Multiple Stage Approach for Performance Improvement of Primary Healthcare Practice

Chilean primary healthcare practice is analyzed using a Data Envelopment Analysis (DEA) multiple stage approach. We estimate the efficiency level of 259 municipalities nationwide. Since the efficiency score by itself is of limited value for decision making, we use a multivariate tool to help explain the effect of relevant factors. First, we use a cluster analysis to homogenize the units under study. Second, we use DEA to estimate the efficiency levels, which varies from 61% to 71% for urban municipalities, and from 51% to 56% in rural ones. Third, we use bootstrap to estimate confidence intervals for the efficiency scores, and a Biplot method to identify adequate variables to include in the Tobit Model, which is our last stage. We identify six factors associated with rural municipalities’ operational efficiency, and two with urban ones. Knowing the efficiency level of municipalities can help determine ways to improve their efficiency.     

基于数据包络分析的军队医院药学部门效率评价

目的:评价军队医院药学部门的相对效率,为医院合理配置药学资源提供决策参考。方法:运用数据包络分析方法（DEA）,以药学人员、设施设备、实际使用面积、发展建设经费为投入要素,以药品收费、临床药学服务、药学信息服务、学术成果、科研活动等为产出要素,评价71家军队医院药学部门的生产效率,对不同经济条件假设下的评价结果进行效率分解,以评估各部门的规模效率、要素处置效率和纯技术效率;结果:数据包络分析表明28家医院的药学部门相对效率值为1,DEA有效,43家医院药学部门相对效率<1,投入要素存在效率损失;多元线性回归分析表明,医院编制等级、医院病床展开数、临床药师下临床的时间、人员新进率、本科以上学历的药学专业技术人员比例、病床使用率等因素与5项产出要素值相关,并建立5个多元线性回归方程;Pearson χ2检验表明规模效率、要素处置效率和纯技术效率相对无效的药学部门在不同的医院编制等级中的分布差异有统计学意义,要素处置效率、纯技术效率相对无效的药学部门在不同地区的分布差异有统计学意义;Pearson 两两相关分析结果表明药学部门DEA相对效率值与医院等级相关,与医院所属地区和所在城市无关。结论:军队医院的编制等级以及由此引起的资源和技术力量的有效配置可能是药学部门提高效率的关键因素。     

中国基层医疗卫生机构运行效率及影响因素研究

背景　基层医疗卫生机构是我国卫生服务体系的网底,承担着基本医疗和基本公共卫生服务的职责,在改善居民健康状况、降低医药费用等方面起着重要作用,目前国内关于全国性基层医疗卫生机构效率的研究较为缺乏。目的　分析我国基层医疗卫生机构运行效率,探讨基层医疗卫生机构效率的影响因素。方法　数据来源于2016年11月—2017年5月“基层医疗服务能力和质量的综合评价”项目,收集322家基层医疗卫生机构的外部特征、内部管理与技术因素、投入-产出指标等资料。采用数据包络分析计算基层医疗卫生机构的效率值,采用多元线性回归分析基层医疗卫生机构效率的影响因素,提出针对性的建议。结果　322家基层医疗卫生机构平均规模效率、纯技术效率、综合技术效率值分别为（0.79±0.23）、（0.52±0.25）、（0.42±0.26）。多元线性回归分析结果显示,地域、财政补助方式、辖区6岁以下儿童人数为基层医疗卫生机构规模效率的影响因素,地域、是否开设外科、是否使用电子病历、绩效工资主要的影响因素是否包括国家公共卫生服务达标率是基层医疗卫生机构纯技术效率的影响因素,地域、辖区6岁以下儿童人数、是否开设外科为基层医疗卫生机构综合效率的影响因素（P<0.05）。结论　财政补贴应逐渐向按服务单元付费的购买模式转变,开设临床外科可能对基层医疗卫生机构效率不利,薪酬制度的改革可提升基层医疗卫生机构效率。     

A 12-year Analysis of Malmquist Total Factor Productivity in Dialysis Facilities

This study examined total factor productivity of dialysis facilities in Greece over a 12-year period, using nationally representative panel data. Data Envelopment Analysis (DEA) was used to compute Malmquist productivity indices, which were decomposed into technical efficiency change and technological change. The sample consisted of 73 dialysis facilities operating throughout the entire study period (1993–2004), corresponding to 97.3% and 58.9% of all facilities in the first and last study years respectively. Production variables were nursing staff and dialysis machines (inputs) and dialysis sessions (output). The DEA model was input-oriented allowing for constant returns to scale (CRS). Technical efficiency change was decomposed into scale efficiency change and variable returns to scale (VRS) “pure” technical efficiency change. Mean overall efficiency, throughout the study years, ranged from 39.6 to 63.1% with an all-time average of 56.7%, and only 2–4% of the facilities were fully efficient in each study year. Productivity indices indicated year-by-year progress or regress up to 5%, but the efficiency and technological components differed, in some cases, by as much as 30%. Although interesting subperiod effects were observed, conclusions could not be generalized for the entire study period due to alternating trends. We suggest that preliminary insight to productivity in this sector has been obtained, but particular subperiods must be isolated and further investigated.     

基于数据包络分析方法的某市民营医院服务效率实证研究

目的:应用数据包络分析(DEA)方法评估某市民营医院的服务效率,分析某市民营医院发展过程中经营战略存在问题.方法:采用问卷调查统计某市全部民营医院业务经营数据,筛选指标与样本,应用DEA方法对58家民营医院的服务效率进行评估.结果:经过测算,各民营医院在不同方面发展的有效性差异较大,医院的总体有效性、技术有效性与规模有效性差异明显.效率总体有效12家、技术有效23家、规模有效14家.结论:某市多数民营医院的规模或技术有效性存在缺陷,为了使得其服务效率达到总体有效,必须结合各自实际,注重技术和规模的协调发展.     

The Effects of Environmental Factors on the Efficiency of Clinical Commissioning Groups in England: A Data Envelopment Analysis

Clinical Commissioning Groups (CCGs) were created in 2013 to make the NHS more responsive, efficient and accountable. A large number of different indicators can be used to measure the quality and outcomes of services provided by CCGs, however there is currently no single measure of overall efficiency available. The performance of CCGs may also be confounded by environmental factors such as deprivation, population size and burden of disease. Data Envelopment Analysis (DEA) is a linear programming technique that can be used to measure the relative efficiency of a given set of organisations. To use DEA to measure the efficiency of English CCGs and assess the impact of environmental factors. This study estimates the technical efficiency of 208 CCGs in England using DEA. The inputs and outputs used include budget allocation, number of general practitioners, mortality rates, patient satisfaction and Quality and Outcomes Framework achievement scores. Regression analysis is used to assess the effects of environmental factors on efficiency, such as population size, prevalence of disease, and socio-economic status. Twenty-three percent (47/208) of CCGs were efficient compared to the others. Three environmental factors were statistically significant predictors of efficiency: CCGs with smaller population sizes were more efficient than those with larger ones, while high unemployment rates and a high prevalence of chronic obstructive pulmonary disease led to a decrease in efficiency scores. Comparative deprivation was not a significant predictor of efficiency. The finding that the relationship between deprivation and efficiency is not statistically significant suggests that NHS England’s adjustment for environmental factors within the CCG-level budget allocation is broadly successful. This study shows the potential of DEA for assessing technical efficiency at CCG-level in the English NHS.     

Investigating Sources of Inefficiency in Residential Mental Health Facilities

Data Envelopment Analysis (DEA) was used to measure efficiency of residential mental health facilities. The sample consisted of 50 half-way houses, 8 nursing homes, and 32 sheltered homes. In total, 68 facilities belonged to the public sector and were 22 supervised by private non-profit organizations. Variables chosen to characterize production were: structure size (m²), staff, salaries and operational costs, and the output measure was patient numbers. An input oriented DEA model, allowing for variable returns to scale, was applied and units were ranked according to a benchmarking approach. Mean efficiency, for the whole sample, was 73.2% and 18 best practice units were found, on average, 33.1% over-efficient. The other 72 were under-performing, with 54 appearing more than 20% inefficient. The mean efficiency scores for public and private non-profit units were 68.8 and 86.6%, respectively, and significantly different (p < 0.001). Results suggest that efficiency improvements are possible with better use of resources but more research employing various data sets is required.     

An Efficiency Data Envelopment Analysis Model Reinforced by Classification and Regression Tree for Hospital Performance Evaluation

As changes in the medical environment and policies on national health insurance coverage have triggered tremendous impacts on the business performance and financial management of medical institutions, effective management becomes increasingly crucial for hospitals to enhance competitiveness and to strive for sustainable development. The study accordingly aims at evaluating hospital operational efficiency for better resource allocation and cost effectiveness. Several data envelopment analysis (DEA)-based models were first compared, and the DEA-artificial neural network (ANN) model was identified as more capable than the DEA and DEA-assurance region (AR) models of measuring operational efficiency and recognizing the best-performing hospital. The classification and regression tree (CART) efficiency model was then utilized to extract rules for improving resource allocation of medical institutions.     

Efficiency Measurement of Cardiac Care Units of Isfahan Hospitals in Iran

The purpose of this study is to evaluate the performance of the Cardiac Care Units (CCU) of hospitals in Isfahan, Iran. The multi-criteria comparison between the wards of different hospitals is not only useful for the patients but also important for the hospitals management to improve their performance and for the medical policy makers to plan strategic decisions. In this paper, it is intended to consider the aspects of efficiency beyond the traditional evaluation of check list. There are some measures which are used in the existing health audit system and several quantified ratios. Among them the most important ones, based on the patient satisfaction and resource efficiency, have been selected using the weights obtained from their paired comparisons. The factors which have been chosen are divided into two subsets: first, input factors consisting of average number of active beds, medical equipment, personnel (such as doctors, nurses and technicians), and technological capabilities, and second, output factors including bed occupancy percentage, average length of stay, total percentage of survival and performance ratio. The input oriented and variable returns to scale model of Data envelopment analysis (DEA) technique is used to evaluate the efficiency of each CCU ward. The model can be used to find out the causes of inefficiency and how to improve the performance. The method has been applied to evaluate and compare 23 CCUs of hospitals in Isfahan. Although the current health audit system, which uses a check list, has reported 21 of them as first class CCU, DEA model reveals that 11 of them are inefficient. The results may be then used to suggest the improvement strategies based on the output factors.     

Measuring technical efficiency in health care organizations

The rising cost of health care has created great interest in developing methods to increase the efficiency of health care organizations. Despite this interest most analyses of prospective payment and other programs designed to control expenditures have examined costs and not efficiency. This article examines a new technique—data envelopment analysis (DEA)—that facilitates the conduct of efficiency studies. The utility of DEA is analyzed by comparing this technique with other methods used to measure efficiency, by discussing the application of DEA in the health care industry and by assessing the validity of results from DEA studies. The article concludes with an assessment of the strengths and weaknesses of DEA and suggestions for refining this technique.     

重庆市卫生资源配置对医患关系的影响分析

目的：分析重庆市卫生资源配置的基本状况,探索卫生资源配置对医患关系的具体影响。方法收集重庆市卫生统计年鉴,对重庆市卫生资源配置的公平性进行分析与评价,采用DEA模型对重庆市卫生资源配置的效率进行分析与评价,同时自行设计调查问卷,对样本医院、医生和患者进行现场调查,之后进行描述性分析和统计分析。结果重庆市卫生资源总量不足,卫生资源配置地域上基本处于不公平状态。重庆市卫生资源效率的评价,代入DEA模型中,其中7个区县有效,5个弱有效,26个非DEA有效。被调查的8家医院,医疗纠纷发生频率都呈上升趋势,医患关系对医方影响更大,医患关系依旧紧张。结论重庆市卫生资源总量较少,优质卫生资源稀缺,卫生资源配置不均衡且配置效率不高是导致医患关系紧张、医患矛盾集中的重要原因。