Efficiency Scores Of Hospitals Research Articles

Efficiency effects of public hospital closures in the context of public hospital reform: a multistep efficiency analysis.

In the wake of hospital reforms introduced in 2011 in Turkey, public hospitals were grouped into associations with joint management and some shared operational and administrative functions, similar in some ways to hospital trusts in the English National Health Service. Reorganization of public hospitals effect hospital and market area characteristics and existence of hospitals. The objective of this study is to examine the effect of closure on competitive hospital performances. Using administrative data from Turkish Public Hospital Statistical Yearbooks for the years 2005 to 2007 and 2014 to 2017, we conducted a three-step efficiency analysis by incorporating data envelopment analysis (DEA) and propensity score matching techniques, followed by a difference-in-differences (DiD) regression. First, we used bootstrapped DEA to calculate the efficiency scores of hospitals that were located near hospitals that had been closed. Second, we used nearest neighbour propensity score matching to form control groups and ensure that any differences between these and the intervention groups could be attributed to being near a hospital that had closed rather than differences in hospital and market area characteristics. Lastly, we employed DiD regression analysis to explore whether being near a closed hospital had an impact on the efficiency of the surviving hospitals while considering the effect of the 2011 hospital reform policies. To shed light on a potential time lag between hospital closure and changes in efficiency, we used various periods for comparison. Our results suggest that the efficiency of public hospitals in Turkey increased in hospitals that were located near hospitals that closed in Turkey from 2011. Hospital closure improves the efficiency of competitive hospitals under hospital market reforms. Future studies may wish to examine the efficiency effects of government and private sector collaboration on competition in the hospital market.

Determining the factors affecting the technical efficiency scores of public hospitals using different regression methods

PurposeThis study aims to identify the efficiency scores of hospitals affiliated to the Ministry of Health in Turkey between the years of 2010–2015 at provincial level and to reveal the factors that affect the efficiency scores.Design/methodology/approachThe two-stage data envelopment analysis (DEA) method was used to achieve the study purpose. In the first stage, DEA method based on input-oriented Charnes–Cooper–Rhodes (CCR) model was performed to calculate the efficiency scores of public hospitals at the provincial level between 2010 and 2015, and in the second stage, Tobit regression and linear regression analyses were used to identify whether the efficiency scores of provinces are affected by the input, output and control variables.FindingsUpon the analysis, the average efficiency scores of 81 provinces by years were found to vary between 0.79 and 0.89. According to both regression analyses, all of the input and output variables were found to have significant effects on the efficiency scores of provinces while only the population of province among the control variables was identified as the factor with an effect on the efficiency scores of provinces (p < 0.05).Practical implicationsThe results of this study are thought to guide health policymakers and managers in terms of both determining efficient and inefficient hospitals at the provincial level and revealing which variables should be taken into account in order to increase efficiency.Originality/valueThe study differs from previous studies on the efficiency of hospitals. First, although previous studies were generally descriptive studies to determine the efficiency level of hospitals, this study is an analytical study that tries also to show the factors affecting the efficiency of hospitals. In addition, while examining the effect of input and output variables on efficiency scores, control variables were also included in the study.

Efficiency and scale effect of county public hospitals in Shandong Province, China: a cross-sectional study

ObjectiveTo evaluate the efficiency of county public hospitals in Shandong Province following China’s new medical reform and compare the efficiency of hospitals with different bed sizes for improving efficiency.Design and...

Measuring the Efficiency of Public Hospitals in Saudi Arabia Using the Data Envelopment Analysis Approach

This purpose of this paper is to measure technical and scale efficiencies of public hospitals in 20 regions in Saudi Arabia. Furthermore, these estimates and the slack-based forecasting and the sensitivity analysis of the stability of efficiency scores were calculated. The researcher used Data Envelopment Analysis (DEA) technique based on two models - CCR and BCC, with an input-oriented approach. The Ministry of Health in Saudi Arabia published the Health Statistical Year Book for the years, 2010, 2011, 2013, 2014 and 2015 from which the data were obtained. The variables chosen included the number of beds, staff nurses and doctors as inputs, and outpatients, inpatients and surgeries representing outputs. Results indicate that the BCC model tends to classify more regions as efficient. The average PTE score of inefficient hospitals was 88 percent over the period in question, which implies that inputs could be reduced by 12 per cent without it impacting in any way on the service provided. Hospital managers and policy makers will thus play a critical role in ensuring that resources are utilised to their full potential in order to optimise efficiency. The inefficient regions could make their hospitals efficient by following the efficient regions as peers. Finally, it was found that there is stability in the efficiency scores of hospitals as revealed by sensitivity analysis, even when taking into account the exclusion of the most efficient hospitals.

Assessing performance of Taiwan hospitals using data envelopment analysis: In view of ownership.

With rapid economic development in Taiwan, people have greater awareness of health care and are paying more attention to it. From the perspective of hospital management, the scale of hospitals and efficiency improvement are of concern to hospital managers. However, the extent of efficiency will differ between public and private hospitals due to their different ownership and goals. The study aims to evaluate the efficiency of public and private hospitals and to investigate the influence of ownership on efficiency of hospitals. The differences between hospitals can be understood by analyzing the features of the organization of hospitals and their geographic environment. In this way, hospitals with relatively low efficiency will be able to make improvements based on concrete evidence. By means of the two-stage method, the efficiency scores of 182 hospitals in Taiwan are compared. In the first stage, the data envelopment analysis is applied to obtain the efficiency scores of hospitals. The results show that private hospitals are more efficient than public hospitals. In the second stage, Tobit regression is used to investigate the factors influencing efficiency obtained by the data envelopment analysis. The results indicate that there are differences between ownership in market competition and the average length of stay.

A decision system using fuzzy cognitive map and multi-group data envelopment analysis to estimate hospitals’ outputs level

Due to its vital role in healthcare system, performance evaluation of hospitals is indispensable. In addition, hospitals try to achieve desired and efficient conditions by careful planning based on their present facilities. Several studies have been conducted on hospital evaluation, but nearly none of them has taken into consideration the difference in the nature of performance in respect of hospitals’ managerial construction, funding, and type of services provided by them. Furthermore, hospitals’ outputs have not been estimated in respect of cause and effect relationships between inputs and outputs for achieving efficient conditions. In the present study, first, a new approach for hospital evaluation is presented according to the differences in the nature of their performances while categorized in groups. Then, optimal outputs for each hospital in its own group are dealt with using results obtained from multi-group data envelopment analysis and the method of fuzzy cognitive map. The activation Hebbian learning (AHL) algorithm is adapted to the concept of efficiency and is conducted to estimate the outputs of inefficient hospitals. In the present study, 27 hospitals located in the provincial capitals in northwest of Iran are categorized in four groups including general governmental hospitals, specialty governmental hospitals, private hospitals, and social security hospitals. Afterward, optimal outputs are estimated for the inefficient hospitals by using the proposed modified AHL algorithm. The results indicate that when the hospitals have been evaluated in groups, efficiency scores of hospitals have changed. Also, given the cause and effect relationships between inputs and outputs in each group can help to decision and policy makers to estimate the optimal outputs that have caused inefficient hospitals become efficient.

Investigation of the Impact of the Massachusetts Health Care Reform on Hospital Costs and Quality of Care

In 2006, the Massachusetts legislature passed a landmark health care reform bill (the Reform) that has served as a model for the national health care reform. By aiming to provide “access to affordable, quality, accountable health care,” the goals of this reform were to reduce the number of uninsured Massachusetts residents while containing the growth of health care costs and improving the quality of health care services. The current paper examines the impact of the Reform on the quality of care in addition to hospital costs simultaneously from a perspective of efficiency analysis. We develop an integer-valued non-radial Russell data envelopment analysis (DEA) model, which is unit variant and calculate the hospitals’ efficiency directly. However, the proposed integer-valued model is non-linear. The current paper thus transforms this model into a parametric integer linear programming. We develop a method to derive its optimal solutions. We then use the new DEA model to calculate and compare the efficiency scores of hospitals in Massachusetts and Connecticut pre- and post-Reform. The analysis shows that the Reform has achieved its cost containment and quality improvement goals at the same time. These analyses provide potentially useful information to hospital regulators and government regulators, especially in light of the national interest on health care legislation.

Using data envelopment analysis-neural network model to evaluate hospital efficiency

Due to an increasing amount of public resources dedicated to healthcare systems, it is important to measure the efficiency of hospitals. Systematically analysing hospital systems is one important way to discover and improve inefficiencies. The purpose of this study is to propose a data envelopment analysis (DEA)–artificial neural network (ANN)-based model to measure and evaluate the efficiency scores of hospitals. DEA is straightforward but requires time, knowledge and more process time than ANN. By combining these two methods, it is possible to lessen the shortcomings of DEA. In the proposed model, DEA classifies each hospital as either efficient or inefficient. Input and output variables of DEA are used for the inputs, and the efficiency scores of the hospitals are defined as the outputs of the ANN system. After the system is trained, the ANN model is applied to the test data to classify each hospital into efficient or inefficient. The results are then compared with each other, and discriminant analysis...

Hospital electronic medical record enterprise application strategies

Successful implementations and the ability to reap the benefits of electronic medical record (EMR) systems may be correlated with the type of enterprise application strategy that an administrator chooses when acquiring an EMR system. Moreover, identifying the most optimal enterprise application strategy is a task that may have important linkages with hospital performance. This study explored whether hospitals that have adopted differential EMR enterprise application strategies concomitantly differ in their overall efficiency. Specifically, the study examined whether hospitals with a single-vendor strategy had a higher likelihood of being efficient than those with a best-of-breed strategy and whether hospitals with a best-of-suite strategy had a higher probability of being efficient than those with best-of-breed or single-vendor strategies. A conceptual framework was used to formulate testable hypotheses. A retrospective cross-sectional approach using data envelopment analysis was used to obtain efficiency scores of hospitals by EMR enterprise application strategy. A Tobit regression analysis was then used to determine the probability of a hospital being inefficient as related to its EMR enterprise application strategy, while moderating for the hospital's EMR "implementation status" and controlling for hospital and market characteristics. The data envelopment analysis of hospitals suggested that only 32 hospitals were efficient in the study's sample of 2,171 hospitals. The results from the post hoc analysis showed partial support for the hypothesis that hospitals with a best-of-suite strategy were more likely to be efficient than those with a single-vendor strategy. This study underscores the importance of understanding the differences between the three strategies discussed in this article. On the basis of the findings, hospital administrators should consider the efficiency associations that a specific strategy may have compared with another prior to moving toward an enterprise application strategy.