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The Measurement of Efficiency and Productivity of Health Care Delivery
Abstract
The measurement of efficiency and productivity of health service delivery has become a small industry. This is a review of 317 published papers on frontier efficiency measurement. The techniques used are mainly based on non-parametric data envelopment analysis, but there is increasing use of parametric techniques, such as stochastic frontier analysis. Applications to hospitals and other health care organizations and areas are reviewed and summarised, and some meta-type analysis undertaken. Cautious conclusions are that public provision may be potentially more efficient than private, in certain settings. The paper also considers conceptualizations of efficiency, and points to dangers and opportunities in generating such information. Finally, some criteria for assessing the use and usefulness of efficiency studies are established, with a view to helping both researchers and those assessing whether or not to act upon published results.
... By contrast, bureaucratic inefficiencies and lack of financial incentives cause delays in technological adoption in healthcare systems running weak institutional environments (Kaufmann et al., 1999). For example, hospitals' efficiency, patient outcomes, and cost-effectiveness have reportedly improved in OECD nations prioritizing AI-driven decision-making tools (Hollingsworth, 2008). ...
... DEA lets policymakers benchmark public organizations, including hospitals, municipalities, and schools, by building an efficiency frontier based on best-performing decision-making units (DMUs) (Charnes et al., 1978). This approach has been widely applied in healthcare (Emrouznejad and Dey, 2011), education (Coelli et al., 2005), and government administration (Hollingsworth, 2008). Over time, researchers have refined DEA methodologies, introducing bootstrapped DEA to enhance statistical robustness and network DEA to model multi-stage processes (Coelli et al., 2005). ...
... Despite its popularity, DEA has limitations, especially in its deterministic form, which assumes that all deviations from the efficiency frontier are caused by inefficiencies rather than external factors (Kumbhakar et al., 2000). It also supposes that all DMUs run under similar conditions, which presents a difficulty in various public sector settings (Hollingsworth, 2008). Researchers have combined DEA with parametric methods, including SFA, to overcome these restrictions and improve efficiency evaluations (Kumbhakar and Lovell, 2000;Tsionas, 2003;Boyd and Lee, 2019). ...
This study investigates the impact of formal institutional factors on public sector efficiency, focusing on environmental and healthcare performance—two of the most widely examined sectors in the public administration literature. The empirical analysis is based on a panel of 139 countries—33 developed and 106 developing—covering the period from 2012 to 2020. The analysis adopts a three-stage methodological framework comprising: (a) Bayesian Data Envelopment Analysis (DEA) to estimate efficiency scores, (b) Principal Component Analysis (PCA) to construct a composite Public Sector Efficiency Index, and (c) a two-step Generalized Method of Moments (GMM) approach to evaluate the influence of institutional variables, country classification (developed vs. developing), and their interactions on public sector efficiency. The findings highlight the importance of sustained efforts to reduce CO₂ emissions and manage healthcare expenditures effectively. The results underscore the critical role of strengthening formal institutions and enhancing the Human Development Index (HDI) to improve public sector efficiency.
... Hence, the input and output variables should be carefully considered when using the DEA to measure the effectiveness of a DMU or an organisation. This suggestion indicates that a precise, thorough, pertinent, and appropriate selection and combination of the input and output variables is necessary to effectively portray the functionality of a hospital while meeting the stakeholders' expectations and assessing its efficiency [18, 21]. Numerous advanced analyses have also been incorporated into DEA, such as the advanced CCR and BCC models, longitudinal or window analysis (Malmquist index), and statistical analysis (regression and bootstrapping methods) [20, 23,[25][26][27]]. ...
... These articles have also included the DEA for hospitalbased applications. Generally, DEA-based applications involve health care performance measurement [15,16,18], categorisation or clustering of DEA techniques [20, 33], DEA comparison with other methods, countries or durations [28, 29, 30, 34], and development of novel knowledge and approaches concerning DEA assessment [17, 21]. Likewise, each stage in a systematic literature review (SLR) employs organised, transparent, and reproducible techniques to identify and integrate relevant articles to a particular topic comprehensively. ...
... Currently, the DEA and SFA approaches are frequently applied to measure the efficiency of the healthcare industry [11]. Since the publication of Nunamaker's study, these strategies have been widely used in healthcare settings over the past 40 years [15][16][17][18][19]. Although the theoretical and methodological limitations have been acknowledged in DEA, this method has attracted interest from researchers who aim to address the limitations. ...
The efficiency and productivity evaluation process commonly employs Data Envelopment Analysis (DEA) as a performance tool in numerous fields, such as the healthcare industry (hospitals). Therefore, this review examined various hospital-based DEA articles involving input and output variable selection approaches and the recent DEA developments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was utilised to extract 89 English articles containing empirical data between 2014 and 2022 from various databases (Web of Science, Scopus, PubMed, ScienceDirect, Springer Link, and Google Scholar). Furthermore, the DEA model parameters were determined using information from previous studies, while the approaches were identified narratively. This review grouped the approaches into four sections: literature review, data availability, systematic method, and expert judgement. An independent single strategy or a combination with other methods was then applied to these approaches. Consequently, the focus of this review on various methodologies employed in hospitals could limit its findings. Alternative approaches or techniques could be utilised to determine the input and output variables for a DEA analysis in a distinct area or based on different perspectives. The DEA application trend was also significantly similar to that of previous studies. Meanwhile, insufficient data was observed to support the usability of any DEA model in terms of fitting all model parameters. Therefore, several recommendations and methodological principles for DEA were proposed after analysing the existing literature.
... Health Care em conjunto com Peacock, publicado em 2008 (23) . Outros trabalhos do autor como os artigos Non-Parametric and Parametric Applications Measuring Efficiency in Health Care (24) e The measurement of efficiency and productivity of health care delivery (25) são revisões de literatura acerca das publicações sobre eficiência em saúde. Hollingsworth (24)(25) aponta o crescimento das publicações que buscam medir a eficiência da saúde. ...
... Outros trabalhos do autor como os artigos Non-Parametric and Parametric Applications Measuring Efficiency in Health Care (24) e The measurement of efficiency and productivity of health care delivery (25) são revisões de literatura acerca das publicações sobre eficiência em saúde. Hollingsworth (24)(25) aponta o crescimento das publicações que buscam medir a eficiência da saúde. Porém, o autor faz um alerta sobre a necessidade de cautela para a interpretação dos resultados sobre a análise de eficiência. ...
... Dentre os insumos mais recorrentes estão as despesas, a mão de obra e a disponibilidade de estrutura física. Já como resultado são utilizados medidas de desempenho físico (consultas, dias de internação, número de atendimentos), mortalidade, expectativa de vida e qualidade do atendimento (23,24,25) . No contexto dos recursos públicos limitados, a análise da eficiência deve perseguir a otimização dos resultados. ...
A análise de redes sociais aplicada a produção cientifica pode colaborar para o entendimento da evolução do conhecimento e favorecer a interpretação. O campo de interesse da produção cientifica é a eficiência da saúde pública. O objetivo do artigo é analisar as redes sociais da produção cientifica em eficiência da saúde pública. O estudo foi realizado por meio da revisão sistemática de literatura e a análise das redes sociais da produção científica. Os resultados evidenciaram que a discussão sobre a eficiência da saúde pública não é um tema recente e vem acompanhando a própria evolução dos sistemas de saúde. Os autores de destaque foram Cooper, Charnes, Ozcan, Hollingswort, Rhodes, Banker e Fare, considerados autores intermediários do campo de pesquisa. A discussão ao longo do tema foi acompanhada pela discussão dos mecanismos de análise da eficiência, que também evoluíram ao longo do tempo. Pode-se compreender que a eficiência pode ser o caminho para a otimização dos recursos de modo a atender melhor às necessidades da população.
... Health economists have employed the frontier analysis using both parametric and non-parametric methods (Hollingsworth, 2003). Comprehensive reviews of existing literature on the productivity of healthcare institutions using non-parametric methods have been conducted (Hollingsworth, 2008;Kohl et al., 2019). Stochastic frontier analysis (SFA) has been extensively utilized in the healthcare sector (Hollingsworth et al., 1999;Jacobs, 2001). ...
... The literature on healthcare productivity and efficiency has seen a rapid increase, with 55% of studies published after 2000 (up to 2006). DEA is the framework used in 67% of these studies in some form as a primary or secondary analysis tool (Hollingsworth, 2008). Among these studies, 52% have treated hospitals as DMUs, and only a few (4%) have analyzed country-wise evaluations with thirty or more countries constituting the dataset. ...
Service quality is believed to influence the productive efficiency of firms, particularly in a service focused industry such as healthcare. However, there is mixed evidence in the literature of both positive and negative correlation (e.g., the cost drivers of care providers vis-à-vis capacity expansion for better quality of service) between quality and efficiency. To address this challenge, a two-phase data-driven analysis is undertaken. In the first stage, an output-oriented Data Envelopment Analysis is employed to model the interdependency between operational efficiency and service quality by assessing the allocation of the input resources for achieving these two objectives. While accounting for the external influences and avoiding the ‘best practice trap’ in the healthcare sector, a set of classification algorithms are used to quantify the impact of external factors on efficiency levels. The proposed model is empirically tested using healthcare data of 31 provinces of China for a period from 2013 to 2018. The results show that the efficiency scores in operational productivity and quality of service are 67% and 64%, respectively. The major source of inefficiency is the number of cases in observation rooms (almost 47%) followed by health examination (22%). The provinces are categorized into three classes (optimally chosen number of clusters) using K-means clustering. The second phase of the analysis starts with selecting a subset of relevant features from 33 explanatory variables using information gain and correlation analysis. The proposed two-phased integrated technique enhances the performance of healthcare services and provides a roadmap for improvement for inefficient regions.
... To obtain a broader scope, we extended the topic from Australia to several reference countries and regions, which share a similar healthcare system structure with high-level quality healthcare services, similar traditions of the British management system, and a similar level of economic development, i.e., the UK, Canada, New Zealand, and Hong Kong. 2 Several comprehensive reviews of comparable topics can be found in the literature. For example, Hollingsworth (2003Hollingsworth ( , 2008; Hollingsworth et al. (1999) broadly reviewed the studies of healthcare delivery efficiency, focusing on the application and development of efficiency measures, the main findings, the indicators of output and quality, etc. The measurements of efficiency, including the indicators of input and output of a healthcare facility and the approach to evaluate the utilization, were also among the main concerns of the reviews by Hussey et al. (2009);O'Neill et al. (2008); Worthington (2004). ...
... Subsequently, Hollingsworth (2008) reviewed a broader collection of 317 published studies. The widely preferred techniques have remained constant from prior years. ...
Along with the development of productivity and efficiency analysis techniques, extensive research on the performance of hospitals has been conducted in the last few decades. In this article, we conduct a systematic review supported by a series of bibliometric analyses to obtain a panoramic perspective of the research about the productivity and efficiency of hospitals—a cornerstone of the healthcare system—with a focus on Australia and its peers, i.e., the UK, Canada, New Zealand, and Hong Kong. We focus on the bibliometric data in Scopus from 1970 to 2023 and provide a qualitative and critical analysis of major methods and findings in selected published journal articles.
... Among these, DEA is used in 82% of frontier efficiency analyses. [21][22][23] In another systematic review, this proportion was even higher, reaching 90%. 24 Based on the above research, we are confident that DEA is a universal method for studying the efficiency of health systems. ...
Purpose
Since 2014, China has been rolling out a new autonomy reform for public hospitals, aiming to enhance their efficiency and better utilize the health budgets. The purpose of this research is to assess the reform’s success and explore its effects on hospital outcome efficiency, laying a foundation based on empirical evidence for future policy decisions.
Methods
The data envelopment analysis(DEA) and interrupted time-series analysis (ITSA) approaches were combined to analyze the reform impacts on the 16 samples of Shenzhen municipal public hospitals in China, using data extracted from the Shenzhen Health Statistics Yearbook from 2002 to 2023.
Results
The results revealed that from 2002 to 2023, 15 out of 16 sample hospitals achieved total factor productivity improvement in Shenzhen city of China, with the average growth rate of Malmquist total factor productivity index(MI) was 3.05% and the highest growth rate was 6.93%, yet only one hospital showing a growth rate of −0.02%. The results of ITSA show a significant intervention in 2014. After the policy intervention, the fixed reference Malmquist total factor productivity index(FRMI) for the general and the specialty hospital group increased at rates of 0.04680(P<0.000) and 0.1746(p<0.000) per year by the Newey-West model, similarly, the rates of 0.04689(P<0.000) and 0.1762(p<0.000) per year by the Prais-Winsten model.
Conclusion
The reform has positively impacted public hospitals’ total factor productivity(TFP). The TFP of the general hospitals was increasing before the policy intervention of autonomy hospitals, but the time of its implementation was associated with a more significant rise. Meanwhile, the TFP of specialty hospitals decreased before the intervention; however, its trend shifted to growth after the intervention. This research further emphasizes the applicability of the DEA-ITSA combination method as an effective tool for health policies evaluation using public data within China’s healthcare framework.
... DEA, which accounts for over 90% of applications in the healthcare sector, excels in handling multiple inputs and outputs, varying weights, and changes in returns to scale. [5][6][7] Previous studies have employed DEA techniques to measure the efficiency of COVID-19 pandemic control efforts in various states and union territories of India. 8 The Malmquist fuzzy DEA (Mal-FDEA) model approach has been employed to evaluate the technical efficiency of health products across Indian states. ...
Background
This study used data envelopment analysis (DEA), to assess relative efficiency of infection control in different clinical departments of the hospital for performance evaluation purposes.
Methods
All wards and departments from January to December 2022 were selected as decision units, and five input and two output indicators related to infection prevention and control were determined using DEA. Pure technical efficiency was evaluated using the Banker–Charnes–Cooper (BCC) model.
Results
In the study, the input-output indexes of the 27 clinical departments varied significantly. The average values of technical efficiency, pure technical efficiency, scale efficiency, and comprehensive benefit were 0.987, 0.995, 0.992, and 0.980, respectively. Among the 27 departments, 52% exhibited constant returns to scale, 44% showed increasing returns to scale, and 4% had decreasing returns to scale. In the context of DEA, 44% of the departments were classified as highly efficient, indicating that their input-output ratios had reached an optimal state. Meanwhile, 56% of the departments were identified as non-DEA efficient, suggesting that there was room for improvement in their input-output efficiency.
Conclusion
The improvement of input-output indexes of non-DEA effective clinical departments was defined by the BCC model. Use of DMUs could improve the efficiency of inventory control by optimizing the allocation of inventory control resources and refining inventory control measures.
... Data envelopment analysis is a classical non-parametric method for measuring the efficiency and productivity of a decision-making unit (DMU) [33]. Over the past decades, DEA has been widely applied to assess efficiency and performance in healthcare, education, operational research, and transport industries [34]. ...
Background
China started a pilot public hospital reform in 2012 to improve governance and efficiency in healthcare services delivery among county-level hospitals. This study aims to investigate the impact of the pilot reform on hospital efficiency and productivity by using a unique dataset of county hospitals in East China during 2009–2015.
Methods
A three-stage approach is used. First, this study uses the output-oriented data envelopment analysis (DEA) to estimate hospital efficiency with variable returns to scale. Second, propensity score matching is used to address potential biases associated with the selection of counties for the pilot program. In the third stage, we assess the impact of the pilot reform on efficiency by using a Tobit Difference-in-Differences approach.
Results
The average level of hospital efficiency for the whole sample experienced a rapid drop in 2013, then returned to a peak in 2014. Except in the reform year (2012), the overall hospital efficiency for the post-reform period is higher than that for the pre-reform period. The baseline model results show that the pilot reform is associated with a 3% decline in pure technical efficiency and a 2.3% increase in hospital scale efficiency, respectively. Our findings are robust when we apply bootstrapped DEA efficiency scores and use different specifications.
Conclusion
The findings of this study suggest no improvements in overall hospital efficiency associated with the pilot reform, possibly due to the combined effects of inefficient governance and hospital scale expansion. This study suggests that further efforts are needed to increase county hospital performance by strengthening management and optimizing resource utiliziation.
... DEA has been applied in, for example, research on banking (Emrouznejad et al., 2008), supply chains and agriculture (Emrouznejad & Yang, 2018), and to assess the efficiency of nonprofit and public sector organisations involved in the arts (Golden et al., 2012;Hong, 2014), education (Colbert et al., 2000;Coupet, 2018;Reichmann & Sommersguter-Reichmann, 2006), health services (Hollingsworth, 2008;Van der Wielen & Ozcan, 2015) and humanitarian assistance (Coupet & Berrett, 2019;Kim & Lee, 2018) on the basis they have many inputs and outputs (often without price). ...
Pressure on charities to explain their performance is pervasive. While data envelopment analysis (DEA) has been widely applied, its use in charity research is limited, possibly due to difficulties in obtaining beneficiary data. Utilising a conventional DEA model, we incorporate beneficiary information as independent variables to assess the relative efficiency of a sample of Irish charities. Four of our eight variables (activity sector, online accounting information availability, staff numbers and cost per employee) were found to be significant determinants of efficiency. Our findings provide valuable insights into charity efficiency, suggesting DEA is useful for beneficiaries, donors, regulators, trustees and researchers.
... [14][15][16][17] The two main methodological approaches used are the non-parametric (such as DEA) and the parametric (such as stochastic frontier analysis or SFA) approaches. 18,19 To examine the productivity trends of a hospital over time, DEA-based MPI has also been widely employed in hospital-efficiency research. 20,21 Hospital-efficiency research has employed these methods and applied the results to improve hospital performance. ...
Introduction
This study aims to evaluate US Department of Defense hospital efficiency.
Methods
Drawing on the American Hospital Association’s annual survey data, the study employs data envelopment analysis, slack analysis, and the Malmquist Productivity Index to identify the differences in hospital efficiency between Air Force, Army, and Navy hospitals as well as the trends of their efficiency from 2010 to 2021.
Results
US Department of Defense hospitals operated inefficiently from 2010 to 2021, although the average technical efficiency of all DOD hospitals increased slightly during this period. The inefficiency of all US Department of Defense hospitals may be due to the lack of pure technical efficiency rather than the suboptimal scale. However, as the efficiency trends in Navy hospitals differ from those in Army and Air Force hospitals, we should be careful in addressing the inefficiency of each type of US Department of Defense hospital.
Conclusion
Informed by the findings, this study enhances our understanding of US Department of Defense hospital efficiency and the policy implications, offering practical advice to healthcare policymakers, hospital executives, and managers on managing military hospitals.
... The focus of this productivity research generally is on organisations (or sectors) that are responsible for the production of public services to citizens, such as education , health care (Hollingsworth, 2008), drinking water supply (Goede et al., 2016;de la Higuera-Molina et al., 2023), waste collection (Pérez-López et al., 2016;Zafra-Gómez et al., 2023), policing (Barton and Barton, 2011) and the immigration and naturalisation services (Niaounakis and van Heezik, 2019). ...
In this paper, we present an empirical model to analyse the efficiency of youth care by local government, especially with regard to purchasing policies. Locally least squares is applied to data from 352 Dutch municipalities operating in 2021. The outcomes reveal significant variations in the cost efficiency related to purchasing policies among municipalities. For all municipalities, the average cost efficiency is 84%. However, the corresponding standard errors of cost efficiency vary between 2.6% and 14.8% with a mean of 8.6% implying that only a limited number of municipalities are able to achieve efficiency gains with a high degree of certainty. Open House outsourcing and a framework contract without intermediate access are the most influential instruments on cost efficiency. Other features such as the duration of the contract and collaboration with other municipalities appear to have only a modest effect.
... 23 in economic papers and papers on economic issues comparing the efficiency and performance of interventions between hospitals, what is usually used is a populationbased retrospective study. [24][25][26][27][28][29][30] We then assess differences using counterfactual analysis on an exhaustive, detailed administrative database that allows us to get more statistical significance, given the study's statistical power. [31][32][33] the database comes from the french National hospital discharge diagnosis databases: pMsi-Mco for acute care and pMsi-ssr for rehabilitation care. ...
Background:
Patients' socioeconomic status on hospitals' efficiency in controlling for clinical component characteristics may have a role that has few been studied in rehabilitation centers.
Design:
Because of the national health insurance system, rehabilitation centers are free of charge. To answer whether a patient's socioeconomic status (SES) is associated with efficiency and performance, we use a counterfactual analysis to get the patient's SES effect "as if" the patient's case was identical to whatever hospital. We restrained the data to patients from public acute care units where the decision on rehabilitation sector admission is based on availability, limiting bias by confounding factors. Besides, an analysis of six pathologies led to the same results.
Setting:
An exhaustive, detailed administrative database on rehabilitation center stays in France. To define the patients' socioeconomic status, we use two sources of data: the information collected at the time of the patient's entry into rehabilitation care and the information collected during the patient's stay in acute care. This double information avoids possible loss of socio-economic details between the two admissions.
Population:
Patients recruited were exhaustively admitted over the year 2018 for stroke, chronic obstructive pulmonary disease, heart failure, or total hip replacement in France in the acute care unit and then in a rehab center. Mainly the elderly population. Information on patients' demography, comorbidities, and SES are coded due to the reimbursement system. Different dimensions controlling for factors (hospital ownership, patient clinical characteristics, rehabilitation care specificities, medical staff detailed information, and patients' socioeconomic status), were progressively added to control for any differences in baseline data between the two groups.
Methods:
We assess rehabilitation centers' efficiency by combining selected outcome quality indicators (Physical score improvement, Cognitive score improvement, Mortality, Return-to-home). The specific Providers' Activity Index is used to get the performance index.
Conclusions:
The performance of healthcare institutions is correlated not only to the case mix of their patients but also to the socioeconomic status of the patients admitted. The performance needs to be seen in light of patients' socioeconomic status.
Clinical rehabilitation impact:
The data reveals that patients' socioeconomic status affects rehabilitation care efficiency and performance. In controlling patients' socioeconomic status, for-profit rehabilitation hospitals seemed more efficient than public ones.
... Özellikle konusu insan hayatı olan sağlık hizmetlerinin en iyi şekilde sunulması ve geliştirilmesi için etkinlik ölçümlerinin yapılması gerekmektedir. Sağlık hizmetlerinde etkinlik belirlenirken mevcut kaynaklar ve hizmet alımı biten bireylerin sağlık sonuçları değişkenleri kullanılmaktadır (Hollingsworth, 2008;Hussey ve diğerleri, 2009;Atmaca ve diğerleri, 2012). ...
Amaç: Bu çalışmada OECD ülkelerinin COVID-19 pandemisiyle mücadelelerinin ilk bir yıllık sürecindeki kaynak verimliliklerinin aylık ve dönem boyu zaman dilimleri açısından karşılaştırmalı olarak incelenmesi amaçlanmıştır. Yöntem: Araştırmada Veri Zaflama Analizi (VZA) kullanılmıştır. VZA ile ülkelerin aylık ve dönem boyu zaman aralıklarına ilişkin etkinlik skorları elde edilmiştir. Daha sonra ülkelerin verimlilik sıralamalarını belirlemek amacıyla süper etkinlik analizi yapılmış ve ülkelerin ele alınan zaman dilimlerindeki kendi aralarındaki verimlilik sıraları elde edilmiştir. Bulgular: Ülkelerin COVID-19 pandemisiyle mücadele etkinlikleri zaman içerisinde değişmiş, bazı ülkelerin süreç boyunca başarısız olduğu görülmüştür. ABD, Kolombiya ve Yeni Zelanda’nın süreç boyunca en başarılı ülkeler olduğu görülmüştür. Özgünlük: Bu çalışma, OECD ülkelerinin COVID-19 pandemisiyle mücadele verimliliğini karşılaştırırken etkili olacağı düşünülen birçok değişkeni ele alması, belirli zaman aralıklarıyla incelemesi ve sadece verimliliklerinin değil, verimlilik sıralamalarının da belirlenmesi açısından literatürdeki diğer çalışmalardan ayrışmaktadır.
... Understanding and analysing the efficiency of decision-making units has long been a cornerstone of different disciplines, including economics (Färe et al., 1994), agriculture (Kumbhakar and Heshmati, 1995), and health studies (Hollingsworth, 2008). Traditionally, stochastic frontier analysis (SFA, Aigner et al., 1977;Battese and Coelli, 1995a) has served as a valuable tool for this purpose, providing information on both the level of production achieved and the potential for improvement. ...
In the literature on stochastic frontier models until the early 2000s, the joint consideration of spatial and temporal dimensions was often inadequately addressed, if not completely ne- glected. However, from an evolutionary economics perspective, the production process of the decision-making units constantly changes over both dimensions: it is not stable over time due to managerial enhancements and/or internal or external shocks, and is influenced by the nearest territorial neighbours. This paper proposes an extension of the Fusco and Vidoli (2013) SEM-like approach, which globally accounts for spatial and temporal effects in the term of inefficiency. In particular, coherently with the stochastic panel frontier literature, two different versions of the model are proposed: the time-invariant and the time-varying spatial stochastic frontier models. In order to evaluate the inferential properties of the proposed es- timators, we first run Monte Carlo experiments and then present the results of an application to a set of commonly referenced data, demonstrating robustness and stability of estimates across all scenarios.
... Hospital safety is an issue which is evolving quickly and has been recognized as a matter of concern because it affects the lives of healthcare workers across a variety of disciplines, as well as impacting upon overall effectiveness of health service delivery (Hollingsworth 2008). Hospitals are dangerous places, with risks that range in nature from highly physical and chemical through to biological and psychological, putting not just employees but patients consumers and visitors at potential risk (Joseph and Arasu 2020). ...
Objective: The current study evaluates occupational safety and health risks in educational hospitals using the Hoshra index by concentrating on the detection and control of frequent hazards.Methods: Using a structured approach, the HOSHRA index classifies risks in to physical, chemical, biological, ergonomic and psychological domains. The framework supports targeted risk scoring, improving the efficiency with which hospitals can allocate resourcesFindings: The analysis uncovers important types of healthcare worker hazards. Biological and psychological risks appear to be particularly suboptimal, emphasizing the importance of effective infection control interventions, as well as psychological care. The study underscores the need to have a culture of safety that supports hazard reporting and management.Novelty: This is one of the first to use the HOSHRA index in many educational hospital, introducing new methods for risk analysis and assessment beyond traditional classic styles.Research Implications: The results highlight the need for adapted and risk based strategies in healthcare settings. Healthcare organizations can improve the well-being of staff and, by extension, patient care outcomes, by aligning safety protocols with the unique features of wards.
... Sağlık sektöründe verimlilik yönetiminin önemli bir yeri vardır. Sağlık sektöründeki işletmelerin de diğer sektörlerdeki işletmeler gibi faaliyetlerini sürdürebilmesinde maliyetlerin ve verimliliğin yönetimi büyük rol oynamaktadır (Smith ve York, 2004;Hollingsworth, 2008 2. Faaliyet Tabanlı Maliyet Hesaplama: Sağlık hizmetlerinin maliyetlerini belirlemek için "faaliyet tabanlı maliyet hesaplama" yaklaşımını kullanmaları önerilmektedir. Bu yöntem, her bir hizmetin maliyetini ayrı ayrı hesaplamayı içermektedir ve bu sayede sağlık hizmetlerinin maliyet yapısının daha iyi anlaşılabileceği belirtilmektedir. ...
Araştırmanın amacı veri zarflama analizi ile yataklı servisi olan hastane birimlerinin verimliliğinin ölçülmesidir. Bu kapsamda bir eğitim ve araştırma hastanesinde yataklı servislerin verimlilik düzeylerini ölçmek için; yataklı servislerde görevli uzmanlığını almış doktor sayısı, servisteki yatak sayısı, servisin toplam gideri, servisteki araştırma görevlisi doktor sayısı, serviste hastaların ortalama yattığı gün sayısı, öğretim üyelerinin toplam ders saati ve serviste çalışan yardımcı personel sayısı girdi olarak, servislerin gelirleri, servisteki akademisyenlerin yayın sayısı ve servisin tedavi ettiği hasta sayısı çıktı olarak veri zarflama analizi ile incelenmiştir. Araştırmanın evrenini yataklı servis ile hizmet veren tüm hastane birimleri oluşturmaktadır. Araştırma örneklemi olarak bir eğitim ve araştırma hastanesindeki yataklı hizmet veren birimler seçilmiştir. Analiz neticesinde beyin sinir cerrahisi, kadın hastalıkları ve doğum, kalp ve damar cerrahisi, ortopedi ve travmatoloji ve üroloji servisleri en verimli servisler olarak belirlenmiştir.
... It has been claimed that "Data Envelopment Analysis (DEA) is the most commonly used approach for evaluating healthcare efficiency" (Hollingsworth, 2008;Gajewski et al., 2009;Matawie and Assaf, 2010), and we shall use an example from this field to illustrate our method. The data (Cooper, Seiford and Tone, 2006, p. 169) cover 14 general hospitals and have two inputs: nurses and doctors. ...
When comparing performance (of products, services, entities, etc.), multiple attributes are involved. This paper deals with a way of weighting these attributes when one is seeking an overall score. It presents an objective approach to generating the weights in a scoring formula which avoids personal judgement. The first step is to find the maximum possible score for each assessed entity. These upper bound scores are found using Data Envelopment Analysis. In the second step the weights in the scoring formula are found by regressing the unique DEA scores on the attribute data. Reasons for using least squares and avoiding other distance measures are given. The method is tested on data where the true scores and weights are known. The method enables the construction of an objective scoring formula which has been generated from the data arising from all assessed entities and is, in that sense, democratic.
... This could then be used to identify appropriate government policy implications and responses or identify processes and/or management practices that should be spread (or encouraged) across the less efficient, but otherwise similar, 16 The current literature is fairly rich on various examples of empirical values of SFA for the estimation and use of efficiency estimates in different fields of research. For example, in the context of electricity providers, see Knittel (2002), Hattori (2002), and Kuosmanen (2012); for banking efficiency, see Case, Ferrari & Zhao (2013) and references cited therein; for the analysis of the efficiency of national health care systems, see Greene (2004) and a review by Hollingsworth (2008); for analyzing efficiency in agriculture, see Bravo-Ureta & Rieger (1991), Battese & Coelli (1992, 1995, and Lien, Kumbhakar & Hardaker (2017), to mention just a few. ...
... Efficiency analysis in the healthcare sector, and particularly in hospitals, often resorts to Data Envelopment Analysis (DEA), a linear programming method that evaluates the relative performance of different units based only on their combination of inputs and outputs (see Hollingsworth (2008) Focusing on DEA applications to Portuguese hospitals, Moreira (2008) evaluates the impact of the 2002's Portuguese health system reform on hospitals' technical efficiency and concludes that hospitals converted to public enterprises achieved relatively higher efficiency gains. The results of Rego et al. (2010) also suggest that the introduction of market processes and changes in organisational structure had a positive impact on Portuguese public hospitals. ...
This study uses Data Envelopment Analysis (DEA) to assess the technical efficiency of a representative panel of 22 Portuguese public hospitals in the period from 2012 to 2022. Based on the different estimated models, there was a decline in average hospital efficiency, particularly since 2017. The results by hospital reveal a high persistence of those that form the efficiency frontier in each year and a widespread decrease in efficiency scores. DEA is a technique that evaluates the performance of hospitals relative to their peers, not taking into account the impact of exogenous factors or quality effects. To address the latter, a simple composite indicator was
constructed to evaluate the quality of health services provided by these hospitals. The results show a general stabilisation in average quality over the period and suggest that hospitals with higher technical efficiency also tend to provide better healthcare services. (JEL: I1, I11)
... This could then be used to identify appropriate government policy implications and responses or identify processes and/or management practices that should be spread (or encouraged) across the less efficient, but otherwise similar, 16 The current literature is fairly rich on various examples of empirical values of SFA for the estimation and use of efficiency estimates in different fields of research. For example, in the context of electricity providers, see Knittel (2002), Hattori (2002), and Kuosmanen (2012); for banking efficiency, see Case, Ferrari & Zhao (2013) and references cited therein; for the analysis of the efficiency of national health care systems, see Greene (2004) and a review by Hollingsworth (2008); for analyzing efficiency in agriculture, see Bravo-Ureta & Rieger (1991), Battese & Coelli (1992, 1995, and Lien, Kumbhakar & Hardaker (2017), to mention just a few. ...
... DEA is favored due to its flexibility in handling multiple inputs and outputs without the need for predefined functional forms [16]. Since its adoption in the mid-1980s, DEA has been extensively utilised to assess healthcare efficiency globally [17][18][19]. Despite its widespread use, traditional DEA models like Charnes-Cooper-Rhodes (CCR) and Banker-Charnes-Cooper (BCC) models often overlook the quality of outputs and the external environmental factors impacting institutional efficiency. ...
Background
With Primary Health Care (PHC) being a cornerstone of accessible, affordable, and effective healthcare worldwide, its efficiency, especially in developing countries like China, is crucial for achieving Universal Health Coverage (UHC). This study evaluates the efficiency of PHC systems in a southwest China municipality post-healthcare reform, identifying factors influencing efficiency and proposing strategies for improvement.
Methods
Utilising a 10-year provincial panel dataset, this study employs an enhanced Data Envelopment Analysis (DEA) model integrating Slack-Based Measure (SBM) and Directional Distance Function (DDF) with the Global Malmquist-Luenberger (GML) index for efficiency evaluation. Tobit regression analysis identifies efficiency determinants within the context of China’s healthcare reforms, focusing on horizontal integration, fiscal spending, urbanisation rates, and workforce optimisation.
Results
The study reveals a slight decline in PHC system efficiency across the municipality from 2009 to 2018. However, the highest-performing county achieved a 2.36% increase in Total Factor Productivity (TFP), demonstrating the potential of horizontal integration reforms and strategic fiscal investments in enhancing PHC efficiency. However, an increase in nurse density per 1,000 population negatively correlated with efficiency, indicating the need for a balanced approach to workforce expansion.
Conclusions
Horizontal integration reforms, along with targeted fiscal inputs and urbanisation, are key to improving PHC efficiency in underdeveloped regions. The study underscores the importance of optimising workforce allocation and skillsets over mere expansion, providing valuable insights for policymakers aiming to strengthen PHC systems toward achieving UHC in China and similar contexts.
... In the literature, the concept of efficiency reveals that it is a multidimensional concept. Some authors distinguished three types of efficiency: technical, allocative, and economic [17][18][19][20][21][22][23]. Technical efficiency is defined as the capacity of an organization to provide the maximum number of services from a given set of inputs. ...
Background
In developing countries, many organizations are attempting to implement interventions to improve adolescent mental, sexual, and reproductive health (ASRH). One of the challenges to implementing these interventions is the efficiency of the use of available resources. Unfortunately, very few or in the case of some of the most resource-constrained contexts such as Niger, no efficiency studies are yet available. To bridge this knowledge gap, this study examined the provision of adolescent mental, sexual, and reproductive health services in Niger. It also analysed the technical efficiency and determinants of public health facilities in providing adolescent sexual and reproductive health services.
Method
The study was a cross-sectional survey of 160 health facilities (rural and urban) providing ASRHs in the Niamey and Maradi regions. The data were collected from May 31 to June 24, 2022. To examine the supply of mental health and sexual and reproductive health services, a descriptive frequency analysis was conducted. Then, to determine the technical efficiency scores of the health facilities, a stochastic frontier analysis based on the translog function was performed. A fractional logit regression was performed to analyse the determinants of the technical efficiency of the health facilities.
Results
More than 89% of the health facilities surveyed reported offering sexual and reproductive health services to adolescents in the Maradi and Niamey regions. However, the provision of mental health services remains very mixed, with only one in 10 facilities offering these services in the Maradi and Niamey regions. The stochastic estimation indicates an average technical efficiency score of 45%. The health facilities with higher-than-average scores were in rural areas and were run by men.
Conclusion
Given that the results indicated a limited provision of mental health services, action needs to be taken to alleviate this issue. Training for health personnel in the management of adolescents suffering from mental disorders. A low technical efficiency score indicates that health facilities can perform better. One way to improve the technical efficiency score is to equip health facilities with rooms for adolescents. Another way is to set up a well-stocked, functional pharmacy stocked with products related to sexual and reproductive health services.
... Maximizing the efficiency and productivity of hospitals is a common target for most countries. Thus, production frontier techniques and particularly DEA have been extensively used in the academic literature for measuring the relative technical efficiency and the SE of the hospitals of different health systems (for a review, see Hollingsworth, 2008;O'Neill et al., 2008;Kohl et al., 2019). The final goal of these measures is to help policymakers make better managerial decisions. ...
In the realm of assessing scale efficiency (SE), it tends to be computed as a firm‐specific phenomenon rather than something associated with the whole shape of the frontier of the technology under evaluation. This circumstance may lead to inaccuracies in the conclusions drawn regarding the returns to scale (RTS) exhibited across the entire frontiers since they can be biased by the location of the observations. This paper addresses this gap by defining the notion of global SE measured through projecting synthetic observations randomly generated within a unit‐hypercube against the technologies under constant and variable RTS. Our approach enables the comparison of the local SE exhibited by each firm in relation to the global expected percentage of production points that display a similar type of RTS across the production frontier. The approach is illustrated through a numerical example and applied to a set of hospitals in Spain.
... A variety of techniques have been used to investigate efficiency in public health service delivery, such as WHO's National Immunization Program Reviews, to reveal components adversely impacting overall performance [11]. This and other techniques seek to explore waste or underutilization of resources, either by reducing the number of inputs to achieve minimal constant outputs-e.g., through a Data Envelopment Analysis (DEA) [12][13][14][15][16][17][18]-or to increase output levels while keeping the number of inputs fixed. DEA is employed to construct an empirical efficient surface for decision-making units (DMUs) with multiple inputs and outputs [19]. ...
Introduction
Child immunization, though cost-beneficial, experiences varying costs influenced by individual facility-level factors. A real-time solution is to optimize resources and enhance vaccination services through proper method to measure immunization facility efficiency using existing data. Additionally, examine the impact of COVID-19 on facility efficiency, with the primary goal of comprehensively assessing child immunization facility efficiency in Pakistan.
Methods
Utilizing survey data collected in four rounds from May 2018 to December 2020, the research focuses on doses administered and stock records for the preceding six months in each phase. In the initial stage, Data Envelopment Analysis (DEA) is utilized to compute facility efficiency, employing two models with varied outputs while maintaining consistent inputs. Model 1 assesses doses administered, encompassing three outputs (pentavalent vaccine 1, 2, and 3). Meanwhile, Model 2, focuses on stock used featuring a single output (total doses used). The inputs considered in both models include stock availability, staff members, cold chain equipment, vaccine carriers, and vaccine sessions. The second stage involves the application of two competing regression specifications (Tobit and Simar-Wilson) to explore the impact of the COVID-19 pandemic and external factors on the efficiency of these facilities.
Results
In 12 districts across Punjab and Sindh, we assess 466 facilities in Model 1 and 455 in Model 2. Model 1 shows 59% efficiency, and Model 2 shows 70%, indicating excess stock. Stock of vaccines need to be reduced by from 36% to 43%. In the stage, COVID-19 period reduced efficiency in Model 1 by 10%, however, insignificant in Model 2.
Conclusions
The proposed methodology, utilizing DEA, emerges as a valuable tool for immunization facilities seeking to improve resource utilization and overall efficiency. Model 1, focusing on doses administered indicates facilities low efficiency at average 59% and proves more pertinent for efficiency analysis as it directly correlates with the number of children vaccinated. The prevalent issue of overstocking across all facilities significantly impacts efficiency. This study underscores the critical importance of optimizing resources through the redistribution of excess stock with low efficiency.
... Radojicic et al. (2019) used Data Envelopment Analysis to assess healthcare efficiency in 22 countries. Most literature focuses on hospital efficiency, with limited studies evaluating national health system efficiency (Hollingsworth, 2008). Interest grew after the World Health Organization's 2000 report evaluated health system effectiveness across 191 countries (Evans et al., 2001). ...
Relevance. Public health effectiveness is crucial for population health, especially in the face of global challenges like the COVID-19 pandemic. The study applies the Data Envelopment Analysis (DEA) model to measure the efficiency of operating costs in the health care system across various regions of Kazakhstan from 2017 to 2021. Existing methods for assessing healthcare effectiveness often overlook the system's complexity, which turns DEA into a valuable tool to identify inequalities in healthcare availability and quality. Research objective. This study aims to employ the DEA model to measure the efficiency of operating costs in the health care system across regions of Kazakhstan in 2017-2021. Data and methods. The DEA model was chosen for its ability to analyze the efficiency of operating costs. Data were collected from the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan. Results. Our findings indicate the need for increased healthcare financing in specific regions, emphasizing the importance of transparent spending. The study concludes that the DEA model can regularly assess health financing, ensuring resources are directed where most needed. The novelty lies in establishing a link between financing and health outcomes. Conclusions. The study's results and methodology can be used by public health authorities in assessing operating costs' effectiveness, allocating resources judiciously, and making informed decisions to enhance the healthcare system.
... Liu et al. (2013) conducted a literature survey of DEA applications and found that health care area was the second most popular area after the banking sector. According to another literature review studied by Hollingsworth (2008), hospital efficiency was the first ranking DEA application in the healthcare area. ...
The purpose of this research is to point out detail performance analysis of general teaching hospitals and investigate of the efficiency pattern of them. For this research, Data envelopment analysis (DEA) approach is used to evaluate the relative technical and scale efficiencies of general teaching hospitals. Clinical service quality development strategies must be developed to decrease hospital mortality. Hospitals must put a lot of numbers of the beds and nurses to serve more suitable scale sizes.
One of the orogen types in the intra-continental tectonic settings is pop-up structures with bi-vergent thrust tectonics. In this research, the western part of the Kopet-dagh Orogen, as the southern boundary of the Turan Plate, is selected as a case study for investigation on tectonic development and deformation patterns, using surface–subsurface structural studies together with complementary remote sensing and GIS environmental capability as a multi-disciplinary approach. The results of this research determined two sets of faults, consisting of (a) first-stage longitudinal reverse faults with a structural trend of N90–100 to N50–70 (Tangrah, Takal-Kuh, Marave-Tappe, and Golijeh faults), and (b) transversal right-hand strike-slip faults with a trend of N130–150 (Kalaleh, Ughcheh, and Sarighamish faults). These two sets of faults formed the structural framework of this zone and played an important role in the tectonic evolution (initiation, shaping, evolution of sedimentary basins, and forming a fold-thrust belt) of this orogen. A change in the mechanism of the first-stage longitudinal faults, as tectonic inversion, at the onset of the Late Alpine Orogeny caused the re-arrangement of P-axes and thus formed bi-vergent reverse faulting in the northern and southern edges of this zone. Subsequently, fault propagation folds were established due to this event. Finally, an extensive V-shaped compressional pop-up structure with bi-vergent thrusting and fold axial surfaces (as fault-related folds) was formed in the Kopet-dagh fold-thrust belt. Also, the mechanism of the second-stage transversal faults changed to right-hand strike-slip faults with some normal components at the onset of the Late Alpine Orogeny. The normal component of these faults (especially the Kalaleh fault) caused the settlement of the western part of this zone along them and formed the Gorgan-Gonbad plain.
Background
Stroke is a major global cause of death and disability, with high body mass index (HBMI) as a key modifiable risk factor. Understanding HBMI‐attributable stroke burden is crucial for effective prevention.
Methods and Results
Using Global Burden of Disease 2021 data, we analyzed disability‐adjusted life years and mortality from stroke and its subtypes (ischemic stroke, intracerebral hemorrhage, subarachnoid hemorrhage) attributable to HBMI at global, regional, and national levels from 1990 to 2021. We conducted decomposition, frontier, inequality, and predictive analyses to assess epidemiological trends and future projections up to 2035. Despite country‐specific variations in disability‐adjusted life years and mortality, the global burden of stroke and its subtypes attributable to HBMI has increased from 1990 to 2021. Frontier analysis indicated that countries with higher sociodemographic index were expected to own lower age‐standardized rates for stroke and its subtypes attributable to HBMI. Decomposition analysis revealed that population growth and aging were the primary contributors to the rise. Significant cross‐country disparities remained, although inequality analysis showed a decline in SDI‐related differences over time. The projected annual rise in disability‐adjusted life years and mortality from 2021 to 2035 suggested ongoing significant challenges in stroke control and management in the coming decades.
Conclusion
The global health challenge posed by the increasing burden of stroke and its subtypes attributable to HBMI remains significant, especially in low‐ and middle‐sociodemographic index regions. Targeted lifestyle modifications and policy interventions are crucial for reducing HBMI and mitigating stroke burden, warranting special attention from policymakers in high‐burden regions.
Background
Productivity in the healthcare sector has evolved as an appealing research topic in the last few years. Despite the growing interest, the extant scientific literature mostly concentrates on methodologies rather than theoretical and practical insights. Although diverse methodologies provide valuable quantitative wisdom, their application is often misaligned with broader economic theories or healthcare purposes, limiting their contribution to advancing theoretical and practical understanding of efficiency and productivity in healthcare systems. In this respect, the current study endeavors to bridge the research gap concerning the lack of a comprehensive overview of productivity measurements in the healthcare sector.
Methods
We investigate this concern through a bibliometric and content analysis of articles published on healthcare productivity measurement techniques in the Web of Science database between 2003 and 2023. We provide a quantitative and critical analysis of conceptualization, methods, findings, and implications of the selected published articles concerning productivity measurements in the healthcare sector.
Results
Our research discovered that the sanitary crisis generated by COVID-19 boosted the publication of scientific papers on productivity measurements in healthcare, with Europe emerging as a leading region in publication output. Although Data Envelopment Analysis and the Malmquist Index monopolize the range of measurement techniques used to quantify productivity, current research highlights the requirement for alternative methodologies to grasp the multidimensionality of healthcare productivity, including its interaction with quality and technological progress.
Conclusions
We raise awareness that future efforts should prioritize multidimensional and context-sensitive approaches to measuring healthcare productivity, balancing efficiency, technological progress, and quality of care. Policymakers should focus on designing context-specific policies tailored to regional challenges and promoting targeted research funding to explore underrepresented areas of healthcare services.
This Element discusses the role of the government in the financing and provision of public health care. It summarises core knowledge and findings in the economics literature, giving a state-of-the-art account of public health care. The first section is devoted to health system financing. It provides policy rationales for public health insurance which rely on both equity and efficiency, the co-existence of public and private health insurance, how health systems deal with excess demand, and the effect of health insurance expansion. The second section covers the provision of health care and the effect of policy interventions that aim at improving quality and efficiency, including reimbursement mechanisms, competition, public–private mix, and integrated care. The third section is devoted to the market for pharmaceuticals, focusing on the challenges of regulating on-patent and off-patent markets, and discussing the main incentives for pharmaceutical innovation.
The current study attempts to assess the operational efficiency of the firms restructured to resolve their situation of financial distress. Efficiency evaluation of the firms helps to understand the firm’s performance after it goes through the restructuring process as opposed to liquidation. Thus, it is a measure to validate the decision of the Tribunal to allow the firm to continue as a going concern against liquidation. Malmquist Index, a data envelopment analysis technique, has been utilised to measure the efficiency of firms for three years after restructuring. A study of 40 manufacturing firms reveals that the firms’ efficiency has declined over three years (2017 to 2020) primarily due to the inability of the firms to achieve optimal scale of production. While there has been an improvement in technical efficiency and technological advancement, the overall technical efficiency has been reduced. The study thus provides valuable insights to management and practitioners regarding the areas that require attention post-restructuring and separates performance changes due to other factors like technological advancement or scale efficiencies.
Healthcare is inextricably bound to productivity, efficiency, and economic development. Although many methods for analyzing productivity and efficiency have been extensively covered, relatively little focus has been placed on how those methods can be applied to health care in a coherent and comprehensive manner. The Cambridge Handbook of Healthcare outlines current foundations and states of the art on which future research can build. It brings together experts in this growing field to cover three key sources and aspects of human welfare – productivity, efficiency, and healthcare. Beginning with academic focused chapters, this book bridges and provides outreach to the practice and regulation of the health care industry and includes academic and regulatory perspectives, including overviews of major evidence from international empirical applications. Each chapter is dedicated to a particular topic and delivered by international experts on that topic.
Strengthening primary healthcare (PHC) is vital for enhancing efficiency and improving access, clinical outcomes, and population well-being. The World Health Organization emphasizes the role of effective PHC in reducing healthcare costs and boosting productivity. With growing healthcare demands and limited resources, efficient management is critical. Background/Objectives: Building on this point, this study aimed to evaluate the efficiency of PHC units across Greece, focusing on Health Centers (HCs) and Local Health Units (ToMYs). The objective was to assess their efficiency levels and identify factors contributing to observed inefficiencies. This study explores a novel research area by being the first to assess the efficiency of restructured primary healthcare facilities in Greece, utilizing 2019 data—the first year operational data became available for the newly established ToMY facilities following recent healthcare reforms. Methods: We applied a comprehensive suite of non-parametric methods, including Data Envelopment Analysis (DEA) under variable, constant, increasing, and decreasing returns to scale (VRS, CRS, IRS/NDRS, DRS/NIRS) assumptions, along with the Free Disposal Hull (FDH) model, all oriented toward output maximization. Efficiency scores were refined using bootstrapping to calculate 95% confidence intervals, and efficient units were ranked via the super-efficiency model. Outliers were identified and removed through the data cloud algorithm. For the first time at this scale, the final sample included the vast majority of PHC facilities in Greece—234 Health Centers and 94 Local Health Units—with inputs categorized into three human resource types: medical, nursing/paramedical, and administrative/other staff. Outputs encompassed scheduled visits, emergency visits, and pharmaceutical prescription visits. This diverse and comprehensive application of DEA methods represents a novel approach to evaluating PHC efficiency in Greece, with potential relevance to broader healthcare contexts. Results: The analysis revealed significant inefficiencies and differences in technical efficiency between HCs and ToMYs. HCs could nearly double their outputs (VRS score: 1.92), while ToMYs could increase theirs by 58% (VRS score: 1.58). Scale efficiency scores were closer, with HCs slightly more aligned with their optimal scale (1.17 vs. 1.20 for ToMYs). Conclusions: There is significant potential to improve efficiency in PHC, with variations depending on unit characteristics and regional differences. This evaluation provides a foundation for policymakers to identify areas for improvement and enhance the overall performance of healthcare services in Greece.
Objective
Analysing and evaluating how efficiently health resources are allocated to county-level Traditional Chinese Medicine (TCM) hospitals in Zhejiang Province, this study aims to provide empirical evidence for improving operational efficiency and optimising resource allocation in these hospitals.
Design and setting
The study employed a three-stage Data Envelopment Analysis (DEA) model to assess efficiency, using data from 68 county-level TCM hospitals. Four input and five output variables related to TCM services were selected for the analysis.
Results
The first-stage DEA results indicated that in 2022, the technical efficiency (TE) of TCM hospitals in Zhejiang Province was 0.788, the pure technical efficiency (PTE) was 0.876 and the scale efficiency (SE) was 0.903. The classification of hospitals into four groups based on the bed size showed statistically significant differences in returns to scale (p<0.001). The Stochastic Frontier Analysis regression results were significant at the 1% level across four regressions, showing that environmental variables such as per capita GDP, population density and the number of hospitals impacted efficiency. In the third stage DEA, after adjusting the input variables, the TE, PTE and SE improved to 0.809, 0.833 and 0.917, respectively. The adjusted mean TE rankings by region were West (0.860) > East (0.844) > South (0.805) > North (0.796) > Central (0.731).
Conclusion
There is an imbalance between the inputs and outputs of county-level TCM hospitals. Each region must consider factors such as the local economy, population and medical service levels, along with the specific development characteristics of hospitals, to reasonably determine the scale of county-level TCM hospital construction. Emphasis should be placed on improving hospital management and technical capabilities, coordinating regional development, promoting the rational allocation and efficient use of TCM resources and enhancing the efficiency of resource allocation in county-level TCM hospitals.
This article aims to critically understand the current state of the art of Public-Private Partnerships (PPPs) in the health sector and to highlight implications useful for implementing sustainability-oriented PPPs. By employing text mining to analyze the sampled articles, we identify the main themes distinguishing healthcare PPPs and sustainability. We critically discuss the narrative underlying the results achieved in terms of emerging topics: governance, managerial tools, barriers, advantages/disadvantages, and big data. Finally, we propose a literature-driven framework on PPP management that identifies implications – systemic approach, technical managerial skills, governance balance, operational instruments – that can facilitate the development of sustainability-oriented PPPs in the healthcare sector.
In major emergencies, psychological crisis intervention plays a critical role in safeguarding public mental health and supporting post-disaster recovery. However, uneven resource allocation underscores the urgent need for more efficient resource integration pathways. This study, based on data from Chinese governmental and health departmental reports in 2022, employs a Data Envelopment Analysis (DEA) and a fuzzy-set Qualitative Comparative Analysis (fsQCA) to evaluate the efficiency of psychological crisis intervention resource integration across various regions. It investigates the combined effects of internal and external factors on improving integration efficiency through a configurational approach. The findings reveal that while the overall efficiency of resource integration is relatively high, there are notable differences between institutions. The analysis identifies five key pathways: policy support-driven (H1), professional capability-driven (H2), comprehensive synergy-driven (H3), resource optimization (NH1), and community empowerment (NH2). Despite regional disparities, effectively integrating key resources can enhance overall efficiency. Coordinating internal and external factors and optimizing essential resources are crucial for improving the effectiveness of psychological crisis interventions. This research offers actionable insights into integration strategies to strengthen psychological crisis intervention during emergencies. The findings also provide practical solutions to improve disaster preparedness and intervention efficiency, addressing a key gap in crisis resource management research.
This study investigates the impact of activity‐based funding (ABF) on the performance of hospitals by exploiting a natural experiment that happened in the state of Queensland, Australia. To examine the outcome of the reform, the performance of hospitals is measured by the technical efficiency estimated from data envelopment analysis (DEA) models. We try to identify the causal effect of ABF on the technical efficiency of hospitals by incorporating difference‐in‐differences approach in the popular two‐stage DEA framework. We find empirical evidence that ABF improves the technical efficiency of hospitals.
Background: Public and private healthcare in epilepsy are two different systems for citizens to enjoy health. Aims: The objective was to compare epidemiological and social data between public and private hospitals in epilepsy care in Mexico. Methods: Descriptive, prospective, observational, and longitudinal study. Inclusion criteria were patients with epilepsy from March 2021 to December 2022 in a tertiary private hospital (1) and public hospitals (89) in Mexico. Study variables were age, gender, type of epilepsy, etiology, number of seizures, paraclinical studies, and treatment. We compare epidemiological and social data between public and private hospitals in epilepsy care. Information was captured in Excel and analyzed in SPSS. Results: A total of 554 patients from the private hospital and 10,852 patients from the public hospitals were treated. In private hospitals, we found that there is a smaller sample of patients, less family history of epilepsy, and increased diagnosis of epileptic syndromes. Also, there are more genetic etiologies, less structural etiologies, and less drug resistance. Besides, more epilepsy surgery, and access to paraclinical studies. In public hospitals, we found that there is a larger sample of patients, more family history of epilepsy, and fewer diagnoses of epileptic syndromes. Also, there are fewer genetic etiologies, more structural etiologies, and more drug resistance. Besides, less epilepsy surgery, and less access to paraclinical studies. Conclusion: In private hospitals, we found more epilepsy surgery and access to paraclinical studies than in public hospitals. In Mexico, programs have been created to unify both systems and achieve the same diagnostic and treatment opportunities.
Accountable care organizations (ACOs) were created to promote health care value by improving health outcomes while curbing health care expenditures. Although a decade has passed, the value of care delivered by ACOs is yet to be fully understood. We proposed a novel measure of health care value using data envelopment analysis and examined its association with ACO organizational characteristics and social determinants of health (SDOH). We observed that the value of care delivered by ACOs stagnated in recent years, which may be partially attributed to challenges in care continuity and coordination across providers. ACOs that were solely led by physicians and included more participating entities exhibited lower value, highlighting the role of coordination across ACO networks. Furthermore, SDOH factors, such as economic well-being, healthy food consumption, and access to health resources, were significant predictors of ACO value. Our findings suggest a “skinny in scale, broad in scope” approach for ACOs to improve the value of care. Health care policy should also incentivize ACOs to work with local communities and enhance care coordination of vulnerable patient populations across siloed and disparate care delivery systems.
This study reviews scholarly publications on data envelopment analysis (DEA) studies on acute care hospital (ACH) efficiency published between 1984 and 2022 in scholarly peer-reviewed journals. We employ systematic literature review (SLR) method to identify and analyze pertinent past research using predetermined steps. The SLR offers a comprehensive resource that meticulously analyzes DEA methodology for practitioners and researchers focusing on ACH efficiency measurement. The articles reviewed in the SLR are analyzed and synthesized based on the nature of the DEA modelling process and the key findings from the DEA models. The key findings from the DEA models are presented under the following sections: effects of different ownership structures; impacts of specific healthcare reforms or other policy interventions; international and multi-state comparisons; effects of changes in competitive environment; impacts of new technology implementations; effects of hospital location; impacts of quality management interventions; impact of COVID-19 on hospital performance; impact of teaching status, and impact of merger. Furthermore, the nature of DEA modelling process focuses on use of sensitivity analysis; choice of inputs and outputs; comparison with Stochastic Frontier Analysis; use of congestion analysis; use of bootstrapping; imposition of weight restrictions; use of DEA window analysis; and exogenous factors. The findings demonstrate that, despite several innovative DEA extensions and hospital applications, over half of the research used the conventional DEA models. The findings also show that the most often used inputs in the DEA models were labor-oriented inputs and hospital beds, whereas the most frequently used outputs were outpatient visits, followed by surgeries, admissions, and inpatient days. Further research on the impact of healthcare reforms and health information technology (HIT) on hospital performance is required, given the number of reforms being implemented in many countries and the role HIT plays in enhancing care quality and lowering costs. We conclude by offering several new research directions for future studies.
In estimating productivity change over time, technical change is frequently miscalculated as the geometric average of technological changes between two periods based on firm-specific information in the dataset. However, the frontier shift over time is a global phenomenon linked to relative technological progress or regress across the entire frontiers. In this paper, we fill this gap by determining the technical change using synthetic observations generated at random within a unit hypercube and calculating the distances between them and the two frontiers being
evaluated. Accordingly, we propose a decomposition of the Malmquist index’s traditional technical change into two components: average global technical change, which is shared by all production units, and local technical change, which captures how each firm experiences global technical change. In this way, our approach establishes a new research avenue in production economics based on using randomly generated virtual points to assess overall phenomena.
Objective:
To analyse the technical efficiency and productivity of general hospitals in the Spanish National Health Service (NHS) (2010-2012) and identify explanatory hospital and regional variables.
Methods:
230 NHS hospitals were analysed by data envelopment analysis for overall, technical and scale efficiency, and Malmquist index. The robustness of the analysis is contrasted with alternative input-output models. A fixed effects multilevel cross-sectional linear model was used to analyse the explanatory efficiency variables.
Results:
The average rate of overall technical efficiency (OTE) was 0.736 in 2012; there was considerable variability by region. Malmquist index (2010-2012) is 1.013. A 23% variability in OTE is attributable to the region in question. Statistically significant exogenous variables (residents per 100 physicians, aging index, average annual income per household, essential public service expenditure and public health expenditure per capita) explain 42% of the OTE variability between hospitals and 64% between regions. The number of residents showed a statistically significant relationship. As regards regions, there is a statistically significant direct linear association between OTE and annual income per capita and essential public service expenditure, and an indirect association with the aging index and annual public health expenditure per capita.
Discussion:
The significant room for improvement in the efficiency of hospitals is conditioned by region-specific characteristics, specifically aging, wealth and the public expenditure policies of each one.
The World Health Assembly Resolution WHA58.33 in 2005 urged Member States to implement universal health coverage (UHC) to ensure that all people, including the poor and the marginalized, are able to afford essential healthcare services. The Sustainable Development Goal indicator 3.8 is dedicated to the UHC goal. However, evidence shows high levels of catastrophic and impoverishing healthcare expenditure among households in sub-Saharan Africa (SSA). This implies that achieving the UHC goal would require evidence-informed policies which would ensure more value for money and not just more money. This study investigated the factors that influence the efficiency of health systems in SSA. The investigation was carried out in three empirical papers. Paper One evaluated the cost efficiency and the factors that influence the cost efficiency of primary health care facilities (PHCFs) in Ghana using stochastic frontier analysis (SFA) model. The results show that the estimated cost efficiency of Health Centers (HCs) and Community-based Health Planning Services (CHPS) are 61.6% and 85.8%, respectively. Also, HCs (CHPS) with higher medical staff to patients’ ratios are likely to be more cost-efficient (inefficient) than those with lower ratios. Paper two estimated the UHC indices for 30 SSA countries and examined the efficiency with which health systems in SSA are utilizing healthcare resources towards achieving the UHC goal by 2030. The paper uses the bootstrap data envelopment analysis (DEA) model. The results show that the estimated UHC indices for countries in SSA range from a minimum of 52% to a maximum of 81% (SD=8.6%) with a median coverage of 66%. The average bias-corrected efficiency score for healthcare spending efficiency in pursuing the UHC goal is 0.81 (95% CI: 0.77-0.85). Paper three investigated the effect of health care financing policy reforms, particularly social health insurance and broader health financing typologies, on health system efficiency. The results reveal that prepayment health financing arrangements significantly improves health system efficiency.
As the US healthcare system transitions from volume to value, various value‐based programs tie medical reimbursements to hospital performance relative to national top performers (i.e., benchmarks). However, prior studies report very limited results on how such benchmarks affect care delivery and patient outcomes across multiple performance fronts. This study examines how general acute care hospitals progress toward benchmarks measured by performance frontiers in technical efficiency, clinical quality, and patient experience over time, subjecting to external market conditions and internal focuses. Based on a panel dataset comprising hospitals in California from 2012 and 2019, our results find support for competitive‐distance‐driven progression rates, suggesting that hospitals' competitive positions measured by their distances to benchmarks drive performance improvements. Yet, the effect diminishes as they move closer to performance frontiers. In addition, we find that market competition reduces the progression rate of technical efficiency. Finally, our results also suggest that focus improves performance progression rates, yet its effects are curvilinear.
One of the most important topics in healthcare management is the performance evaluation of hospitals and healthcare centers. This work is done with different models of decision analysis and after determining the evaluation criteria. One of the most up-to-date and widely used ways to evaluate performance is the use of non-parametric models such as Data Envelopment Analysis (DEA). In many evaluations, after determining the criteria and specifying the inputs and outputs for using DEA models, we are faced with ratio data. Traditional DEA models are not suitable models for handling this type of data, and it is necessary to use DEA models to handle this type of data. In this research, after examining some ratio criteria for evaluating public hospitals and health centers, DEA-R models are presented to handle these data both in cases of non-negative data and negative data.
This paper focuses on hospital performance using Data Envelopment Analysis (DEA) and Malmquist index numbers. We present a new approach that restricts achievement in productivity if quality is reduced. Results present an apparent negative evolution in productivity. The decomposition on the Malmquist index shows a clear improvement in technical quality, a convergence in efficiency between frontier and non-frontier hospitals and a theoretical fall in technical change (drop of the best practice frontier between years). The conclusions present reasoning justifying these results and propose the use of this methodology for the appraisal of effectiveness in the public sector.
This highly successful textbook is now available in its third edition. Over the years it has become the standard textbook in the field world-wide. It mirrors the huge expansion of the field of economic evaluation in health care, since the last edition was published in 1997. This new edition builds on the strengths of previous editions, being clearly written in a style accessible to a wide readership. Key methodological principles are outlined using a critical appraisal checklist that can be applied to any published study. The methodological features of the basic forms of analysis are then explained in more detail with special emphasis of the latest views on productivity costs, the characterisation of uncertainty and the concept of net benefit. The book has been greatly revised and expanded especially concerning analysing patient-level data and decision-analytic modelling. There is discussion of new methodological approaches, including cost effectiveness acceptability curves, net benefit regression, probalistic sensitivity analysis and value of information analysis. There is an expanded chapter on the use of economic evaluation, including discussion of the use of cost-effectiveness thresholds, equity considerations and the transferability of economic data. This new edition is required reading for anyone commissioning, undertaking or using economic evaluations in health care, and will be popular with health service professionals, health economists, pharmacand health care decision makers. It is especially relevant for those taking pharmacoeconomics courses.
This paper develops a productivity index applicable when producers are cost minimisers and input prices are known. The index is inspired by the Malmquist index as extended to productivity measurement. The index developed here is defined in terms of input cost rather than input quantity distance functions. Hence, productivity change is decomposed into overall efficiency and cost technical change. Furthermore, overall efficiency change is decomposed into technical and allocative efficiency change and cost technical change into a part capturing shifts of input quantities and shifts of relative input prices. These decompositions provide a clearer picture of the root sources of productivity change. They are illustrated here in a sample of hospitals; results are computed using non-parametric mathematical programming.
The Spanish pharmaceutical industry underwent an important transformation during the 1990s. To survive under the new market conditions, labs had to refocus their competitive strategies towards increasing productive efficiency or reinforcing research and development (R&D) activities. This paper analyzes the evolution of the productive patterns in a sample of 80 pharmaceutical laboratories that operated in Spain from 1994 to 2000. We estimate Malmquist productivity indexes and decompose them into four sources of productivity change. The results suggest that pure technical efficiency change and the scale change of the technology explain most of the productivity growth observed during the period. The contribution of technical change to productivity growth is negligible, indicating a poor result from R&D activities at least in the groups of Small and Medium-sized labs.
This paper analyses hospital performance using Data Envelopment Analysis (DEA) and the Malmquist productivity index. We follow two approaches to quantify move- ments in productivity: (1) the traditional approach that only considers output and input variables; and (2) a more comprehensive approach that incorporates movements in quality and restricts some achievements, if quality is reduced. On the premise that the indicator for quality (nosocomial infections) is equivalent to a bad output, we explore the characteristics of, and compare the results of, the different technological ways to incorporate quality (good or bad attributes, strong or weak disposability technological assumptions). After discussing the virtues and limitations of the existing possibilities, the paper presents a better formula- tion that allows the preservation of TQM postulates. The decomposition in the Malmquist productivity index shows an improvement in productivity and a positive technical change, especially when quality is introduced.
In a healthcare system, resource allocation mechanisms should promote efficiency, improve quality of care and reduce practice variation. This paper presents a framework that has been used at the US Department of Veterans Affairs, VA Healthcare Network Upstate New York, to guide resource allocation and productivity assessment since 1999. It synthesises the latest developments in the field of healthcare finance and stochastic frontier analysis and generalises the traditional concept of productivity to include overutilisation or over-consumption of resources as part of inefficiency. Objective rather than subjective information is employed to predict resource need at the hospital level and to assess the operational efficiency of hospitals.
Background
The Government of Ghana has been implementing various health sector reforms (e.g. user fees in public health facilities, decentralization, sector-wide approaches to donor coordination) in a bid to improve efficiency in health care. However, to date, except for the pilot study reported in this paper, no attempt has been made to make an estimate of the efficiency of hospitals and/or health centres in Ghana. The objectives of this study, based on data collected in 2000, were: (i) to estimate the relative technical efficiency (TE) and scale efficiency (SE) of a sample of public hospitals and health centres in Ghana; and (ii) to demonstrate policy implications for health sector policy-makers.
Methods
The Data Envelopment Analysis (DEA) approach was used to estimate the efficiency of 17 district hospitals and 17 health centres. This was an exploratory study.
Results
Eight (47%) hospitals were technically inefficient, with an average TE score of 61% and a standard deviation (STD) of 12%. Ten (59%) hospitals were scale inefficient, manifesting an average SE of 81% (STD = 25%). Out of the 17 health centres, 3 (18%) were technically inefficient, with a mean TE score of 49% (STD = 27%). Eight health centres (47%) were scale inefficient, with an average SE score of 84% (STD = 16%).
Conclusion
This pilot study demonstrated to policy-makers the versatility of DEA in measuring inefficiencies among individual facilities and inputs. There is a need for the Planning and Budgeting Unit of the Ghana Health Services to continually monitor the productivity growth, allocative efficiency and technical efficiency of all its health facilities (hospitals and health centres) in the course of the implementation of health sector reforms.
Various attempts to assess the performance of German hospitals have generated a wide range of estimates regarding their efficiency. These attempts were based on different, often rather small data sets consisting of heterogeneous hospitals; the techniques applied range from simple benchmarking approaches to studies which employ Data Envelopment Analysis (DEA). Some studies report 'dramatic differences in efficiency' and propose savings potentials of 50%; others find an average efficiency in excess of 95% and characterize almost 75% of their observations as fully efficient. This study presents results for two datasets representative of two segments of the German hospital system. These segments comprise all hospitals that have one internal medicine and one surgery department; the hospitals are located in the old federal states of Germany. None of the hospitals provides tertiary care. DEA can be applied because all hospitals offer a comparable quality and range of services. The results were estimated with a DEA-bootstrapping procedure and suggest an average bias-corrected efficiency of around 80%.
This paper contributes to the effort to model and measure how the method of financing of health expenditure affects the efficiency with which better health can be achieved. The focus is on the health system efficiency at the country level, which provides an alternative to the work done in the WHO in this regard. The approach uses frontier techniques as in the WHO studies; however the paper appeals to the economic index number theory of quantity and productivity indexes, which have well-established axiomatic properties, and provides a means for aggregating multiple health output proxies without having to attach arbitrary weights. This allows the proposal of a specification that embeds health sector performance in a broader index of economic inputs and outputs and allows for comparisons across countries and time.
There has been increasing interest in measuring the productive performance of health care services, since the mid-1980s. This paper reviews this literature and, in particular, the concept and measurement of efficiency and productivity. Concerning measurement, we focus on the use of Data Envelopment Analysis (DEA), a technique particularly appropriate when multiple outputs are produced from multiple inputs. Applications to hospitals and to the wider context of general health care are reviewed and the empirical evidence from both the USA and Europe (EU) is that public rather than private provision is more efficient.
In Sub-Saharan Africa (SSA), there is a huge knowledge gap of health facilities performance. The objective of this study is to measure relative technical efficiencies of 54 public hospitals in Kenya using Data Envelopment Analysis (DEA) technique. 14 (26%) of the public hospitals were found to be technically inefficient. The study singled out the inefficient hospitals and provided the magnitudes of specific input reductions or output increases needed to attain technical efficiency.
This study examines how ownership affected changes in hospital inefficiency after the introduction of prospective payment by Medicare. Using a national data set, we estimate cost frontiers for 1986 and 1991 to assess hospitals' efficiency relative to best practice in both those years. We then use regression analysis to determine the effect of ownership on the change in hospitals' efficiency. The results indicate that, in both 1986 and 1991, mean inefficiency was highest for for-profit hospitals and lowest for not-for-profit hospitals, with government hospitals falling in the middle. Moreover, between 1986 and 1991, both for-profit and government hospitals had significantly less improvement in efficiency than not-for-profit hospitals, all else equal.
We address the efficiency in education and health sectors for a sample of OECD countries by applying two alternative non-parametric methodologies: FDH and DEA. Those are two areas where public expenditure is of great importance so that findings have strong implications in what concerns public sector efficiency. When estimating the efficiency frontier we focus on measures of quantity inputs. We believe this approach to be advantageous since a country may well be efficient from a technical point of view but appear as inefficient if the inputs it uses are expensive. Efficient outcomes across sectors and analytical methods seem to cluster around a small number of core countries, even if for different reasons: Japan, Korea and Sweden.
This paper examines the efficiency of the German hospital sector over time and the relative efficiency of public, welfare (both nonprofit) and private (for-profit) hospital sectors using data from the Federal Statistics Office of German hospitals. Efficiency scores were computed using data envelopment analysis. The absolute efficiency of the hospital sector as a whole was found to have improved between 1991 and 1996. In this comparison, the empirical results showed that the hospitals in the public and welfare sector are relatively more efficient than private hospitals. Our results suggest that public, welfare and private hospital sectors have different best-practice frontiers; and that public and welfare hospital sectors appear to use relatively fewer resources than private hospitals. These results suggest differences in quality of care arising from ownership.
In this paper, we apply Data Envelopment Analysis (DEA) and cluster analysis (CA) to assess psychiatric hospitals operating in the US. DEA is utilized to examine the relative performance of these hospitals in terms of input use. CA is applied for two purposes. First, we use CA to find similar hospitals prior to the DEA to facilitate peer-groupings; and second, we can also use CA to identify factors that distinguish efficient from inefficient hospitals. To preview our results, we find that even though public hospitals appear to be relatively less efficient than either not-for-profit or for-profit hospitals, no one ownership group dominated any other in terms of efficiency. Using CA, we identify which groupings of hospitals (irrespective of ownership) make up peer groupings based on hospital characteristics. This approach is superior to simply stratifying by ownership which may lead to biased efficiency results.
Executive Summary: In this article, we estimate X-efficiency levels in the nursing home industry and investigate the impact of their profit status and chain affiliation on performance. Using a Bayesian stochastic frontier approach and a nation-wide data set, we find that nursing homes are relatively cost inefficient. We also find strong evidence that chain affiliations and nonprofit status reduce a firm's operational cost efficiency. Finding that chain-affiliated homes are less cost efficient than independent homes casts concerns on the industry in light of the recent growth in chain affiliations.
In a relatively short period of time Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. DEA has been successfully applied to a host of different types of entities engaged in a wide variety of activities in many contexts woridwide. This chapter discusses the fundamental DEA models and some of their extensions.
This work focuses on measuring and explaining producer performance. The contributors to this volume view performance as a function of the state of technology and economic efficiency. They show that insights can be gained by allowing for the possibility of a divergence between the economic objective and actual performance, and by associating this inefficiency with causal variables subject to managerial or policy influence. The book is derived from a series of lectures held on techniques and applications of the three approaches to the construction of production frontiers and measures of efficiency.
Purpose
The importance of health care is growing world‐wide, and the health sector is receiving a good proportion of public funds. As health‐care costs are increasing, efforts have been made to assess the operational efficiency of hospitals in many countries.
Design/methodology/approach
In this paper, the efficiencies of operation of 20 hospitals in the Sultanate of Oman are evaluated using Data Envelopment Analysis (DEA). The hospitals selected are Regional and Wilayat hospitals under the Ministry of Health, the Sultan Qaboos University Hospital and the hospital of the Royal Oman Police. Four outputs representing out‐patient visits, in‐patient services and surgical operations, and three inputs representing the number of beds and manpower are used in the analysis.
Findings
Using data for the year 2000, ten of the 20 hospitals are found to be efficient. A ranking of performance of efficient hospitals has been provided by computing their super‐efficiency scores. The patterns of efficiency changes over the time period 1999‐2000 are studied using the Malmquist Productivity Index (MPI) approach. It has been found that there is a decline in the efficiencies of hospitals during the period. The (geometric) average MPI of the hospitals during the period has declined, and the average technical efficiency change declined less compared with the average technology change.
Originality/value
This paper is one of the few published studies that evaluates the performance of hospitals in countries of the Middle East.
This paper complements the existing literature on hospital mergers by using data envelopment analysis (DEA) to generate both efficiency and productivity measures to ascertain whether hospital mergers, at least in the short run, result in performance gains. Using data over the period 1996–1998, we apply DEA, both pre-merger and post-merger, to set of hospitals that merged in 1997 as well as to a matching control group of non-merging hospitals over the same timeframe. A comparison of DEA efficiency scores and the Malmquist index values across the case and control hospitals allow us to assess whether any increase in productivity is the result of a merger rather than simply and randomly adding two hospitals' inputs and outputs together.
As an alternative to existing physician profiling methods, we used DEA to evaluate the efficiency of primary care physicians from a managed care organisation. Taking a case study approach, we discuss how data collected by this organisation regarding resource utilisation, cost, and quality can be used in various DEA models to determine physician efficiency. We consider several decisions regarding the selection of the DEA models and data to be used in these models. We introduce a ''guided stepwise'' method to determine a small, but relevant, set of input and output variables. Physicians were analysed on the basis of both in-patient and out-patient performance. DEA not only provided measures of physician efficiency, but also identified a small set of reference groups to serve as role models for non-efficient physicians.
During the 1990s the UK carried out one of the first experiments at introducing competition for hospital services, on the assumption that this would enhance their efficiency. This paper analyses this assumption by estimating DEA frontiers and calculating Malmquist indexes of TFP in order to measure the changes in productivity and technical efficiency of a sample of hospitals during that reform. The results show an average mild improvement in the frontier accompanied by a worsening of technical efficiency, no positive link between the reform and higher efficiency and a change in techniques not necessarily beneficial to patients.
In 1995 Taiwan launched the National Health Insurance (NHI) programme, which changed the method payment for hospital finances. This article examines the impact of this financial reform on hospital productivity during this period. Using the Malmquist productivity index approach, a hospital's change in productivity is decomposed into quality, efficiency, and technological change components. Factors affecting efficiency and productivity are also assessed. Results indicate that most hospitals experienced a significant productivity slowdown due to declines in technology and quality of service, but efficiency did significantly improve. The inception of the NHI programme does greatly improve a hospital's productivity and quality of service, but decreases efficiency. Public hospitals' efficiency improvements are significant.
Introduction: Oral Health and Dental Care as Economic GoodsChallenges in the Economic Evaluation of Dental CareMeasuring the Performance of Dental Health Care SystemsDataApplication 1: Regression ModellingApplication 2: Data Envelopment AnalysisConclusion
AcknowledgementsReferences
An attempt is made here to construct and present relative efficiency indices for the services rendered by health districts and specific hospitals in Botswana, using Stochastic Frontier Regression analysis and Data Envelopment Analysis. The analysis indicated that three districts - Kweneng East, Kgalagadi and Boteti - have efficiency scores below the optimum level. Among the 13 hospitals considered, Tsabong Primary Hospital was found to have an efficiency score of less than one. Since the health services involve a number of factors, these indices ought to serve as indicators for further scrutiny of those units (health districts and hospitals) that fall below the optimum efficiency level. The data used for the analysis are from the published material by the Central Statistics Office, Botswana for the year 1997. Health is considered one of the major concerns of the government of Botswana. As a consequence, the authors feel that this study will be useful to policy makers and health planners in giving them some kind of relative ranking among health districts and hospitals.
With the healthcare sector accounting for a sizeable proportion of national expenditures, the pursuit of efficiency has become a central objective of policymakers within most health systems. However, the analysis and measurement of efficiency is a complex undertaking, not least due to the multiple objectives of health care organizations and the many gaps in information systems. In response to this complexity, research in organizational efficiency analysis has flourished. This book examines some of the most important techniques currently available to measure the efficiency of systems and organizations, including data envelopment analysis and stochastic frontier analysis, and also presents some promising new methodological approaches. Such techniques offer the prospect of many new and fruitful insights into health care performance. Nevertheless, they also pose many practical and methodological challenges. This is a timely critical assessment of the strengths and limitations of efficiency analysis applied to health and health care.
In 1984, Banker, Charnes, and Cooper introduced the capability of using data envelopment analysis to assess increasing, decreasing, or constant returns to scale. This analysis would appear to make an important contribution to the health care field because of the regulatory environment within which the industry exists and the competition among hospitals for additional services and capacity. In many states, hospitals must submit a “certificate of need” to prove eligibility to add capacity or services. Agency administrators at the state level should analyze each hospital's production performance to determine the effectiveness of resource utilization. Residents of a state where hospitals are regulated need to know the effectiveness of agencies in allowing resources to be properly allocated to hospitals. Returns to scale analysis can help provide answers to these concerns. We examine Michigan rural hospitals and propose a simple, yet logical procedure for evaluating returns to scale for technically inefficient hospitals.
Insurers, health plans, and individual physicians in the United States are facing increasing pressures to reduce costs while maintaining quality. In this study, motivated by our work with a large managed care organization, we use readily available data from its claims database with data envelopment analysis (DEA) to examine physician practices within this organization. Currently the organization evaluates primary care physicians using a profile of 16 disparate ratios involving cost, utilization, and quality. We employed these same factors along with indicators of severity to develop a single, comprehensive measure of physician efficiency through DEA. DEA enabled us to identify a reference set of “best practice” physicians tailored to each inefficient physician. This paper presents a discussion of the selection of model inputs and outputs, the development of the DEA model using a “stepwise” approach, and a sensitivity analysis using superefficiency scores. The stepwise and superefficiency analyses required little extra computation and yielded useful insights into the reasons as to why certain physicians were found to be efficient. This paper demonstrates that DEA has advantages for physician profiling and usefully augments the current ratio-based reports.
In this study we assess the performance of US teaching hospitals operating in 1995. Since teaching hospitals must increasingly compete with non-teaching hospitals for managed care contracts based on price, decreasing costs can only come from either reducing inefficiencies or decreasing the ‘public good’ production of teaching and research. We use a data envelopment analysis (DEA) approach to measure the relative technical and scale efficiencies on a sample of 254 US teaching hospitals. The next step of our research is to assess in a bivariate context the effect market competition has on the teaching hospitals in our sample. We find that competition (as measured by the number of managed care contracts per hospital and the number of patients covered by these contracts per hospital) has positive effects on the teaching hospitals. In other words, as competition increases so does the teaching hospital’s relative efficiency. We also regress each hospital’s relative efficiency scores on ownership form, organization structure, teaching effort, and competitive market variables. We find that increased competition leads to higher efficiency without compromising teaching intensity.
Traditional data envelopment analysis (DEA) focuses exclusively on measuring the overall efficiency of a decision making unit (DMU). Yet, variables that have explanatory power for the overall operational inefficiency of a DMU may not necessarily be the same as those that affect its individual input inefficiencies. On many occasions, variables that explain the overall inefficiency of a DMU can be inconsistent or incongruent with those that cause its individual input inefficiencies. Therefore, we conjecture that an overall inefficiency score alone may have limited value for decision making since such a process requires fine-tuning and adjustments of specific input factors of the DMU in order to maximize its overall efficiency. In this paper, the utilization and financial data of a set of hospitals in California is used to empirically test the above conjecture.Our study has several important contributions and practical implications. First, we fine-tune previous efficiency measures on hospitals by refining input and output measures. Second, with variables on organization, management, demographics, and market competition, we identify specific factors associated with a hospital's overall operational inefficiency. More importantly, by decomposing the overall DEA operational inefficiency score into different individual input inefficiencies (including slacks), we further identify specific variables that cause individual input inefficiency. Third, significant differences are observed among factors of the overall inefficiency and individual input inefficiencies. These findings have important implications for identifying congruent factors for performance standard setting and evaluation; it also provides invaluable information for guiding effective resource allocation and better decision making for improving hospital operational efficiency.
This paper develops Bayesian tools for making inferences about firm-specific inefficiencies in panel data models. We begin by establishing a Bayesian setting in which fixed and random effects models are defined. What distinguishes these classes of models is the marginal prior independence of the effects. We show how such models can be analyzed using Monte Carlo integration or Gibbs sampling. These techniques are applied to a panel of U.S. hospitals. Our empirical findings illustrate the different characteristics of both types of models, as well as the influence of the particular priors used on the firm effects.
This paper employs the non-parametric data envelopment analysis to document empirical evidence on the relationship between hospital ownership and operating efficiency using annual cross-sectional data on Taiwan hospitals over the period 1996–1997. Hospitals within the same category are compared on the basis of their relative efficiency. Conventional and data-envelopment-analysis-based test procedures are employed to test for efficiency differences between public and private hospitals. The statistical test results indicate that, in general, public hospitals are less efficient than private hospitals for both regional and district hospitals. Specifically, we provide evidence that private hospitals without intensive-care units outperform their public counterparts.
Modern textbook presentations of production economics typically treat producers as successful optimizers. Conventional econometric practice has generally followed this paradigm, and least squares based regression techniques have been used to estimate production, cost, profit and other functions. In such a framework deviations from maximum output, from minimum cost and cost minimizing input demands, and from maximum profit and profit maximizing output supplies and input demands, are attributed exclusively to random statistical noise. However casual empiricism and the business press both make persuasive cases for the argument that, although producers may indeed attempt to optimize, they do not always succeed. This book develops econometric techniques for the estimation of production, cost and profit frontiers, and for the estimation of the technical and economic efficiency with which producers approach these frontiers. Since these frontiers envelop rather than intersect the data, and since the authors continue to maintain the traditional econometric belief in the presence of external forces contributing to random statistical noise, the work is titled Stochastic Frontier Analysis.
The second edition of An Introduction to Efficiency and Productivity Analysis is designed to be a general introduction for those who wish to study efficiency and productivity analysis. The book provides an accessible, well-written introduction to the four principal methods involved: econometric estimation of average response models; index numbers, data envelopment analysis (DEA); and stochastic frontier analysis (SFA). For each method, a detailed introduction to the basic concepts is presented, numerical examples are provided, and some of the more important extensions to the basic methods are discussed. Of special interest is the systematic use of detailed empirical applications using real-world data throughout the book. In recent years, there have been a number of excellent advance-level books published on performance measurement. This book, however, is the first systematic survey of performance measurement with the express purpose of introducing the field to a wide audience of students, researchers, and practitioners. Indeed, the 2nd Edition maintains its uniqueness: (1) It is a well-written introduction to the field. (2) It outlines, discusses and compares the four principal methods for efficiency and productivity analysis in a well-motivated presentation. (3) It provides detailed advice on computer programs that can be used to implement these performance measurement methods. The book contains computer instructions and output listings for the SHAZAM, LIMDEP, TFPIP, DEAP and FRONTIER computer programs. More extensive listings of data and computer instruction files are available on the book's website: (www.uq.edu.au/economics/cepa/crob2005).
This paper presents a data envelopment analysis model that can be implemented by public sector management for assessing the efficiency of a health system within a developing country. To illustrate the practical implementation and interpretation of the model this study compares health systems across 51 developing countries using 1998–99 data. The results of the analysis generated empirical indicators of efficiency, and we demonstrate how these may then be used by management in order to understand the factors associated with good performance of a health system.
French and US hospital technologies are compared using directional input distance functions. The aggregation properties of the directional distance function allow comparison of hospital industry-level performance as well as standard firm-level performance with regard to productive efficiency. In addition, the underlying constituents of efficiency - in the short run, congestion and technical inefficiency, and in the long run, scale inefficiency - are analysed by decomposing the overall measure. By virtue of using the directional distance function, it is also possible to obtain an estimate of a lower bound on allocative inefficiency. It is found that French and US hospitals use quite different technologies. Long run scale inefficiencies cause most of the French hospitals' inefficiency, while short run technical inefficiency is the main source of overall productive inefficiency in the US hospitals.
There are some general considerations which have implications for the delivery and finance of health care in all countries, not only Canada and the USA. Beginning with two propositions: that access to health care is a right of citizenship, which should not depend on individual income and wealth; and that the objective of health services is to maximise the impact on the nation's health of the resources available; the paper examines the ethical justification for pursuing efficiency in health care provision. The different meanings of efficiency are discussed in detail, and the use of quantitative indicators of health benefit, such as the QALY, placed in context. It is argued that the determination of health care resource allocations should take account of costs at both the macro planning level and the micro level of the individual doctor-patient relationship. Given the starting points the overall conclusion is that it is ethical to be efficient, since to be inefficient implies failure to achieve the ethical objective of maximising health benefits from available resources.
The authors compute and compare productivity growth in the health-care sectors for a sample of Organization for Economic Cooperation and Development countries over the period from 1974 to 1989. The authors compute Malmquist productivity indexes, which allow productivity growth to be decomposed into efficiency changes and technical change. These indexes also allow the use of primary quantity data (recently available from the Organization for Economic Cooperation and Development), rather than expenditure data, which the authors argue reduces bias resulting from distorted prices.
The authors specify two models. The first model focuses on the hospital sector; inputs include physicians and medical care beds, whereas outputs are the "intermediate" type used in hospital efficiency studies, namely, inpatient days and discharges.
For the 19 countries with complete data, the authors found little productivity growth based on this model (with the exception of Denmark, with 15.4% cumulated growth, and the United States, with about 5% from 1974 to 1989). The authors did find, however, that the highest productivity levels are found in the United States (Italy and Finland were also on the frontier of technology in the base period, 1974). The second model uses the same inputs as the first (but in per capita terms), but it specifies simple proxies of health outcomes as outputs: life expectancy of women at age 40 and the reciprocal of the infant mortality rate.
For the 10 countries with complete data for this model, the authors found evidence of much more widespread and rapid productivity growth: Denmark's cumulated growth was close to 33%, with the United States close behind. In both these countries, this growth was due solely to technical change over this period.
This paper examines the efficiency of the German hospital sector over time and the relative efficiency of public, welfare (both nonprofit) and private (for-profit) hospital sectors using data from the Federal Statistics Office of German hospitals. Efficiency scores were computed using data envelopment analysis. The absolute efficiency of the hospital sector as a whole was found to have improved between 1991 and 1996. In this comparison, the empirical results showed that the hospitals in the public and welfare sector are relatively more efficient than private hospitals. Our results suggest that public, welfare and private hospital sectors have different best-practice frontiers; and that public and welfare hospital sectors appear to use relatively fewer resources than private hospitals. These results suggest differences in quality of care arising from ownership.
The state of California has recently mandated minimum nurse-staffing ratios, raising concerns about possible affects on hospital efficiency. In this study, we examine how market factors and quality were related to staffing levels in California hospitals in 1995 (prior to implementation of the new law). We are particularly interested in the affect of managed care penetration on this aspect of hospital efficiency because the call to legislative action was predicated on fears that hospitals were reducing staffing below optimal levels in response to managed care pressures. We derive a unique measure of excess staffing in hospitals based on a data envelopment analysis (DEA) production function model, which explicitly includes ancillary care among the inputs and outputs. This careful specification of production is important because ancillary care use has risen relative to daily hospital services, with the spread of managed care and advances in medical technology. We find that market share (adjusted for size) and market concentration are the major determinants of excess staffing while managed care penetration is insignificant. We also find that poor quality (outcomes worse than expected) is associated with less efficient staffing. These findings suggest that the larger, more efficient urban hospitals will be penalized more heavily under binding staffing ratios than smaller, less-urban hospitals.
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.
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.
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.
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.
To expand care for chronic haemodialysis (HD) patients throughout England and Wales by studying two aspects of service delivery that are important: to identify relative performance of haemodialysis satellite units (HDSUs), and understand the factors that influence the performance. As a first step toward these aspects, this work reports a study of apparent comparative efficiency in the delivery of HDSUs and demonstrates the potential of data envelopment analysis (DEA).
DEA was applied to data obtained from a national survey of the organizational structures and processes of delivering care at HDSUs in England and Wales.
DEA was found to be a judicious approach for performance assessment of HDSUs, although valid results depend on appropriate model specification and quality of data available. The available data were not of sufficient comprehensiveness or quality to produce definitive results but suggested that overall efficiency could improve; these data suggested by as much as 10% overall (mean efficiency score 90%) and variably within the sample (46 [65%] that HDSUs were potentially inefficient, the lowest unit scoring 38%).
Addressing questions raised by comparative inefficiency could help plans to improve capacity to deal with the growing demand for HD delivered in HDSUs. The application was an important start and needs to be followed by further research to establish model validity and obtain authoritative results.
The purpose of this study is to discern what factors affect the longevity of amalgam and of composite restorations by dentists who perform posterior restorations. Data are obtained from the Washington Dental Service and contain 1.5 million patient encounters representing visits to 23,000 providers from January 1993 through 31 December 1999. Analysis of provider performance is estimated through Data Envelopment Analysis. The principal finding is that the most efficient dentists produce posterior restorations that survive almost 5 months (4.6 months) longer than those by inefficient providers (chi2 = 18.98, p < 0.0001). The findings suggest that there is no difference in restoration longevity between amalgam and composite restorations when the restoration is performed by efficient provider.
Several tools are available to health care organisations in England to measure efficiency, but these are widely reported to be unpopular and unusable. Moreover, they do not have a sound conceptual basis. This paper describes the development and evaluation of a user-friendly tool that organisations can use to measure their efficiency, based on the technique of data envelopment analysis (DEA), which has a firm basis in economic theory.
Routine data from 57 providers and 14 purchasing organisations in one region of the English National Health Service (NHS) for 1994-1996 were used to create information on efficiency based on DEA. This was presented to them using guides that explained the information and how it was to be used. They were surveyed to elicit their views on current measures of efficiency and on the potential use of the DEA-based information.
The DEA measure demonstrated considerable scope for improvements in health service efficiency. There was a very small improvement over time with larger changes in some hospitals than others. Overall, 80% of those surveyed gave high scores for the potential usefulness of the DEA-based measures compared with 9-45% for existing methods. The quality of presentation of the information was also consistently high.
There is dissatisfaction with efficiency information currently available to the NHS. DEA produces potentially useful information, which is easy to use and can be easily explained to and understood by potential users. The next step would be the implementation, on a developmental basis, of a routine DEA-based information system.