基于DEA不同指标组合的31个省市医院相对效率研究

选取我国31个省市的医院作为决策单元,应用数据包络分析(DEA)的CCR模型,选出7组不同指标组合对医院效率进行全面分析,发现10个省份总体效率较高,年出院人次指标对DEA结果影响较大,B组指标组合对医院效率分析效果最好。DEA可以科学有效地评价医院的相对效率,不同指标组合对DEA评价结果存在差异,应合理选取指标组合。各级政府还需依照科学管理方法,提高医疗资源使用效率。     

中国医院效率DEA研究分类与投入产出指标分析

目的:了解中国目前医院效率数据包络分析(DEA)研究现状,为进一步提升中国医院效率DEA研究水平提供参考.方法:对1984—2014年应用数据包络分析中国医院效率的85篇密切文献进行系统分类和综述,总结归纳文献的基本特点,对其投入产出指标进行归类统计,并与国际同类文献比较,评价中国此类研究的规范性.结果:应用DEA分析中国医院效率的研究存在医院配置效率研究文献缺乏、医院技术效率模型单一、投入产出指标筛选不严谨、误将经济类指标作为产出指标等突出问题.结论与建议:中国医院效率DEA研究方法使用的合理性、规范性有待进一步提高,研究与国际研究仍存在较大差距.建议相关研究者在医院效率DEA研究方面应追踪国际最新研究,科学筛选投入产出指标,深入分析使用方法和应用条件,提升中国医院效率DEA研究的规范性和科学性.     

基于数据包络分析法的安徽省妇幼保健机构资源配置效率评价与分析

目的：分析2010—2020年安徽省妇幼保健机构卫生资源配置效率及影响因素，提出妇幼保健机构卫生资源合理配置政策建议。方法：从《安徽统计年鉴》中提取安徽省妇幼保健机构2010—2020年基础配置、服务数量和服务效率的相应指标数据，运用数据包络分析（data envelopment analysis,DEA）法对其投入产出效率进行分析。结果：2010—2015年安徽省妇幼保健机构综合效益处于波动状态；2016—2019年综合效益稳定为1.000，均为DEA强有效，投入产出达到相对最有效率，规模报酬达到最优；2020年综合效益最低，为0.913，全要素生产率指数最低，为0.651。2010—2020年安徽省妇幼保健机构投入产出全要素生产率指数均值为0.961。结论：安徽省妇幼保健机构资源配置效率整体较高，投入与产出效率值得肯定。医疗技术水平滞后与医院规模的日趋扩大不匹配，医院管理者应予以重视。     

基于数据包络分析的某区民营医院效率评价

目的对天津市某区民营医院效率进行评价,为提高民营医院效率提出有效建议。方法采用文献优选法确定评价指标,运用数据包络分析(DEA)模型对投入产出指标进行分析。结果 13所民营医院中8所医院DEA有效,5所医院非DEA有效,5所非DEA有效的医院规模效率均小于1;2010—2012年,13所医院的全要素生产率平均降低18.4%,技术进步降低19.4%,技术效率提高1.3%。结论民营医院存在效率低下的问题,主要原因是规模不经济和技术投入不足。     

基于超效率DEA模型的外科主诊医师组手术投入产出效率评价

目的 分析各外科主诊医师组手术投入产出相对效率，为提升主诊组手术效率提供参考依据。方法 将2021年1月1日—12月31日的40个外科主诊医师组作为决策单元，运用超效率数据包络分析（Data Envelopment Analysis,DEA）及灰色关联分析对其手术投入产出效率进行评价及分析。结果 40个外科主诊医师组的DEA综合技术效率均值为0.620 8，纯技术效率均值为0.775 1，规模效率均值为0.802 0，仅3个主诊医师组达到DEA有效；其中，主诊医师组开放床位数是影响手术超效率值的主要因素。结论 医院外科主诊医师组手术效率总体水平偏低，非DEA有效主诊医师组存在投入冗余或产出不足，建议医疗机构适时优化主诊组医疗资源配置，加强手术绩效管理，提高手术产出。     

数据包络分析与随机前沿分析的比较研究--基于 Monte Carlo 模拟研究

目的：比较研究数据包络分析（ DEA）和随机前沿分析（ SFA）方法。方法通过蒙特卡罗模拟方法,比较两种方法效率值估计的准确性和效率值排名的一致性（ Spearman相关系数）以及评价指标选择对两者的影响。结果DEA与SFA结果一致性较高;删减必要指标对两种方法评估的准确性和一致性的影响较大,而增加无关指标对两者的影响较少,增加无关指标前后效率值一致性较高,Spearman相关系数大部分在0．9以上。结论选择指标时要慎重,对效率评估结果影响较大的指标应保留。     

基于数据包络分析的福建三级甲等医院运营效率评价

目的对福建省随机抽取的15所三级甲等医院效率进行评价,为提高三级甲等医院效率提出有效建议。方法通过文献优选法确定评价指标,运用数据包络分析(DEA)模型对投入产出指标进行分析。结果 15所医院平均效率值为0.929,其中9所医院DEA有效,6所医院非DEA有效,6所非DEA有效的医院规模效率均小于1,2所医院规模报酬递减,4所医院规模报酬递增;非DEA有效医院若达到DEA有效卫生技术人员可减少1240人,节省床位1523张,固定资产总额147418万元。结论医院不但需要提高管理水平,改进管理模式,资源合理投入,提高利用效率,也要加强协同合作,实现共赢。     

DEA在评价哈尔滨市二级医院相对效率中的应用

目的 尝试运用数据包络分析（DEA分析）对哈尔滨市48所二级医院进行相对效率综合评价。方法 共收集48所二级医院投入产出资料，采用描述性分析、聚类分析及数据包络分析进行分析研究。结果 DEA分析结果显示：①近1／2的被评价医院处于低效率运行状态；②最无效率的医院（D24）通过各指标的改善值可达到相对有效。结论 建议在增加三级医院投入的同时，也要结合二级医院的特点增加投入，建立一个良好的投资机制、经营机制。     

数据包络分析对某公立医院科室的效率研究

目的分析临床科室资源配置情况,为医院提升效率、加强内部管理提供决策依据。方法通过文献研究确定数据包络分析的投入产出指标;运用数据包络分析研究某公立医院30个临床科室的2015年运行效率。结果确定了临床科室效率研究的3个投入指标为科室人员数、床位数、业务支出,5个产出指标为医疗(不含药品)收入、门诊人次、出院人次、实际占用床日、论文当量;2015年30个临床科室中DEA有效的临床科室10个,占33.33%,20个DEA无效的临床科室各投入指标的投入冗余率、产出不足率均不相同。结论数据包络分析结果与临床科室实际情况基本相符;数据包络分析可优化临床科室资源配置,辅助医院绩效考核;"专业+效率"将会为临床科室的发展方向。     

数据包络分析法对某医院临床科室相对效率的评价

目的 以西部某大型综合性三甲医院2004～2008年5年内14个临床科室作为研究对象,对其进行相对效率研究.方法 通过应用聚类分析法确定投入产出指标、进行数据调查,采用数据包络分析(Data Envelopment Analysis,DEA)方法中CCR和BCC模型对各科室相对效率进行评价和分析.结果 分析了DEA方法在临床科室相对效率评价中的应用价值,总结了应用中在指标选择、数据处理和结果判读方面需注意的问题,并提出了DEA应用于临床科室相对效率评价的发展方向.结论 利用DEA对相对效率进行分析,对医院管理具有重要意义.     

Relative efficiency in rural primary health care: an application of data envelopment analysis.

This study applied data envelopment analysis (DEA) to the evaluation of rural primary health care programs, which are known to be very heterogeneous. DEA is a mathematical programming technique that optimizes the relative efficiency ratio of current inputs over current outputs for each decision-making unit (DMU). It produces a summary scalar efficiency ratio for each DMU and identifies the amount of inefficiency. The data came from the National Evaluation of Rural Primary Health Care Programs. Despite the demands of the software used for homogeneous units and nonzero values, the efficiency analysis was useful to the evaluation. It assessed multiple inputs and multiple outputs simultaneously, and identified directly those units that are performing efficiently or inefficiently when compared to specific peer programs. This then allowed us to compare this efficient-inefficient classification with other data, first, to verify the classification and, second, to assist with the evaluation. DEA can contribute to the evaluation of heterogeneous health programs, especially when used in conjunction with other methods of analysis.     

The Effect of Activity-Based Financing on Hospital Efficiency: A Panel Data Analysis of DEA Efficiency Scores 1992–2000

Activity-based financing (ABF) was implemented in the Norwegian hospital sector from 1 July 1997. A fraction of the block grant from the state to the county councils has been replaced by a matching grant depending upon the number and composition of hospital treatments. As a result of the reform, the majority of county councils have introduced activity-based contracts with their hospitals. This paper studies the effect of activity-based funding on hospital efficiency. We predict that hospital efficiency will increase because the benefit from cost-reducing efforts in terms of number of treated patients is increased under ABF as compared with global budgets. The prediction is tested using a panel data set from the period 1992–2000. Efficiency indicators are estimated by means of data envelopment analysis (DEA) with multiple inputs and outputs. Using a variety of econometric methods, we find that the introduction of ABF has improved efficiency when measured as technical efficiency according to DEA analysis. The result is less uniform with respect to the effect on cost-efficiency.     

Sensitivity of super-efficient data envelopment analysis results to individual decision-making units: an example of surgical workload by specialty

We use resampling of data to explore the basic statistical properties of super-efficient data envelopment analysis (DEA) when used as a benchmarking tool by the manager of a single decision-making unit. Our focus is the gaps in the outputs (i.e., slacks adjusted for upward bias), as they reveal which outputs can be increased. The numerical experiments show that the estimates of the gaps fail to exhibit asymptotic consistency, a property expected for standard statistical inference. Specifically, increased sample sizes were not always associated with more accurate forecasts of the output gaps. The baseline DEA’s gaps equaled the mode of the jackknife and the mode of resampling with/without replacement from any subset of the population; usually, the baseline DEA’s gaps also equaled the median. The quartile deviations of gaps were close to zero when few decision-making units were excluded from the sample and the study unit happened to have few other units contributing to its benchmark. The results for the quartile deviations can be explained in terms of the effective combinations of decision-making units that contribute to the DEA solution. The jackknife can provide all the combinations contributing to the quartile deviation and only needs to be performed for those units that are part of the benchmark set. These results show that there is a strong rationale for examining DEA results with a sensitivity analysis that excludes one benchmark hospital at a time. This analysis enhances the quality of decision support using DEA estimates for the potential of a decision-making unit to grow one or more of its outputs.     

Measuring efficiency in acute care hospitals: an application of data envelopment analysis

In this article, the authors attempted to demonstrate how DEA can be useful to hospital administrators and health care planners. They used actual data collected by the American Hospital Association through its Monitrend Data Service. Since these were national data, they are presented here for illustrative purposes only. The efficiency with which a hospital operates may well depend upon the local or regional labor market, the competition among health care providers in that market, and the demographics of the service area. The choice of variables was dictated by reasonableness and availability of data. Given the routine collection of case mix data by DRG since 1984, the use of a different set of output variables for any future studies would be quite appropriate. Additionally, if DEA were to be used, a consensus concerning relevant controllable and non-controllable input variables would need to be achieved. There are more technical caveats of which the reader should be aware. 1) The efficiency scores are all relative and are based on the performance of the other hospitals being compared; nothing can be said about the absolute efficiency of a given hospital. However, the relative ratings are conservative in that the approach "bends over backwards" to give the individual hospital the benefit of the doubt in terms of the relative importance of the various outputs and inputs utilized. The approach maintains equity in that any weights chosen for a given hospital must be feasible for all of the other hospitals. 2. The ratings assume a causal impact of the inputs on the outputs. In addition, it is possible that inclusion of additional inputs and outputs could modify the relative scores and/or help explain the differences. However, based on the factors available, any unit rated inefficient is inferior in a very real and demonstrable sense. 3. DEA is based on the generalized notion of convexity which assumes that the performance arrived at by taking any linear weighted combination of other hospitals' inputs and outputs represents a feasible and achievable technology. The general frontier surface is approximated by piecewise-linear segments with the result that observed differences in efficiency cannot be explained away as differences in economies of scale. 4. The inefficiency score and the resource conservation potentials are based on a unit's so-called contraction path, i.e., all of the controllable inputs are required to be reduced by the same factor.(ABSTRACT TRUNCATED AT 400 WORDS)     

主成分分析和数据包络分析的联合应用

目的 联合应用主成分分析和数据包络分析以综合评价某医院各临床科室的相对效率,提出改善建议.方法 根据医院情况,选择财务、工作量和病人满意度等各方面有代表性的12个指标,计算出16个临床科室的主成分得分和相对效率值,将2者分别排序并综合于二维象限,直观地评价和判断各科室相对效率.并得出非有效科室的松弛量,指出需调整的投入产出量,以达到相对有效.结果 二维象限显示各科室2种方法间排序一致,差异无统计学意义（P〉0.05）.位于第一象限的普外科、神经内科、肿瘤内科相对效率高（最优解θ=1）、综合情况较好 （PCA得分〉1）;而第四象限的小儿外科、眼科等相对效率高（最优解θ=1）,但主成分得分低（PCA得分〈-1.5）,规模偏小综合实力较差,应考虑予以发展;第二、三象限的感染病科、血液内科和口腔科则效率较差,为达到科室相对有效,在产出不变时,实际占用总床日数、职工人数、工资和福利、固定资产、材料及一次性消耗等投入应根据松弛量进行调整.结论 主成分分析和数据包络分析能互相补充,既体现评价单元的综合差异,又可比较相对效率,联合应用可以更全面地评价各决策单元.使用二维象限能更直观的判断较多评价单元的相对效率和综合情况,可以考虑在医院管理决策中予以推广.     

Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework

Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals.

     

Risk of inappropriateness and efficiency assessment of hospitals

We evaluated with the Data Envelopment Analysis (DEA) 13 decision making units (DMU) at IDI -IRCCS for the years 2000 and 2001. Input variables were: cost for medical personnel, cost for non medical personnel and number of beds; output variables was the number of discharged patients weighted with DRG. Later in a second model we delete the cases considered to be at "high risk" to be inappropriate for treatment as inpatients. DEA instrument is confirmed useful in the efficiency evaluation for DMU at hospital level, ranking were different between the two models. The Health Direction can utilise the analysis to understand reasons of inefficiency and for incentive policy.     

Measuring efficiency of physician practices using data envelopment analysis

PURPOSE: Medical-group practices are becoming increasingly common-place, with more than a third of licensed physicians in the United States currently working in this mode. While previous studies have focused on physician practices, little attention has been focused specifically on the contribution of internal organizational factors to overall physician practice efficiency. This paper develops a model to help determine best practices of efficient physician offices while allowing for choices between inputs. Measuring how efficient practices provide services yields useful information to help improve performance of less efficient practices. DESIGN: Data for this study were obtained from the 1999 Medical Group Management Association (MGMA) Cost Report. In this study, 115 primary care physician practices are analyzed. Outputs are defined as gross charges; inputs include square footage and medical, technical, and administrative support personnel. METHODOLOGY: Data envelopment analysis (DEA) is used in this study to develop a model of practice outputs and inputs to help identify the most efficient medical groups. DEA is a linear programming technique that converts multiple input and output measures to a single comprehensive measure of efficiency. These practices are used as a reference set for comparisons with less efficient ones. CONCLUSION: The overall results indicate that size of physician practice does not increase efficiency. There does not appear to be extensive substitution among inputs. Compared to other practices, efficient practices seem to manage each input well.     

东营市城乡一级医院效率差异分析

效率一直是医院经济管理的重点问题。本文利用线性比分析和数据包络分析两种方法对东营市城乡共60所一级医院进行了服务效率评价研究，分别对城乡一级医院1997—2001年的服务数量、医院投入、医疗服务产出等指标做了比较分析。结果表明．城市和农村地区一级医院效率都不甚理想，特别是城市地区一级医院五年间效率呈现下降趋势。农村地区一级医院效率要优于城市地区一级医院。     

An efficient method for kidney allocation problem: a credibility-based fuzzy common weights data envelopment analysis approach

Given the perennial imbalance and chronic scarcity between the demand for and supply of available organs, organ allocation is one of the most critical decisions in the management of organ transplantation networks. Organ allocation systems undergo rapid revisions for the sake of improved outcomes in terms of both equity and medical efficiency. This paper presents a Data Envelopment Analysis (DEA)-based model to evaluate the efficiency of possible patient-organ pairs for kidney allocation in order to enhance the fitness of organ allocation under inherent uncertainty in such problem. Eligible patient-kidney pairs are regarded as decision making units (DMUs) in a Credibility-based Fuzzy Common Weights DEA (CFCWDEA) approach and are ranked based on efficiency scores. Using a common set of weights for all DMUs ensures a high degree of fairness in the assessment and ranking of DMUs. The proposed model is also the first allocation method capable of coping with the vague and intervallic medical and nonmedical allocation factors by the aid of fuzzy programming. Verification and validation of the proposed approach are performed in two steps using a real case study from the Iranian kidney allocation system. First, the superiority of the proposed deterministic model in enhancing allocation outcomes is demonstrated and analyzed. Second, the applicability of the proposed fuzzy DEA method is demonstrated using a series of data realizations for different credibility levels.