Inefficient Hospitals Research Articles

Do hospitalizations push households into poverty in India: evidence from national data.

High percentage of OOP (Out-of-Pocket) costs can lead to poverty and exacerbate existing poverty, with 21.9% of India's 1.324 billion people living below the poverty line. Factors such as increased patient cost-sharing, high-deductible health plans, and expensive medications contribute to high OOP costs. Understanding the poverty-inducing impact of healthcare payments is essential for formulating effective measures to alleviate it. The study used data from the 75th round of the National Sample Survey Organization (Household Social Consumption in India: Health) from July 2017-June 2018, focusing on demographic-socio-economic characteristics, morbidity status, healthcare utilization, and expenditure. The analysis included 66,237 hospitalized individuals in the last 365 days. Logistic regression model was used to examine the impact of OOP expenditures on impoverishment. Logistic regression analysis shows that there is 0.2868 lower odds of experiencing poverty due to OOP expenditures in households where there is the presence of at least one child aged 5 years and less present in the household compared to households who do not have any children. There is 0.601 higher odds of experiencing poverty due to OOP expenditures in urban areas compared to households in rural areas. With an increasing duration of stay in the hospital, there is a higher odds of experiencing poverty due to OOP health expenditures. There is 1.9013 higher odds of experiencing poverty due to OOP expenditures if at least one member in the household used private healthcare facility compared to households who never used private healthcare facilities. In order to transfer demand from private to public hospitals and reduce OOPHE, policymakers should restructure the current inefficient public hospitals. More crucially, there needs to be significant investment in rural areas, where more than 70% of the poorest people reside and who are more vulnerable to OOP expenditures because they lack coping skills.

Assessment of the Efficiency of Public Hospitals in Romania

Considering the high pressure on the healthcare limited resources, mainly on hospitals, determined by the population ageing, and the increased incidence of chronic and infectious diseases, it is essential to both decrease expenditures and provide good quality healthcare. In this paper we focus on the efficiency of Romanian public hospitals. Our research goals are to identify and examine the inefficient public hospitals in Romania; to determine sources of inefficiency in Romanian public hospitals; to describe a potential reduction in all inputs on average to rationalize hospital resources; and recommend that hospital management be improved. We propose an approach that contains preliminary data analyses to obtain homogeneous distributions, then we use Data Envelopment Analysis to estimate the technical efficiency scores for the hospitals in the sample. The results showed that more than half of the examined small hospitals were technically inefficient and that they could have produced a larger number of discharges and consequently an increased number of inpatient days. Possible reductions in inputs were also indicated. These results suggest ways of improving hospital management and restructuring and reorganizing decisions that can be implemented in the hospital network.

Evaluating the technical efficiency of neonatal health service among primary hospitals of northwest Ethiopia: Using two-stage data envelopment analysis and Tobit regression model.

Most cases of neonatal mortality are preventable but a substantial number of cases get complicated and become irreversible not only due to scarcity but also due to inefficient utilization of available resources in the health service. However, limited evidence shows the efficiency level of health facilities in Ethiopia. Therefore, this study aimed to assess the technical efficiency of neonatal health service and its associated factors among primary hospitals in three zones of Northwest Ethiopia. A facility-based cross-sectional study was conducted among fifteen randomly selected primary hospitals from March 17 to April 17, 2021. Input data on non-salary recurrent costs, salary costs, and the number of beds, and output data on neonatal admissions, outpatient visits, and referrals for the 2019/20 fiscal year were collected using a document review. In the first stage of analysis, input-oriented data envelopment analysis with a variable return-to-scale assumption was employed to estimate the efficiency scores using DEAP 2.1. A Tobit regression model was fitted in the second stage to identify the associated factors with technical efficiency. Variables with a p-value <0.05 were declared as statistically associated factors. In this study, 80% of the primary hospitals were pure technical efficient whereas 46.67% of the facilities were scale efficient with all of the scale inefficient hospitals operating below their scale. The mean pure technical and scale efficiency score of primary hospitals was 0.948±0.113 and 0.887±0.143, respectively. Total catchment population, incentive packages for the clinical staff, and the educational status of the manager were positively associated with the technical efficiency of hospitals. On the other hand, technical efficiency was negatively associated with the presence of a health facility that provides neonatal health services near the hospital and the distance of the manager's residence. Though most of the primary hospitals in Northwest Ethiopia were technical efficient, more than half of them were working below their scale of operation. Our results also indicated that introducing the performance-based provision of incentive packages for clinical staff and employing master's and above-educated health professionals as a manager might improve the efficient utilization of resources in primary hospitals.

Assessing the impact of COVID-19 on the efficiency of Portuguese state-owned enterprise hospitals

This paper uses Value-Based Data Envelopment Analysis (VBDEA), to assess the impact of the COVID-19 pandemic on the efficiency of 37 state-owned enterprises (SOE) hospitals by employing data publicly available from the Portuguese Health Service database between January and November 2019 and 2020, respectively. Furthermore, a productivity index (specifically adjusted to the VBDEA approach) is also used that allows identifying which factors are behind the relative efficiency changes of these hospitals. The factors considered to perform the efficiency assessment of the Portuguese SOE hospitals include labour, capacity, and activity-related indicators. Out of the 37 SOE hospitals, 21 and 17 were efficient in 2019 and 2020, respectively. Irrespective of the value functions considered, the hospitals more often viewed as a reference for best practices were Santa Maria Maior, Tâmega e Sousa and Entre Douro e Vouga. Santa Maria Maior and Algarve were the only hospitals found to be robustly efficient for both years. Overall, the majority of SOE hospitals showed negative productivity (except for Évora and Santa Maria Maior) and all of them presented negative technological change, thus highlighting the massive impact that the COVID-19 outbreak has had on the performance of these hospitals. An additional conclusion is that inefficient hospitals substantially increased all their resources in 2020 as compared to inefficient hospitals in 2019, suggesting that the inefficiency of these hospitals was not due to the lack of resources. Finally, irrespective of the model employed, the hospitals located in the Portuguese northern region were more resilient to the COVID-19 crisis. All in all, to become more resilient (even for future COVID-19 outbreaks), hospitals should undertake changes that are advantageous irrespective of the obstacles they face and that are even beneficial during normal times. A culture of cooperation within and across hospitals should also be cultivated, which allows exchanging resources where they can be used more efficiently.

A study of the evacuation and allocation of hospital beds during the Covid-19 epidemic: a case study in Iran

BackgroundShortage of resources, such as hospital beds, needed for health care especially in times of crisis can be a serious challenge for many countries. Currently, there is no suitable model for optimal allocation of beds in different hospital wards. The Data Envelopment Analysis method (DEA) has been used in the present study to examine the evacuation and allocation of hospital beds during the covid-19 pandemic in order to contribute to effective planning for fighting the spread the covid-19 virus.MethodsThe present study was conducted in two stages in hospitals affiliated with Urmia University of Medical Sciences (UUMS) in 2021. First, the number of excess beds was determined by calculating the technical efficiency using the DEA method and Deap2.1 software. To reallocate excess beds to covid-19 patients, the types of hospital wards were considered. As a result of this analysis, the inefficient hospitals with excess beds in different wards, which could be used for covid-19 patients with more serious symptoms, were identified.ResultsThe results of the study show that the average technical efficiency of the studied hospitals was 0.603. These hospitals did not operate efficiently in 2021 and their current output can be produced with less than 61% of the used input. Also, the potential of these hospitals, over a period of 1 year, for the evacuation of beds and reallocation of them to covid-19 patients was calculated to be 1781 beds, 450 of which belonged to general wards and 1331 belonged to specialized wards.ConclusionsThe DEA method can be used in the allocation of resources in hospitals. Depending on the type of hospital wards and the health condition of patients, this method can help policy-makers identify hospitals with the best potential but less emergency services for the purpose of reallocation of resources, which can help reduce the severe effects of crises on health resources.

Efficiency and Optimal size of Italian Public Hospitals: Results from Data Envelopment Analysis

Background: One of the major issues discussed in the literature about hospital efficiency is the impact of hospital size on technical efficiency. Many studies have observed that in some cases, hospital technical inefficiency is correlated with an incorrect size. This paper addresses this topic. In particular, we attempt to identify an optimal size of hospitals in terms of beds.&#x0D; Methods: The study is organized as follow: first, we performed the Data Envelopment Analysis in order to calculate the technical and scale efficiency scores for a sample of 41 Italian public hospitals during the period 2010-2013; second, we investigated the impact of size on hospital efficiency, identifying the magnitudes of input reductions needed to make inefficient public hospitals efficient. Finally, we calculated the most productive scale size for each hospital in the sample. According to these results, through an overall observation, we attempted to identify an optimal size for hospitals in terms of beds.&#x0D; Results: Most of the hospitals were inefficient and most of the inefficiency was the results of the presence of waste in terms of input resources. During the period considered, we found that inputs could be reduced by 22% on average. Economies of scale were found around 200 beds for 20.000 discharges per year.&#x0D; Conclusions: The identification of an optimal size of hospitals in terms of beds still requires further efforts in the literature. However, this study contributes to support hospital managers in resource allocation choices through a quantitative approach.

The Efficiency Evaluation of Municipal-Level Traditional Chinese Medicine Hospitals Using Data Envelopment Analysis After the Implementation of Hierarchical Medical Treatment Policy in Gansu Province, China.

Background: Efficiency evaluation is an integral part of new medical reform and is necessary to solve the problem of limited and unbalanced medical resources. This study evaluated the efficiency of municipal-level Traditional Chinese Medicine hospitals by Data Envelopment Analysis application after a hierarchical medical treatment policy was implemented. We propose solutions to the problems existing in hospital operations and promote the utilization efficiency of medical resources in those hospitals. Methods: The sample included all municipal-level TCM hospitals in Gansu province from 2017 to 2019. The DEA-BCC model was employed to evaluate the relative efficiency of hospital operations, and the Manny-Whitney test was used to compare the input and output variables of technical efficiency efficient and inefficient hospitals. Results: From 2017 to 2019, the growth in the number of staff in secondary hospitals (25.88%) was lower than that in tertiary hospitals (31.98%). However, the increase in the number of beds (16.52%) in secondary hospitals was higher than that in tertiary hospitals (−0.30%). 5 (38.46%) achieved DEA efficient in secondary hospitals and 2 (40.00%) in tertiary hospitals. The means of technical efficiency, pure technical efficiency, and scale efficiency in secondary hospitals were 0.812, 0.887, and 0.908, respectively. The means in tertiary hospitals were 0.868, 0.926, and 0.935, respectively. The hospital areas were statistically different between the TE efficient and inefficient hospitals (P<0.05) in secondary hospitals. However, the number of outpatients between the two groups was statistically different (P<0.05) in tertiary hospitals. Conclusion: In this study, the medical and health services of municipal TCM hospitals in Gansu Province have made great progress. Due to the backward economy of Gansu Province, the classification of diagnosis and treatment of diseases was still based on Western medicine, resulting in the slow medical development of some municipal TCM hospitals. TCM hospitals should improve management efficiency, optimize hospital operation scale, improve the utilization efficiency of medical resources and promote efficient hospital development.

Spatial reorganization of the Brazilian Unified National Health System's inpatient care supply.

The joint provision of efficient and equitable healthcare service delivery is a critical factor in improving social welfare. However, healthcare services pose a particular challenge when balancing healthcare provider efficiency and equity. Typically characterized by economies of scale and scope, inpatient care involves a wide variety of medical care that usually demands a broad range of health professional expertise and technological complexity to ensure health care quality. This study analyzes the current spatial organization of the Brazilian general hospitals and their respective flow of patients to identify the possible benefits of closing inefficient hospitals. We studied how inpatient care referrals may be reallocated without increasing access inequities following the potential closure of inefficient public hospitals. We used data from the Brazilian Hospital Information System of the Brazilian Unified National Health System (SIH/SUS) and the Brazilian National Register of Health Establishments (CNES). The smallest and least efficient hospitals were selected as units for potential closure, conditioned on an optimization criterion that minimizes patient travel distances to the nearest efficient hospital. Our results show that there is room for hospital resource reorganization in Brazil without compromising health care access equity.

Privatization, Corporatization, and Public-Private Partnership in the Kingdom of Saudi Arabia.

There are different governmental reasons and technologies for seeking public-private partnerships throughout the developed countries. Significant motives have been to improve efficiency and risk distribution in comparison to traditional financing techniques and to lessen budget and borrowing limits. In this study, the movement toward privatization enhancing the efficiency of MoH’s hospitals has been assessed using Data Envelopment Analysis (DEA) from 1979 to 2020. Moreover, the efficiencies of the individual hospital were estimated through the DEA model, which includes: scale efficiency (SE), pure technical efficiency (PTE), and overall technical efficiency (OTE). In addition, factor affecting such efficiencies was analyzed through Tobit regression. The VRS results suggest that the numbers of hospitals benefiting from the complete corporatized phase are greater than those benefiting from the last phase (and vice versa in the case of DRS). The variance between inefficient hospitals in the less corporatized phase (2000-2020) was more prominent than in the fully corporatized phase (1988-1999). In conclusion, fully corporatized hospitals (on average) achieve relatively better overall efficiency. It is recommended that additional corporatization might be stimulated by a standardized set of performance measures, which cover both the quality criteria and economic efficiency measurements from a healthcare perspective.

Inefficiency of public hospitals: a multistage data envelopment analysis in an Italian region

BackgroundThe objective of this study was to assess public hospital efficiency, including quality outputs, inefficiency determinants, and changes to efficiency over time, in an Italian region. To achieve this aim, the study used secondary data from the Veneto region for the years 2018 and 2019.MethodsA nonparametric approach—that is, multistage data envelopment analysis (DEA)—was applied to a sample of 43 hospitals. We identified three categories of input: capital investments (Beds), labor (FTE), operating expenses. We selected five efficiency outputs (outpatient visits, inpatients, outpatient visit revenue, inpatient revenue, bed occupancy rate) and two quality outputs (mortality rate and inappropriate admission rate). Efficiency scores were estimated and decomposed into two components. Slack analysis was then conducted. Further, DEA efficiency scores were regressed on internal and external variables using a Tobit model. Finally, the Malmquist Productivity Index was applied.ResultsOn average, the hospitals in the Veneto region operated at more than 95% efficiency. Technical and scale inefficiencies often occurred jointly, with 77% of inefficient hospitals needing a downsizing strategy to gain efficiency. The inputs identified as needing significant reductions were full-time employee (FTE) administrative staff and technicians. The size of the hospital in relation to the size of the population served and the length of patient stay were important factors for the efficiency score. The major cause of decreased efficiency over time was technical change (0.908) rather than efficiency change (0.974).ConclusionsThe study reveals improvements that should be made from both the policy and managerial perspectives. Hospital size is an important feature of inefficiency. On average, the results show that it is advisable for hospitals to reorganize nonmedical staff to enhance efficiency. Further, increasing technology investment could enable higher efficiency levels.

Assessing the performance of hospitals before and after the implementation of Iran's Health Sector Evolution Plan (HSEP) using the Pabon Lasso model.

Background: The health sector evolution plan was implemented in 2014 in government hospitals across the country as a part of the universal health coverage achievement programs. This study assessed the performance of hospitals before and after the implementation of this plan, using the Pabon Lasso model. Methods: The population of this study consisted of the hospitals of the country in the 2013-2015 time frame; overall, 874 hospitals (94.5% of the population) were included in the study. In order to assess performance, we used the Pabon Lasso model and hospital performance indicators (Average Length of Stay, Bed Turnover, and Bed Occupancy Rate). The data were collected from the Hospital Information System and provincial deputies of curative affairs and were then analyzed using the descriptive indicators of mean, frequency, and median in SPSS 22. Also, Paired Student T-test and ANOVA were used to compare the performance of different groups of hospitals before and after the implementation of the health sector evolution plan. Results: The implementation of the health sector evolution plan has led to a significant improvement in the three performance indicators in the hospitals of the country. Before the implementation of the health sector evolution plan, the most inefficient, inefficient, fairly efficient, and most efficient zones included 31%, 18%, 17%, and 33% of the studied hospitals, respectively. However, the implementation of the health sector evolution plan changed the percentages to 29%, 21%, 20%, and 30%, respectively. Teaching hospitals, which are governmental and are mostly located in capital cities of the provinces, were overall more inefficient than non-teaching hospitals. Conclusion: The number of the most efficient and most inefficient hospitals has decreased, and the number of average performance hospitals has increased after the implementation of the health sector evolution plan. Therefore, the health sector evolution plan has not led to an overall increase or decrease in the performance of hospitals but has reduced the difference in the performance of hospitals. Equal support of government hospitals along with financial protection against health expenses, improves the performance indicators of hospitals and reduces performance differences among them.

A New Model to Analyze the Efficiency of a Multilevel Drug Supply Network for Hospitals

It is of special importance to procure medicines as strategic commodities. The purpose of this study is to manage a drug supply network using a non-radial data envelopment analysis (DEA) model. This study is conducted in private hospitals in Tehran. In this regard, a drug supply chain at three levels of suppliers, distributors, and consumers (pharmacies and hospitals) is examined. Indicators and limitations of the network are identified and then the appropriate model is formulated using mathematical equations on the basis of a non-radial DEA model. In order to validate the proposed model, data related to 30 hospitals during 2019 and 2020 is evaluated. According to this model, 5 hospitals had efficient supply networks and 25 hospitals had deficient supply networks. The proposed new model is able to evaluate the multilevel supply network and intermediate components in hospitals. The problems of the drug supply network are due to the increase in demands for the time period under consideration and the existence of inconsistencies in the drug supply chain in hospitals, which has led to an increase in inefficient hospitals.

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 &lt; 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.

Changes in hospital efficiency and size: An integrated propensity score matching with data envelopment analysis

Turkey has made huge investments in city hospitals. The distinguishing feature of these hospitals is that they are physically large. Although many studies have investigated the efficiency of public hospitals, there are a few studies on the effect of hospital size on efficiency. This study examines the effect of hospital size on changes in public hospital efficiencies. The analysis is made up of three steps. First, using a bootstrap data envelopment analysis (DEA), the pure efficiency scores of each hospital were calculated. Second, propensity score matching (PSM) was used to ensure that any differences could be attributed to a particular class of hospital size, and not be due to differences in sample characteristics between the intervention and control groups. To highlight a potential time difference between small and large hospitals, the efficiencies of hospitals were examined from 2014 to 2017. Third, the Mann–Whitney U test was used to conduct a robustness check of the DEA and PSM results. Fourth, logistic regression was used on balanced data to examine the determinants of the efficiency of public hospitals. There are remarkable differences in the results obtained before and after matching the groups based on the bed-occupancy rate. Additionally, urban location is a key predictor of efficient and inefficient hospitals. This study also highlights that integrating DEA and PSM is a useful strategy in accurately identifying predictors of efficiencies of hospitals by creating balanced groups. Health policymakers should consider the efficiency advantages of high workload and service burden in the planning of public hospitals.

Efficiency Measurement for Public Hospitals by Data Envelopment Analysis: An Empirical Study in Artvin Province

Bu araştırmada veri zarflama analizi (VZA) yöntemi kullanılarak, Artvin ilinde sağlık hizmeti veren kamu hastanelerinin etkinlik düzeylerinin belirlenmesi amaçlanmıştır. Bu amaçla 6 adet hastanenin 2016 ve 2017 yıllarındaki üçer adet girdi ve çıktı değişkenine ait veriler dikkate alınmıştır. VZA modeli olarak girdileri minimize etmeyi amaçlayan, ölçeğe göre sabit getiri varsayımına dayanan girdi yönelimli Charnes-Cooper-Rhodes (CCR) modeli kullanılmıştır. Araştırma sonucunda, 2016 yılında 4 kamu hastanesinin, 2017 yılında ise 3 kamu hastanesinin etkin olduğu tespit edilmiştir. Araştırmada aynı zamanda etkin olmayan hastanelerin etkin hastaneleri referans alarak etkin olabilmeleri için yapması gereken iyileştirmelere yer verilmiştir. Ortaya koyduğu bulgularıyla çalışmanın kamu hastanelerinde yönetimsel kararların alınması süreçlerinde karar verici pozisyonunda olan yöneticilere yol gösterici bilgiler sunması beklenmektedir.

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...

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.

Efficiency evaluation of public hospitals in Saudi Arabia: an application of data envelopment analysis

ObjectiveIn this study, we assess the performance of public hospitals in Saudi Arabia. We detect the sources of inefficiency and estimate the optimal levels of the resources that provide the...

Optimizing payments based on efficiency, quality, complexity, and heterogeneity: the case of hospital funding

AbstractIn several countries around the world, public hospitals are paid for by the health care services they provide to the population. Different funding systems are available, such as the Beveridge Model. Furthermore, payments can be either prospective, retrospective, or even a merging of both. Payments are usually nonoptimal in the Pareto sense and disregard the quality of delivered services as well as the environment (either internal or external) in which hospitals operate. This gap may introduce inefficiencies and inequities in the health care system, which currently faces scenarios of reduction/scarcity of resources. Furthermore, there are problems related to overpaying and misallocation of resources. Hence, payments should be optimized to guarantee an efficient allocation of resources to the health care systems. This study proposes a new best practice based tool, applying it to the Portuguese National Health Service. Considerable cost savings may emerge for inefficient hospitals if the model is successfully employed.

Integrated k-means clustering with data envelopment analysis of public hospital efficiency.

The goal of this study is to integrate k-means clustering with data envelopment analysis to examine technical efficiencies in public hospitals in Turkey. A two-step analysis procedure involving provinces and public hospitals is applied in this study. The first step examines similar provinces in terms of welfare state indicators by using k-means clustering and silhouette (Sil) cluster validity index measures. Then, the efficiencies of public hospitals in different groups of provinces are determined. The data are taken from the Turkish Statistical Institute and the 2017 Public Hospitals Statistical Year Book for eighty-one provinces and 688 public hospitals. Study results show that, relative to similarities of welfare state indicators, there are five province groups (Sil = .58). The number of technically inefficient public hospitals is greater than the number of technically efficient public hospitals in all groups. Study results emphasize that incorporated methodology of k-means clustering with data envelopment analysis is useful to identify efficiencies of public hospitals located in provinces that have similar welfare status.