An analysis of hospital efficiency and productivity growth using the Luenberger indicator

Can performance-based financing improve efficiency of health centers in Ethiopia? A Malmquist Productivity Index analysis

INTRODUCTION

The Ethiopian government has introduced several healthcare financing reforms intending to improve efficiency. Piloting implementation of performance-based financing is one of these actions. The purpose of this research is to assess the efficiency of healthcare facilities that have implemented performance-based financing compared to those that have not.

METHODS

Efficiency was measured using a nonparametric data envelopment analysis and the Malmquist Productivity Index technique. Total factor productivity change, technical change, and technological change are compared across eight sampled healthcare facilities that are implementing performance-based financing and eight that are not in Ethiopia.

RESULTS

Health facilities implementing performance-based financing have a mean technical efficiency score of 64%, allowing for a potential 36% reduction in inputs without affecting outputs. Their scale efficiency is 88%, indicating a potential 12% increase in total outputs without expanding facilities. In contrast, facilities not implementing performance-based financing have a mean technical efficiency score of 62%, with a potential for 38% input reduction without affecting outputs. Their scale efficiency is 87%, suggesting a potential 13% increase in total outputs without scaling up facilities. Among the 16 healthcare facilities observed, seven experienced a decline in the mean total productivity, while one remained stagnant. The remaining eight facilities witnessed an increase in productivity. The healthcare facilities implementing performance-based financing showed a 1.3% decrease in mean total productivity during the observed period. Among them, five showed an increase and three showed a decrease in the total factor of productivity. The mean total factor of productivity of all healthcare facilities not implementing performance-based financing remained stagnant over the three-year period (2019-2021), with four showing an increase and four showing a decrease in total productivity.

CONCLUSIONS

The study concludes that implementing performance-based financing did not improve productivity levels among healthcare facilities over three years. In fact, productivity decreased among the facilities implementing performance-based financing, while those not implementing it remained stagnant. This shows health facilities that implement performance-based financing tend to utilize more resources for similar outputs, contradicting the anticipated efficiency improvement.

Efficiency and Productivity Change of Public Hospitals in Panama: Do Management Schemes Matter?

In Latin American and Caribbean countries, the main concern of public health care managers has been traditionally placed on problems related to funding, payment mechanisms, and equity of access. However, more recently, there is a growing interest in improving the levels of efficiency and reducing costs in the provision of health services. In this paper we focus on measuring the technical efficiency and productivity change of public hospitals in Panama using bootstrapped Malmquist indices, which allows us to assess the statistical significance of changes in productivity, efficiency, and technology. Specifically, we are interested in comparing the performance of hospitals belonging to the two different management schemes coexisting in the country, the Social Security Fund (SSF) and the Ministry of Health (MoH). Our dataset includes data about 22 public hospitals (11 for each model) during the period between 2005 and 2015. The results showed that the productivity growth of hospitals belonging to the SSF has been much higher than that of the hospitals belonging to the Ministry of Health over the evaluated period (almost 4% compared to 1.5%, respectively). The main explanation for these divergences is the superior growth of technological change in the former hospitals, especially in the final years of the evaluated period.

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

Purpose

This 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/approach

The 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.

Findings

Upon 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 implications

The 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/value

The 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.

Assessing Productivity Development of Public Hospitals: A Case Study of Shanghai, China

As the main provider of medical services for the general public, the productivity changes of public hospitals directly reflect the development of the healthcare system and the implementation effect of medical reform policies. Using the dataset of 126 public hospitals in China from 2013 to 2018, this paper improves the existing literature in both index selection and model formulation, and examines public hospitals' total factor productivity (TFP) growth. Empirical results not only demonstrate the trend of productivity development but also point out the directions in how to improve the current running status. Our study demonstrates that there were no obvious productivity fluctuations in public hospitals during the recent observing years, indicating that the performance of China's public health system was generally acceptable in coping with fast-growing medical demand. However, the effect of public hospital reform has not been remarkably shown; thus, no significant productivity improvement was observed in most hospitals. Tertiary hospitals witnessed a slight declining trend in TFP, while secondary hospitals showed signs of rising TFP. To effectively enhance the overall performance of public hospitals in China, practical suggestions are proposed from the government and hospital levels to further promote the graded medical treatment system.

Integration of Data Envelopment Analysis and Clustering Methods

Data Envelopment Analysis (DEA) has been applied creatively in various study domains to compare and evaluate different Decision Making Units (DMUs) based on multiple input–output attributes. In this paper, the performance of Jordanian public hospitals is assessed via a methodology combining DEA with data mining methods, specifically, clustering. Initially, inputs of inefficient hospitals were altered to check for waste in the allocated resources. Then, the number of inputs–outputs was manipulated to test if the number is strongly influencing the productivity of the DMUs. The number of DMUs used was 27 public hospitals and the applicable efficiency measurements used were constant return to scale (CRS) and variable return to scale (VRS) through the DEAP software. Experiments showed that the efficiency of a hospital might be more meaningfully assessed if it is compared with a group of hospitals that are similar in some factors. More specifically, results of applying the CRS model proved that 77% of the hospitals were efficient. Additionally, we found that the inefficiencies of some hospitals are linked to weak resource utilization. It is concluded that number of inputs–outputs inserted in the efficiency evaluation process impacts the resulted values.

Determinants of efficiency growth of county-level public hospitals-evidence from Chongqing, China

BACKGROUND

The reform of county-level public hospitals is a breakthrough in the new era of healthcare reform in China and has attracted considerable attention since 2012. Continuous and efficient operations of hospital are primary concerns of this reform. To ensure the effectiveness of county-based intervention reform measures in Chongqing, it is significant to understand how hospital and county characteristics are associated with county-level public hospital efficiency due to significant development differences between counties. This study identifies the trajectory of hospital efficiency over time and determinants. It will also provide preliminary references for advancing reform.

METHODS

This study employs data from the Chongqing Regional Health Information Platform, Chongqing Health and Family Planning Statistical Yearbook, and Chongqing Statistical Yearbook for 2012-2016. A three-level growth model is used to estimate the efficiency growth trajectories within the contexts of hospitals and counties.

RESULTS

The intra-hospital level factors that affect the initial efficiency include government financial assistance, daily charge per bed, total assets turnover, number of hospital healthcare technicians, and medical costs per 100-yuan medical income. Inter-hospital variance is explained by hospital type. Inter-county level factors affecting the growth rate include the number of healthcare technicians per 1000 people and population density of the county. The interaction effect of the number of hospital healthcare technicians, hospital type, and number of healthcare technicians per 1000 people on hospital efficiency growth is significant.

CONCLUSIONS

This study identifies determinants that contribute to efficiency changes in public county-hospitals over time by using a three-level growth model. The differences in efficiency are associated with intra-hospital, inter-hospital, and inter-county characteristics in Chongqing, which provides useful insight into government decision-making and the progress of reform. The stability and reasonable increase in the number of healthcare technicians in a county are the key factors that improve the efficiency. Further reform should focus on maternal and child healthcare hospitals for increasing investment and implementing government compensation.

Efficiency analysis of Indian banking industry over the period 2008–2017 using data envelopment analysis

Purpose

The purpose of this paper is to estimate the relative efficiencies of banks of the Indian domestic banking sector by employing various models of data envelopment analysis (DEA) using the panel data of the recent decade (2008–2017). The paper provides a comparative analysis of these models based on the efficiency outputs. It compares the performance of banks based on their ownership and sizes and studies the decade-long trend of productivity using Malmquist indices.

Design/methodology/approach

This paper estimates overall technical, pure technical and scale efficiencies of 21 public sector banks and 17 private banks. It compares the descriptive statistics of efficiency estimates found out through 18 different DEA models and compares them using two non-parametric statistical tests. It studies the difference in efficiencies based on ownership and size by applying the same statistical tests. It employs the Malmquist index method to study the technological and technical progress in the banks’ productivity over the decade of FY 2008–FY 2017.

Findings

During FY 2016–2017, only 9 out of 38 banks were overall technically efficient with the whole sample having a mean overall technical inefficiency of 5 percent with scale inefficiency contributing more than pure technical inefficiency. The comparative study ascertains that private sector and public sector banks (PSBs) possess efficiencies that are similar based on super-efficiency slack-based model – variable returns to scale and non-oriented, a model that the authors argue to be the most suitable for the real-life business banking scenarios whereas the private sector banks possess better efficiency than the PSBs. The Malmquist indices prove that private sector banks have a higher increase in productivity based on both technological progress and efficiency improvements whereas PSBs had a loss of efficiency and comparatively less improvement in technology.

Research limitations/implications

This study has a limitation of choosing a single model of inputs and outputs. Improved insights can be drawn by employing more models based on different inputs and outputs. Further, relevance of each input and output can be examined using a regression-based feedback mechanism (Ouenniche and Carrales, 2018). The influence of environmental factors on the efficiencies can be studied using second-stage regression models and the relationship between efficiency scores and financial ratios can be examined.

Originality/value

This study is based on the panel data of the recent decade (2008–2017) and provides insights into the efficiency scenario of the Indian banking industry and how it changed over the past decade, to the leadership of banks, the banking regulators and the policy makers. The comparative analysis of DEA models based on a sample of Indian banks is first of its kind in the Indian context and helps the researchers to select an appropriate model and delve into further research on the same.

Cross-efficiency evaluation in the presence of flexible measures with an application to healthcare systems

In recent years, most countries around the world have struggled with the consequences of budget cuts in health expenditure, obliging them to utilize their resources efficiently. In this context, performance evaluation facilitates the decision-making process in improving the efficiency of the healthcare system. However, the performance evaluation of many sectors, including the healthcare systems, is, on the one hand, a challenging issue and on the other hand a useful tool for decision- making with the aim of optimizing the use of resources. This study proposes a new methodology comprising two well-known analytical approaches: (i) data envelopment analysis (DEA) to measure the efficiencies and (ii) data science to complement the DEA model in providing insightful recommendations for strategic decision making on productivity enhancement. The suggested method is a first attempt to combine two DEA extensions: flexible measure and cross-efficiency. We develop a pair of benevolent and aggressive scenarios aiming at evaluating cross-efficiency in the presence of flexible measures. Next, we perform data mining cluster analysis to create groups of homogeneous countries. Organizing the data in similar groups facilitates identifying a set of benchmarks that perform similarly in terms of operating conditions. Comparing the benchmark set with poorly performing countries we can obtain attainable goals for performance enhancement which will assist policymakers to strategically act upon it. A case study of healthcare systems in 120 countries is taken as an example to illustrate the potential application of our new method.

Assessment of the impact of public hospital associations (PHAs) on the efficiency of hospitals under the ministry of health in Turkey with data envelopment analysis

This study aimed to reflect the efficiency of hospitals under the Ministry of Health (MoH) before and after the implementation of Public Hospital Associations (PHAs). This study covered 3 years before (2010-2012) and 3 years after (2013-2015) the implementation. The population of the study included 865 hospitals under the MoH providing health care services in 81 provinces. No sample was selected; all hospitals were included under the scope of study. The provinces were chosen as the decision-making unit. Changes in the efficiency values of hospitals were analyzed with the Malmquist Total Factor Efficiency Index. The results showed that, for the pre-PHAs period, 24 provinces in 2010, 27 provinces from 2010 to 2011, and 32 provinces from 2011 to 2012 were assessed as efficient. From 2012, the year of implementation, to 2013, the number of efficient provinces dropped to 16. However, 20 provinces were subsequently found to be efficient from 2013 to 2014, and 26 from 2014 to 2015. The efficiency scores average of all provinces were found to be 0.89, 0.92, 0.92, 0.82, 0.88, and 0.90, respectively. Further, the efficiency score average of the 3 years before PHAs (0.91 ± 0.09) was significantly higher than the score obtained 3 years after PHAs (0.86 ± 0.11; p < 0.05). In consideration of such finding, the PHAs established at the provinces were identified as hampering increased efficiency.

Technical efficiency of selected hospitals in Eastern Ethiopia

This study examines the relative technical efficiency of 12 hospitals in Eastern Ethiopia. Using six-year-round panel data for the period between 2007/08 and 2012/13, this study examines the technical efficiency, total factor productivity, and determinants of the technical inefficiency of hospitals. Data envelopment analysis (DEA) and DEA- based Malmquist productivity index used to estimate relative technical efficiency, scale efficiency, and total factor productivity index of hospitals. Tobit model used to examine the determinants of the technical inefficiency of hospitals. The DEA Variable Returns to Scale (VRS) estimate indicated that 6 (50%), 5 (42%), 3 (25%), 3 (25%), 4 (33%), and 3 (25%) of the hospitals were technically inefficient while 9 (75%), 9 (75%), 7 (58%), 7 (58%), 7 (58%) and 8 (67%) of hospitals were scale inefficient between 2007/08 and 2012/13, respectively. On average, Malmquist Total Factor Productivity (MTFP) of the hospitals decreased by 3.6% over the panel period. The Tobit model shows that teaching hospital is less efficiency than other hospitals. The Tobit regression model further shows that medical doctor to total staff ratio, the proportion of outpatient visit to inpatient days, and the proportion of inpatients treated per medical doctor were negatively related with technical inefficiency of hospitals. Hence, policy interventions that help utilize excess capacity of hospitals, increase doctor to other staff ratio, and standardize number of inpatients treated per doctor would contribute to the improvement of the technical efficiency of hospitals.

Hospital Policy and Productivity - Evidence from German States

Total factor productivity (TFP) growth allows for additional healthcare services under restricted resources. We examine whether hospital policy can stimulate hospital TFP growth. We exploit variation across German federal states in the period 1993-2013. State governments decide on hospital capacity planning (number of hospitals, departments, and beds), ownership, medical students, and hospital investment funding. We show that TFP growth in German hospital care reflects quality improvements rather than increases in output volumes. Second-stage regression results indicate that reducing the length of stay is generally a proper way to foster TFP growth. The effects of other hospital policies depend on the reimbursement scheme: Under activity-based (German Diagnosis-related Group) hospital funding, scope-related policies (privatization and specialization) come with TFP growth. Under fixed daily rate funding, scale matters to TFP (hospital size and occupancy rates). Differences in capitalization in East and West Germany allow to show that deepening capital may enhance TFP growth if capital is scarce. We also show that there is less scope for hospital policies after large-scale restructurings of the hospital sector. Copyright © 2016 John Wiley & Sons, Ltd.

Hong Kong Hospital Authority resource efficiency evaluation: Via a novel DEA-Malmquist model and Tobit regression model

The Hospital Authority (HA) is a statutory body managing all the public hospitals and institutes in Hong Kong (HK). In recent decades, Hong Kong Hospital Authority (HKHA) has been making efforts to improve the healthcare services, but there still exist some problems like unfair resource allocation and poor management, as reported by the Hong Kong medical legislative committee. One critical consequence of these problems is low healthcare efficiency of hospitals, leading to low satisfaction among patients. Moreover, HKHA also suffers from the conflict between limited resource and growing demand. An effective evaluation of HA is important for resource planning and healthcare decision making. In this paper, we propose a two-phase method to evaluate HA efficiency for reducing healthcare expenditure and improving healthcare service. Specifically, in Phase I, we measure the HKHA efficiency changes from 2000 to 2013 by applying a novel DEA-Malmquist index with undesirable factors. In Phase II, we further explore the impact of some exogenous factors (e.g., population density) on HKHA efficiency by Tobit regression model. Empirical results show that there are significant differences between the efficiencies of different hospitals and clusters. In particular, it is found that the public hospital serving in a richer district has a relatively lower efficiency. To a certain extent, this reflects the socioeconomic reality in HK that people with better economic condition prefers receiving higher quality service from the private hospitals.

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.

Has the medical reform improved the cost efficiency of Chinese hospitals?

After three decades of reform, the medical care system in China has experienced significant changes. However, the present research has not made a tentative evaluation of it to justify further reform. This paper analyses the cost efficiency of Chinese hospitals in 31 provinces during the period from 2002 to 2011 and adopts a Bayesian stochastic frontier model taking account of the identified heterogeneity according to the background of Chinese medical system reform, including the coastal location, 3A class hospital proportion, public subsidies and medical insurance reforms. It finds that the public subsidies and medical insurance reforms have improved the cost efficiency of Chinese hospitals, while the coastal location and 3A class hospital proportion have decreased the cost efficiency of Chinese hospitals. Therefore, these results imply that it will be beneficial for Chinese medical system to optimize the fiscal subsidies of public hospitals, encourage the entrance of private hospitals, improve the medical insurance coverage and set up the pre-triage system.

Did the corporatization of Portuguese hospitals significantly change their productivity?

This paper aims to investigate if the market structure reforms in the Portuguese health system have improved hospital performance and productivity. A robust non-parametric Malmquist index is applied to measure group performance. The significance of the results achieved is examined using a conditional and non-conditional subsampling bootstrapped-based methodology, enhanced by the likelihood cross validation criterion based on the k-nearest neighbors method. The sample contains information about 216 non-corporatized and 176 corporatized Portuguese hospitals for the period 2002–2009. Five models were applied, based on three study dimensions (internment, emergencies and doctor visits). The results show that although corporatized hospitals presented the highest efficiency consistency, they had also the lowest levels of productivity, while the hospitals under the traditional administrative public management system were the ones with the best average performance. However, several best practices were also found in all groups, being the limited companies were often dominated by both noncorporatized and public enterprise entities. Consistent output ranges where all groups present dominance over the others were also identified. It was possible to conclude that the more autonomy the hospital had from the Ministry of Health, the lower was its productivity.

Determinants of healthcare system's efficiency in OECD countries

OBJECTIVE

Firstly, to compare healthcare systems' efficiency (HSE) using two models: one incorporating mostly inputs that are considered to be within the discretionary control of the healthcare system (i.e., physicians' density, inpatient bed density, and health expenditure), and another, including mostly inputs beyond healthcare systems' control (i.e., GDP, fruit and vegetables consumption, and health expenditure). Secondly, analyze whether institutional arrangements, population behavior, and socioeconomic or environmental determinants are associated with HSE.

DESIGN

Data envelopment analysis (DEA) was utilized to calculate OECD countries' HSE. Life expectancy and infant survival rate were considered as outputs in both models. Healthcare systems' rankings according to the super-efficiency and the cross-efficiency ranking methods were used to analyze determinants associated with efficiency.

RESULTS

(1) Healthcare systems in nine countries with large and stable economies were defined as efficient in model I, but were found to be inefficient in model II; (2) Gatekeeping and the presence of multiple insurers were associated with a lower efficiency; and (3) The association between socioeconomic and environmental indicators was found to be ambiguous.

CONCLUSIONS

Countries striving to improve their HSE should aim to impact population behavior and welfare rather than only ensure adequate medical care. In addition, they may consider avoiding specific institutional arrangements, namely gatekeeping and the presence of multiple insurers. Finally, the ambiguous association found between socioeconomic and environmental indicators, and a country's HSE necessitates caution when interpreting different ranking techniques in a cross-country efficiency evaluation and needs further exploration.

Assessment of productivity of hospitals in Botswana: a DEA application

BACKGROUND

Botswana national health policy states that the Ministry of Health shall from time to time review and revise its organization and management structures to respond to new developments and challenges in order to achieve and sustain a high level of efficiency in the provision of health care. Even though the government clearly views assuring efficiency in the health sector as one of its leadership and governance responsibilities, to date no study has been undertaken to measure the technical efficiency of hospitals which consume the majority of health sector resources. The specific objectives of this study were to quantify the technical and scale efficiency of hospitals in Botswana, and to evaluate changes in productivity over a three year period in order to analyze changes in efficiency and technology use.

METHODS

DEAP software was used to analyze technical efficiency along with the DEA-based Malmquist productivity index which was applied to a sample of 21 non-teaching hospitals in the Republic of Botswana over a period of three years (2006 to 2008).

RESULTS

The analysis revealed that 16 (76.2 percent), 16 (76.2 percent) and 13 (61.9 percent) of the 21 hospitals were run inefficiently in 2006, 2007 and 2008, with average variable returns to scale (VRS) technical efficiency scores of 70.4 percent, 74.2 percent and 76.3 percent respectively. On average, Malmquist Total Factor Productivity (MTFP) decreased by 1.5 percent. Whilst hospital efficiency increased by 3.1 percent, technical change (innovation) regressed by 4.5 percent. Efficiency change was thus attributed to an improvement in pure efficiency of 4.2 percent and a decline in scale efficiency of 1 percent. The MTFP change was the highest in 2008 (MTFP = 1.008) and the lowest in 2007 (MTFP = 0.963).

CONCLUSIONS

The results indicate significant inefficiencies within the sample for the years under study. In 2008, taken together, the inefficient hospitals would have needed to increase the number of outpatient visits by 117627 (18 percent) and inpatient days by 49415 (13 percent) in order to reach full efficiency. Alternatively, inefficiencies could have been reduced by transferring 264 clinical staff and 39 beds to health clinics, health posts and mobile posts. The transfer of excess clinical staff to those facilities which are closest to the communities may also contribute to accelerating progress towards the Millennium Development Goals related to child and maternal health.Nine (57.1 percent) of the 21 hospitals experienced MTFP deterioration during the three years. We found the sources of inefficiencies to be either adverse change in pure efficiency, scale efficiency and/or technical efficiency.In line with the report Health financing: A strategy for the African Region, which was adopted by the Fifty-sixth WHO Regional Committee for Africa, it might be helpful for Botswana to consider institutionalizing efficiency monitoring of health facilities within health management information systems.