No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Measuring Hospital Efficiency with Frontier Cost Functions
Abstract
This paper uses a stochastic frontier multiproduct cost function to derive hospital-specific measures of inefficiency. The cost function includes direct measures of illness severity, output quality, and patient outcomes to reduce the likelihood that the inefficiency estimates are capturing unmeasured differences in hospital outputs. Models are estimated using data from the AHA Annual Survey, Medicare Hospital Cost Reports, and MEDPAR. We explicitly test the assumption of output endogeneity and reject it in this application. We conclude that inefficiency accounts for 13.6 percent of total hospital costs. This estimate is robust with respect to model specification and approaches to pooling data across distinct groups of hospitals.
... These persistent budgetary deficits suggest possible efficiency issues within New Zealand's healthcare system. Many studies have highlighted the connection between rising healthcare costs and inefficiencies [6][7][8]. Despite the complexities introduced by the unique structure of New Zealand's public healthcare system, the consistent budget deficits undoubtedly underscore the imperative of scrutinising resource utilisation within the DHBs. ...
... In New Zealand, as a standard practice, every inpatient stays in a hospital is assigned a code based on AR-DRGs (Australian Refined Diagnostic Related Groups). 8 The AR-DRGs groups for each inpatient stay are based on similar clinical conditions and resources. A Weighted Inlier Equivalent Separation (WIES) weight within each group is calculated based on the length of stay to derive case-weighted inpatient discharges [55]. ...
... 7 The price index for various goods and services can be accessed at Statistics New Zealand [53]. 8 For more information, refer to The Australian Institute of Health and Welfare [54]. 9 The purchase unit is a classification system used to measure and quantify a healthcare service. ...
Efficiency analysis is crucial in healthcare to optimise resource allocation and enhance patient outcomes. However, the prompt adaptation of inputs can be hindered by adjustment costs, which impact Long-Run Technical Efficiency (LRTE). To bridge this gap in healthcare literature, this research employs a Bayesian Dynamic Stochastic Frontier Model to estimate parameters and explore healthcare efficiency dynamics over time. The study reveals the LRTE for New Zealand District Health Boards (DHBs) as 0.76, indicating around 32% more input utilisation due to adjustment costs. Most DHBs exhibit consistent short-run operational efficiency, with the national Short-Run Technical Efficiency (SRTE) very close to the LRTE. Among the tertiary providers, Auckland and Capital & Coast DHBs operate below the LRTE level, setting them apart from other tertiary providers. Similarly, Tairawhiti and West Coast DHBs also fall below the LRTE level, as indicated by their SRTE scores, potentially influenced by their unique healthcare settings and resource challenges. This research brings a new perspective to policy discussions by incorporating the temporal dynamics of decision-making and considering adjustment costs. It underscores the need to balance short-term and long-term technical efficiency, underlining their collective significance in fostering a sustainable and efficient healthcare system in New Zealand.
... The first health care application of SFA was published by Wagstaff (1989), who examined 49 Spanish hospitals. Zuckerman et al. (1994) published the first SFA-based study of US hospitals. Rosko and Mutter (2011) reviewed the results from 27 U.S. hospital SFA studies. ...
... The Medicare Wage Index was used for the price of labor. Following past practices (Rosko et al. 2018;Zuckerman et al. 1994), the price of capital was approximated by the area (i.e., core-based statistical area) average depreciation and interest expenses per bed. The input price variables were also normalized by the price of labor. ...
... Since some hospitals serve a mixture of acute care and nonacute care patients, we included the proportion of total hospital beds classified as acute care to reflect patients who would not be included in the DRG-based Medicare Case-Mix Index. Following an approach similar to Zuckerman et al. (1994), teaching status was incorporated using three binary variables related to the number of residents and interns trained by the hospital. The three categories of teaching are based on tritiles of the number of residents and interns trained by the hospital and non-teaching hospitals serve as the omitted reference category. ...
This article examines the relationship between hospital profitability and efficiency. A cross-section of 1317 U.S. metropolitan, acute care, not-for-profit hospitals for the year 2015 was employed. We use a frontier method, stochastic frontier analysis, to estimate hospital efficiency. Total margin and operating margin were used as profit variables in OLS regressions that were corrected for heteroskedacity. In addition to estimated efficiency, control variables for internal and external correlates of profitability were included in the regression models. We found that more efficient hospitals were also more profitable. The results show a positive relationship between profitability and size, concentration of output, occupancy rate and membership in a multi-hospital system. An inverse relationship was found between profits and academic medical centers, average length of stay, location in a Medicaid expansion state, Medicaid and Medicare share of admissions, and unemployment rate. The results of a Hausman test indicates that efficiency is exogenous in the profit equations. The findings suggest that not-for-profit hospitals will be responsive to incentives for increasing efficiency and use market power to increase surplus to pursue their objectives.
... Following the common practice in the literature (e.g. Zuckerman et al. 1994;Magnussen 1996;Harris et al. 2000;Nayar and Ozcan 2008;Nayar et al. 2013;Chowdhury and Zelenyuk 2016), in this study, outpatient outputs of Queensland public hospitals (OUT) are measured by the number of non-admitted occasions of service including both emergency and non-emergency services for non-admitted patients. For inpatient service quantities, it is not sufficient to measure the output by the raw counts of admitted patient episodes because of the need to distinguish inpatient care services based on their complexity and resources required. ...
... In principle, for hospitals, labour can be viewed to some extent as a variable input since some nurses and doctors can be hired per diem, rather than having them salaried throughout a year. This might potentially lead to the endogeneity of labour input since a productivity shock might alter a hospitals' choice of the number and the composition of employees (see similar discussions inZuckerman et al. 1994;Thornton 1998, and references therein). The issue, however, might not be serious in the context of our analysis, where in Australia casual employees only account for around 10% of the hospital labour force(Gilfillan 2020). ...
In this paper, we explore the efficiency of different groups of hospitals in Queensland, Australia, focusing on teaching and non-teaching hospitals, by adapting the most recent developments on statistical analysis of aggregate efficiency. We focus on the two approaches: the bootstrap approach proposed by Simar and Zelenyuk (J Appl Econ 22(7):1367–1394, 2007) and the central limit theorems recently developed by Simar and Zelenyuk (Oper Res 66(1):137–149, 2018), (Eur J Oper Res, 2020). To adapt these developments, we extend the central limit theorems to the context where there are several sub-groups in the population. Using real data on Queensland public hospitals, we found that teaching hospitals are significantly less efficient than non-teaching hospitals when benchmarking is done with respect to the constant returns to scale frontier, but are significantly more efficient when benchmarking with respect to the variable returns to scale frontier.
... Without worrying about justifying their preference, [6] used frontier cost functions in a comparative study about hospital efficiency in the United States. [7] compared the performance of nursing homes in New York and opted by cost frontiers estimation to measure jointly allocative and technical efficiency. ...
... Yet, regarding econometric specification possibilities for efficiency cost analysis recently applied at hospital medical care area are important the comments of [18] and [19] about the researches [7] and [6] -considered by [20] the state of art of efficiency frontier econometric -pointing to the fixed effects panel as preferable to the stochastic frontier function. In replying, [21] accept these opinions and choose random effects panel data. ...
This article aims to revisit the measurement of the relative technical efficiency of three clinics
belonging to the University Hospital of the Federal University of Santa Catarina, Brazil, held in the
second half of the 1990s. On this occasion, the specification of the econometric model included the
analysis of productive sectors of the same institution in different periods of time. A Cobb-Douglas
function of stochastic frontier cost using the compound error model was estimated by maximum
likelihood (MLE), for a monthly data panel. The efficiency measures were calculated using a formula
adapted to the costs, based on the estimated parameters. Now, in addition to the re-presentation of
the original study, we return to its methodological path from the year 2000 onwards. In this task, it is
evident that the evolution of the theme continued to prioritize the comparative evaluations of efficiency
between hospitals, without exploring confrontations between units of the same institution. In view of
this, it is proposed to move towards the use of stochastic frontier cost panels, estimated
simultaneously with functions of variables potentially explaining the levels of inefficiency
... Without worrying about justifying their preference, [6] used frontier cost functions in a comparative study about hospital efficiency in the United States. [7] compared the performance of nursing homes in New York and opted by cost frontiers estimation to measure jointly allocative and technical efficiency. ...
... Yet, regarding econometric specification possibilities for efficiency cost analysis recently applied at hospital medical care area are important the comments of [18] and [19] about the researches [7] and [6] -considered by [20] the state of art of efficiency frontier econometric -pointing to the fixed effects panel as preferable to the stochastic frontier function. In replying, [21] accept these opinions and choose random effects panel data. ...
This article aims to revisit the measurement of the relative technical efficiency of three clinics belonging to the University Hospital of the Federal University of Santa Catarina, Brazil, held in the second half of the 1990s. On this occasion, the specification of the econometric model included the analysis of productive sectors of the same institution in different periods of time. A Cobb-Douglas function of stochastic frontier cost using the compound error model was estimated by maximum likelihood (MLE), for a monthly data panel. The efficiency measures were calculated using a formula adapted to the costs, based on the estimated parameters. Now, in addition to the representation of the original study, we return to its methodological path from the year 2000 onwards. In this task, it is evident that the evolution of the theme continued to prioritize the comparative evaluations of efficiency between hospitals, without exploring confrontations between units of the same institution. In view of this, it is proposed to move towards the use of stochastic frontier cost panels, estimated simultaneously with functions of variables potentially explaining the levels of inefficiency.
... where C In are costs for inpatient care, C T costs for medical transport, C O costs for other medical care, C N costs for nonmedical procedures, C Out outpatient costs and D number of inpatient days. The most important output for hospital efficiency analysis is the number of patients (only inpatients in our case, not outpatients) which is often used in the literature and which is preferred to inpatient days that may bias the results due to possible endogeneity born by the length of stay (see Zuckerman et al. 1994;Farsi and Filippini 2006;Hofmarcher et al. 2002). ...
... We found that public nonprofit hospitals tend to be less efficient than the for-profit ones, consistent with Dormont and Milcent (2012) or Czypionka et al. (2014). However others (Choi et al. 2017;Zuckerman et al. 1994; Rosko and Chilingerian 1999;Rosko 2001;Herr 2007;Daidone and D'Amico 2009) came to the opposite conclusion. International comparison of the effect of ownership structure on efficiency has to consider differences in the financing structure and institutional characteristics. ...
This paper estimates the cost efficiency of 81 general hospitals in the Czech Republic during 2006–2010. We employ the conditional order-m approach to assess how inpatient costs in a hospital translate to inpatient outputs considering its environmental characteristics. The outputs include quantitative indicators such as (i) acute patients adjusted for DRG case-mix index, (ii) nursing patients, and (iii) publications reflecting research activity of a hospital; but also a qualitative indicator (iv) nurses/bed ratio. Nonprofit hospitals, university hospitals, and hospitals with specialized centers are generally less efficient.
... It also has been developed into more advanced models for a wider range of empirical purposes. The analysis by Wagstaff (1989) appears to be the first application for the efficiency of hospitals using ALS77, followed by Zuckerman et al. (1994); Rosko (1999); Chirikos and Sear (2000); Farsi and Filippini (2008) for example, as well as studies in other healthcare sectors, e.g., Vitaliano and Toren (1994); Farsi et al. (2005) for the efficiency of nursing homes. ...
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.
... With the aging trend of Taiwan's population, the annual increase in patients with chronic disease and cancer has increased the pressure on health care expenditures. Assessing the efficiency of Taiwan's health care system is thus a crucial topic of concern for the sustainability of the health care system and the preservation of national health (Zuckerman et al. [4]). ...
... Sankey diagram of Australia 9 See e.g.,Burgess and Wilson (1996);Färe et al. (1992);Zuckerman et al. (1994) and the summarization inO'Neill et al. (2008). ...
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.
... Since this study employs a cross-sectional study design, it may only measure the association of the Table 1. profitability [12][13][14]. Although this result was not supported by the abovementioned studies, the positive correlation of the dependent variables with the case mix index was also guided by suggestions of two studies [15,16]. ...
... 1 Stochastic frontier analysis employs econometric models to estimate production frontiers and technical (in)efficiency with respect to these frontiers. Since its first introduction by Aigner et al. (1977) and Meeusen and van den Broeck (1977), stochastic frontier analysis has been applied to study the productivity and efficiency of production units in various economic sectors, such as banking (e.g., Ferrier and Lovell (1990) ;Adams et al. (1999); Kumbhakar and Tsionas (2005); Malikov et al. (2016)), healthcare (e.g., Zuckerman et al. (1994); Rosko (2001); Greene (2004); Mutter et al. (2013);Comans et al. (2020)), agriculture (e.g., Battese and Coelli (1995); Battese and Broca (1997); Kumbhakar and Tsionas (2008)), to mention a few. Moreover, the methodology is also used to undertake cross-country studies on various important aspects of society such as the healthcare system (Greene, 2004) and taxation (Fenochietto and Pessino, 2013). ...
This chapter provides a brief introduction to the stochastic frontier paradigm—one of the most powerful techniques for performance analysis developed over the last few decades to address various research questions for many contexts with empirical applications in a wide variety of economic sectors such as banking, healthcare, agriculture and so on. The chapter also documents the estimation routines used to implement the classical models as well as the recent developments in this research area for practitioners, especially those who are willing to use Stata, but also with tips on sources for R and Matlab users.KeywordsStochastic frontier analysisPanel dataSemi-parametricStataMatlabR
... In each of the six performance domains, organizations would be compared against a frontier performance level or production function. This approach has been widely used to model health facility efficiency, and in some cases learning, with the frontier defined either by the organization with the best results or by the average performance level among organizations in the sample (Vitaliano and Toren 1994; Zuckerman et al. 1994;Pisano et al. 2001;Rosko and Mutter 2008;Vitikainen et al.2009;Bernet et al. 2010). If the organizations in the sample are very different in terms of size, location, or population served, it may be necessary to group the organizations on the basis of key shared characteristics that are salient to the content of the assessment and then identify a best performer in each group, or use an analytic method that controls for these characteristics (Newhouse 1994; Rosko and Mutter 2008). ...
This report is a user's guide for defining, measuring, and improving the performance of health service delivery organizations. The authors define six core performance domains: quality, efficiency, utilization, access, learning, and sustainability and provide a compendium of metrics that have been used to measure organizational performance in each of these six domains. The compendium, which includes 116 distinct categories of metrics, is based on a detailed literature review of peer-reviewed empirical studies of health care organizational performance in World Bank client countries. Based on reading of the literature, the authors define seven major strategy areas potentially useful for improving performance among health care organizations: 1) standards and guidelines, 2) organizational design, 3) education and training, 4) process improvement and technology and tool development, 5) incentives, 6) organizational culture, and 7) leadership and management. The authors provide illustrations of facility-level interventions within each of the strategy areas and highlight the conditions under which certain strategies may be more effective than others. The authors propose that the choice of strategy targeted at organizational level to improve performance should be informed by the identified root causes of the problem, the implementation capabilities of the organization, and the environmental conditions faced by the organization.
... On the micro level, technical efficiency was evaluated for hospitals and nursing homes (Wagstaff, 1989;Hofler and Rungeling, 1994;Zuckerman et al., 1994;Chirikos, 1998;Gerdtham et al., 1999;Kooreman, 1994;Lo et al., 1996;Magnussen, 1996;Street and Jacobs, 2002;Colombi et al., 2017). On the macro level, where the overall performance of the healthcare system is evaluated, different variables and different methodologies (including either non-parametric or parametric approaches or both) were used in addition to conducting the evaluation process within or across countries. ...
... In total, 1.26% (53/4221) of hospitals were randomly selected from each of the 4 strata, representing a total of 5.02% (212/4221) of the hospitals in the HCAHPS data set. The sample size was restricted to maintain the feasibility of manual data collection and processing costs [12]. In sum, we had 29,167 observations of standard charges grouped by 81 different hospitals. ...
Background
The federal health care price transparency regulation from 2019 is aimed at bending the health care cost curve by increasing the availability of hospital pricing information for the public.
Objective
This study aims to examine the associations between publicly reported diagnosis-related group chargemaster prices on the internet and quality measures, process indicators, and patient-reported experience measures.
Methods
In this cross-sectional study, we collected and analyzed a random 5.02% (212/4221) stratified sample of US hospital prices in 2019 using descriptive statistics and multivariate analysis.
Results
We found extreme price variation in shoppable services and significantly greater price variation for medical versus surgical services (P=.006). In addition, we found that quality indicators were positively associated with standard charges, such as mortality (β=.929; P<.001) and readmissions (β=.514; P<.001). Other quality indicators, such as the effectiveness of care (β=−.919; P<.001), efficient use of medical imaging (β=−.458; P=.001), and patient recommendation scores (β=−.414; P<.001), were negatively associated with standard charges.
Conclusions
We found that hospital chargemasters display wide variations in prices for medical services and procedures and match variations in quality measures. Further work is required to investigate 100% of US hospital prices posted publicly on the internet and their relationship with quality measures.
... In recent years, it has become a topic of great interest within the scientific community (Seiford, 1997). In prior literature, efficiency research has been widely performed on various institutions or industries, such as the bank (Bonin, Hasan, & Wachtel, 2005), insurance (Fenn, Vencappa, Diacon, Klumpes, & O'Brien, 2008), hospital (Zuckerman, Hadley, & Iezzoni, 1994), agriculture (Färe, Grabowski, & Grosskopf, 1985), transportation (Chang, Zhang, Danao, & Zhang, 2013), and industrial sectors (Hasanbeigi, Menke, & du Pont, 2010). With the prosperous development of the tourism industry globally, the evaluation of tourism efficiency has received increasing attention from academia over the last few decades. ...
This paper divides the entire operating process of a tourist attraction into the production subprocess and the environmental governance subprocess from a two-subprocess perspective, and evaluates the overall efficiency and the respective subprocess efficiency by establishing a range-adjusted measure (RAM) network data envel-opment analysis (DEA) model and further identifies the determinants of efficiency using a panel Tobit regression model. The applicability of the proposed model was examined using 25 tourist attractions in Chengdu during the period 2012-2016 as cases. The first-stage efficiency results show that only a few tourist attractions perform efficiently in two sub-processes and achieve overall efficiency during the observed period. In particular, most tourist attractions outperform environmental governance in the production subprocess. The overall efficiency is highly correlated with production efficiency and environmental governance efficiency. The second-stage regression analysis implies that the scale effect and technology effect have significant positive impacts on efficiency improvement, while the capital effect and resource endowment effect have significant negative impacts. The structure effect has no significant impacts, and environmental regulation has a significant positive impact on environmental governance efficiency and overall efficiency. Overall, the proposed model has better discriminatory power in distinguishing inefficient subprocesses than the traditional 'black box' models and provides process-specific guidance for efficiency improvement. This study broadens the perspective of efficiency bench-marking and improves the performance evaluation of tourist attractions, and the empirical results provide some valuable policy implications for destination management in tourist attractions.
... There exists pertinent literature concerning the application of stochastic production frontier model for assessing the performances of the health system. Studies which advocates the superiority of the stochastic production frontier approach for estimating the efficiency of health service providers, e.g., hospitals etc., are-Wagstaff [32], Hofler and Rungeling [15], Zuckerman et al. [36], Defelice and Bradford [9], Chirikos [5], Gerdtham et al. [13], Chirikos and Sear [6] and Street and Jacobs [28]. Moreover, studies by Murray and Frenk [22], Evans et al. [10], Sankar and Kathuria [25], Kathuria and Sankar [18], Forbes et al. [12], Hamidi [14] and Yildiz et al. [35], endorse that the 'Stochastic Frontier Approach (SFA)' is a robust approach to measure the efficiency of the health service. ...
Background
Currently, the novel coronavirus or COVID-19 pandemic poses the greatest global health threat worldwide, and India is no exception. As an overpopulated developing country, it is very difficult to maintain social distancing to restrict the spread of the disease in India. Under these circumstances, it is necessary to examine India’s interstate performances to combat COVID-19.
This study aims to explore twin objectives: to investigate the comparative efficiency of Indian states to combat COVID-19 and to unfold the factors responsible for interstate disparities in the efficiency in combatting COVID-19.
Methods
The stochastic production frontier model was utilized for data analysis. The empirical analysis was facilitated by the inefficiency effects model, revealing the factors that influence interstate variability in disease management efficiency. Three types of variables, namely, output, inputs, and exogenous, were used to measure health system efficiency. The relevant variables were compiled from secondary sources. The recovery rate from COVID-19 was the output variable and health infrastructures were considered as the input variable. On the contrary, the non-health determinants considered to have a strong influence on the efficiency of states’ disease management, but could not be considered as input variables, were recognised as exogenous variables. These exogenous variables were specifically used for the inefficiency analysis.
Results
The empirical results demonstrated the existence of disparities across Indian states in the level of efficiency in combatting COVID-19. A non-trivial outcome of this study was that Tamil Nadu was the best performer and Manipur was the worst performer of the investigated states. Variables such as elderly people, sex ratio, literacy rate, population density, influenced the efficiency of states, and thus, affected the recovery rate.
Conclusion
This study argues for the efficient utilisation of the existing health infrastructures in India. Simultaneously, the study suggests improving the health infrastructure to achieve a long-run benefit.
... The first DEA studies to evaluate health care facilities in the literature were performed by Sherman [37], Borden [38] and Färe et al. [39]. To the best of our knowledge, the first SFA studies of a health care organization were published by Wagstaff [40] and Zuckerman et al. [41]. After these studies, there were many contributions in this area, such as Hollingsworth et al. [42], Ersoy, et al. [43], Yesilyurt and Yesilyurt [44], Yesilyurt and Yesilyurt [45], Lee et al. [46], Caballer-Tarazona et al. [47], Lindlbauer and Schreyogg [48], Gholami et al. [49], Wu et al. [50], Tunca and Yesilyurt [51], and Omrani et al. [52]. ...
There are two main methods for measuring the efficiency of decision-making units (DMUs): data envelopment analysis (DEA) and stochastic frontier analysis (SFA). Each of these methods has advantages and disadvantages. DEA is more popular in the literature due to its simplicity, as it does not require any pre-assumption and can be used for measuring the efficiency of DMUs with multiple inputs and multiple outputs, whereas SFA is a parametric approach that is applicable to multiple inputs and a single output. Since many applied studies feature multiple output variables, SFA cannot be used in such cases. In this research, a unique method to transform multiple outputs to a virtual single output is proposed. We are thus able to obtain efficiency scores from calculated virtual single output by the proposed method that are close (or even the same depending on targeted parameters at the expense of computation time and resources) to the efficiency scores obtained from multiple outputs of DEA. This will enable us to use SFA with a virtual single output. The proposed method is validated using a simulation study, and its usefulness is demonstrated with real application by using a hospital dataset from Turkey.
... There is a large literature on the productivity and efficiency of hospitals (see, e.g., Koop et al., 1997;Zuckerman et al., 1994;McCallion et al., 2000;Castelli et al., 2015) examining how successfully hospitals utilize their given resources. While the present paper is inspired by those previous productivity studies, we approach hospital performance from a different perspective of effectiveness rather than efficiency. ...
COVID-19 is an unprecedent virus that had killed more than 1.2 million people around the world by November 2020. Thus far there has been little effort to examine performance of hospitals that are in the forefront in the battle against the pandemic. In this paper we propose a novel approach to assess the effectiveness of hospitals in saving lives. More specifically, we empirically estimate the production function of COVID-19 caused death among hospital inpatients, and subsequently assess performance of hospitals based on the regression residual. Using data of 193 hospitals in England over a 22-week period from April to September 2020, we find significant improvement in the hospital performance during the study period. We also find that larger units are more effective in saving lives, and that the mortality rate is significantly and positively affected by the share of senior patients aged 65 and above. Finally, we find large and systematic performance differences between individual hospitals. A regional comparison shows that hospitals in London had lower mortality than the national average. The findings of our study could be utilized to identify the best performing hospitals to serve as benchmarks. The novel approach developed in this paper is directly applicable in other countries and jurisdictions where similar data are available at the hospital or regional level.
... On the micro level, technical efficiency was evaluated for hospitals and nursing homes (Wagstaff, 1989;Hofler and Rungeling, 1994;Zuckerman et al., 1994;Chirikos, 1998;Gerdtham et al., 1999;Kooreman, 1994;Lo et al., 1996;Magnussen, 1996;Street and Jacobs, 2002;Colombi et al., 2017). On the macro level, where the overall performance of the healthcare system is evaluated, different variables and different methodologies (including either non-parametric or parametric approaches or both) were used in addition to conducting the evaluation process within or across countries. ...
Purpose
This paper aims to evaluate the technical efficiency of the health-care systems in 21 selected middle-income countries during the period (2000–2017) and determine the source of inefficiency whether it is transient (short run) or persistent (long run).
Design/methodology/approach
The study uses the stochastic frontier analysis technique through employing the generalized true random effects model which overcomes the drawbacks of the previously introduced stochastic frontier models and allows for the separation between unobserved heterogeneity, persistent inefficiency and transient inefficiency.
Findings
Persistent efficiency is lower than the transient efficiency; hence, there are more efficiency gains that can be made by the selected countries by adopting long-term policies that aim at reforming the structure of the health-care system in the less efficient countries such as South Africa and Russia. The most efficient countries are Vietnam, Mexico and China which adopted a social health insurance that covers almost the whole population with the aim of increasing access to health-care services. Also, decentralization in health-care has assisted in adopting health-care policies that are suitable for both the rural and urban areas based on their specific conditions and health-care needs. A key success in the implementation of the adopted long-term policies by those countries is the continuous monitoring and evaluation of their outcomes and comparing them with the predefined targets and conducting any necessary modifications to ensure their movement in the right path to achieve their goals.
Originality/value
Although several studies have evaluated the technical efficiency both across and within countries using non-parametric (data envelopment analysis) and parametric (stochastic frontier analysis) approaches, to the best of the authors’ knowledge, this is the first attempt to evaluate the technical efficiency of selected middle-income countries during the period (2000–2017) using the generalized true random effects stochastic frontier analysis model.
... Many authors (Deily & McKay, 2006;Ferrier & Valdmanis, 1996;Khushalani & Ozcan, 2017;Mutter et al., 1992;Nayar & Ozcan, 2008;Rosko et al., 2017;Stanford, 2004;Vera & Kuntz, 2007;Wang et al., 1999;Zuckerman et al., 1994;) have applied DEA to measure hospital efficiency, often from a financial or operational perspective. In this article, DEA is used to measure hospital performance based on quality measures. ...
Executive summary:
The objective of this study was to build a unified quality performance model for hospitals using publicly available data. We obtained data from the New York State Department of Health's Statewide Planning and Research Cooperative System database for our model, which had three outcome measures that we wished to make smaller (deaths, readmissions, average length of stay). Because this was a performance model rather than an economic efficiency model, we excluded costs, which are affected significantly by local economic conditions. We included four site characteristics. With our data envelopment analysis model structure, we used logistic regression to analyze the output. We extracted data for 2,233,214 discharges in 2014 from 183 hospitals in the state. We found that 20.8% of the facilities were on the quality performance frontier-20.6% of the not-for-profit facilities and 21.4% of the other facilities. Hospitals with more discharges performed better with respect to mortality, readmission, and average length of stay. We found no difference in performance between not-for-profit hospitals and others. We concluded that 79.2% of hospitals could improve their quality of care. As an upper bound, if all hospitals increased each quality factor performance to 100%, there would have been 11,722 (24.8%) fewer deaths, 17,840 (15.8%) fewer readmissions, and the statewide average length of stay would have been 0.71 days (13.5%) less.
... e reported cost includes payroll, employee bene ts, infrastructure depreciation, interest, supply, and other expenses. For every patient at each of the hospitals, all procedures received are recorded via the International Classi cation of Diseases, Ninth Revision, Clinical Modi cation (ICD9-CM) codes (Zuckerman et al., 1994). Following Pope and Johnson (2013) and Layer et al. (2020), we map the codes into four categories of procedures, speci cally "Minor Diagnostic, " "Minor erapeutic, " "Major Diagnostic, " and "Major erapeutic". ...
We develop a new approach for the estimation of a multivariate function based on the economic axioms of monotonicity and quasiconvexity. We prove the existence of the nonparametric least squares estimator (LSE) for a monotone and quasiconvex function and provide two characterizations for it. One of these characterizations is useful from the theoretical point of view, while the other helps in the computation of the estimator. We show that the LSE is almost surely unique and is the solution to a mixed-integer quadratic optimization problem. We prove consistency and find finite sample risk bounds for the LSE under both fixed lattice and random design settings for the covariates. We illustrate the superior performance of the LSE against existing estimators via simulation. Finally, we use the LSE to estimate the production function for the Japanese plywood industry and the cost function for hospitals across the US.
... However, their model did not include a variable for quality. Azevedo and Mateus argued that, while the literature recommends including a quality variable to avoid omission bias, some studies indicated that quality measures were not significant in hospital cost functions; their references here were to work by Carey (2003) and Zuckerman, Hadley, and Iezzoni (1994). Given that they lacked sufficient degrees of freedom,-i.e., their sample size was too small-the authors decided to exclude a quality variable. ...
This is a quantitative study of archival data that examines Merger and Acquisition
(M&A) activity using currently established healthcare quality and financial performance
metrics. The research seeks to explicate the relationship between M&A activity and
M&A experience in the healthcare industry as it relates to initiatives aimed at improving
the quality and decreasing the cost of healthcare. The Affordable Care Act (ACA)
legislation appears to be contributing to a trend toward M&A consolidation; by
illuminating how this trend potentially impacts healthcare quality and cost reduction
initiatives, this study’s contribution is both useful and practical. The units of analysis are
Medicare reporting hospitals, hospital systems, and related healthcare providers that have
or have not experienced an M&A or multiple M&As.
The study shows a statistically significant improvement in quality each year from
2006–2014, which is reflected in higher scores for the four quality metrics measured.
M&A activity, as measured by acquisition status and acquirer experience, did not appear
to influence these quality metrics, with the exception of the heart failure measure, which
xiii
showed a statistically significant positive influence of acquirer experience across all
specifications.
M&A activity’s possible effects on hospital financial performance was assessed
through operating-cost-to-charge and capital-cost-to-charge ratios (CCRs). The operating
CCR appears to be positively influenced by both acquisition status and acquirer
experience, while the capital CCR was positively influenced only by acquirer experience.
A positive influence is reflected in a decreasing ratio.
Results on quality improvement over time, both before and after the ACA,
suggest that the ACA itself may not be the driver for quality improvement. Similarly,
decreases in OCCR occurred consistently and statistically significantly over time, both
pre- and post-ACA, while CCCR showed statistically significant decreases in 2006–2008,
2013, and 2014. These results appear to support the notion that the trend was ongoing
before the ACA was enacted and gave these measures high-profile exposure.
... We chose to estimate a cost function and make the assumption of common input prices rather than impose an arbitrary division of the cost. the medical services it provides (Zuckerman et al. (1994)). We map the codes to four categories of procedures, specifically the procedure categories are "Minor Diagnostic," "Minor Therapeutic," "Major Diagnostic," and "Major Therapeutic" which are standard output categories in the literature (Pope and Johnson (2013)). ...
Researchers rely on the distance function to model multiple product production using multiple inputs. A stochastic directional distance function (SDDF) allows for noise in potentially all input and output variables. Yet, when estimated, the direction selected will affect the functional estimates because deviations from the estimated function are minimized in the specified direction. Specifically, the parameters of the parametric SDDF are point identified when the direction is specified; we show that the parameters of the parametric SDDF are set identified when multiple directions are considered. Further, the set of identified parameters can be narrowed via data-driven approaches to restrict the directions considered. We demonstrate a similar narrowing of the identified parameter set for a shape constrained nonparametric method, where the shape constraints impose standard features of a cost function such as monotonicity and convexity.
Our Monte Carlo simulation studies reveal significant improvements, as measured by out of sample radial mean squared error, in functional estimates when we use a directional distance function with an appropriately selected direction and the errors are uncorrelated across variables. We show that these benefits increase as the correlation in error terms across variables increase. This correlation is a type of endogeneity that is common in production settings. From our Monte Carlo simulations we conclude that selecting a direction that is approximately orthogonal to the estimated function in the central region of the data gives significantly better estimates relative to the directions commonly used in the literature. For practitioners, our results imply that selecting a direction vector that has non-zero components for all variables that may have measurement error provides a significant improvement in the estimator’s performance. We illustrate these results using cost and production data from samples of approximately 500 US hospitals per year operating in 2007, 2008, and 2009, respectively, and find that the shape constrained nonparametric methods provide a significant increase in flexibility over second order local approximation parametric methods.
... This analytical framework, and the closely related framework for cost functions, have been applied extensively to hospitals. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] The primary data source for our analysis was the Medicare Inpatient File from 2013. The medical claims in this file report patient diagnoses and procedures, demographic characteristics, charges and payments, dates of service, and the identity of the short-stay hospital. ...
Objectives
To measure value in the delivery of inpatient care and to quantify its variation across U.S. regions.
Data sources / Study setting
A random (20%) sample of 33,713 elderly fee-for-service Medicare beneficiaries treated in 2,232 hospitals for a heart attack in 2013.
Study design
We estimate a production function for inpatient care, defining output as stays with favorable patient outcomes in terms of survival and readmission. The regression model includes hospital inputs measured by treatment costs, as well as patient characteristics. Region-level effects in the production function are used to estimate the productivity and value of the care delivered by hospitals within regions.
Data collection / Extraction methods
Medicare claims and enrollment files, linked to the Dartmouth Atlas of Health Care and Inpatient Prospective Payment System Impact Files.
Principal findings
Hospitals in the hospital referral region at the 90th percentile of the value distribution delivered 54% more high-quality stays than hospitals at the 10th percentile could have delivered, after adjusting for treatment costs and patient severity.
Conclusions
Variation in the delivery of high-value inpatient care points to opportunities for better quality and lower costs.
... We chose to estimate a cost function and make the assumption of common input prices rather than impose an arbitrary division of the cost. the medical services it provides (Zuckerman et al. (1994)). We map the codes to four categories of procedures, specifically the procedure categories are "Minor Diagnostic," "Minor Therapeutic," "Major Diagnostic," and "Major Therapeutic" which are standard output categories in the literature (Pope and Johnson (2013)). ...
Researchers rely on the distance function to model multiple product production using multiple inputs. A stochastic directional distance function (SDDF) allows for noise in potentially all input and output variables. Yet, when estimated, the direction selected will affect the functional estimates because deviations from the estimated function are minimized in the specified direction. The set of identified parameters of a parametric SDDF can be narrowed via data-driven approaches to restrict the directions considered. We demonstrate a similar narrowing of the identified parameter set for a shape constrained nonparametric method, where the shape constraints impose standard features of a cost function such as monotonicity and convexity. Our Monte Carlo simulation studies reveal significant improvements, as measured by out of sample radial mean squared error, in functional estimates when we use a directional distance function with an appropriately selected direction. From our Monte Carlo simulations we conclude that selecting a direction that is approximately orthogonal to the estimated function in the central region of the data gives significantly better estimates relative to the directions commonly used in the literature. For practitioners, our results imply that selecting a direction vector that has non-zero components for all variables that may have measurement error provides a significant improvement in the estimator's performance. We illustrate these results using cost and production data from samples of approximately 500 US hospitals per year operating in 2007, 2008, and 2009, respectively, and find that the shape constrained nonparametric methods provide a significant increase in flexibility over second order local approximation parametric methods.
... Whereas, in the past, scholars and professionals have focused on healthcare organizations' improvements in effectiveness and efficiency [7][8][9][10][11], in recent years the importance of the patient's active role as a fundamental resource for healthcare sustainability has been re-evaluated [12][13][14]. ...
Extensive literature suggests that a solution to the current problems of healthcare sustainability is the active involvement of patients in health management through the empowerment of their abilities. Latest marketing frameworks suggest that patients are important resources for co-creating health value together with operators. This research aims to analyze the effects of patient empowerment on patients’ value co-creation behaviors. An empirical survey was conducted on 250 patients with chronic diseases in Italy. The results, analyzed using the structural equation modeling, showed that their empowerment enhanced value co-creation behaviors. Patients apply their health competencies and resources in their co-creation of health service with operators. It is, therefore, important to empower patients in their transformation from passive to active stakeholders, working with providers for the most optimal health outcomes. This research provides practitioners with suggestions for patient involvement which utilizes their knowledge, capabilities and responsibility to improving healthcare services.
... a conclusion that the optimal strategies selection should depend on the effectiveness of the system operation [2]. Also, efficiency analysis became more popular among nonprofit organizations such as hospitals [3], environmental efficiency [4] as well as educational institutions [5]. By such practices people can better know whether public funding has been properly utilized towards a better societal life. ...
The classic data envelopment analysis (DEA) model is used to evaluate decision-making units' (DMUs) efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new researches begin to focus on the efficiency analysis of non-homogeneous DMU arose by real practices such as the evaluation of departments in a university, where departments argue for the adoption of different criteria based on their disciplinary characteristics. A DEA procedure is proposed in this paper to address the efficiency analysis of two non-homogeneous DMU groups. Firstly, an analytical framework is established to compromise diversified input and output (IO) criteria from two non-homogenous groups. Then, a criteria fusion operation is designed to obtain different DEA analysis strategies. Meanwhile, Friedman test is introduced to analyze the consistency of all efficiency results produced by different strategies. Next, ordered weighted averaging (OWA) operators are applied to integrate different information to reach final conclusions. Finally, a numerical example is used to illustrate the proposed method. The result indicates that the proposed method relaxes the restriction of the classical DEA model, and can provide more analytical flexibility to address different decision analysis scenarios arose from practical applications.
... The bed occupancy rate is the ratio of occupied to available hospital beds (e.g. Zuckerman et al., 1994). We measure the rate of cancelled elective operations dividing the number of cancelled elective operations for non-clinical reasons by the number of elective admissions (Rumbold et al., 2015). ...
We investigate whether hospitals in the English National Health Service increase their quality (mortality, emergency readmissions, patient reported outcome, and patient satisfaction) or efficiency (bed occupancy rate, cancelled operations, and cost indicators) in response to an increase in quality or efficiency of neighbouring hospitals. We estimate spatial cross-sectional and panel data models, including spatial cross-sectional instrumental variables. Hospitals generally do not respond to neighbours’ quality and efficiency. This suggests the absence of spillovers across hospitals in quality and efficiency dimensions and has policy implications, for example, in relation to allowing hospital mergers.
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.
This study asks: Does the empirical evidence support the conclusion that for-profit (FP) hospitals are more productive or efficient than private not-for-profit (NFP) hospitals or non-federal public (PUB) hospitals? Alternative theories of NFP behavior are described. Our review of individual empirical hospital studies of quality, service mix, community benefit, and cost/efficiency in the United States published since 2000 indicates that no systematic difference exists in cost/efficiency, provision of uncompensated care, and quality of care. But FPs are more likely to provide profitable services, higher service intensity, have lower shares of uninsured and Medicaid patients, and are more responsive to external financial incentives. That FP hospitals are not more efficient runs counter to property rights theory, but their relative responsiveness to financial incentives supports it. There is little evidence that FP market presence changes NFP behaviors. Observed differences between FP and NFP hospitals are mostly a "little deal."
Technical efficiency (TE) is an important indicator to examine the performance of an enterprise. This paper applies the most popular stochastic frontier (SF) production functions to estimate the TE index for state-owned enterprises (SOEs) over the period 2010–2019. The results show that mean efficiency scores increased over the period 2010–2015, then decreased gradually in the period 2016–2019. Moreover, the analysis results by industry sectors also indicate that chemical manufacturing industry has the highest mean efficiency scores. Meanwhile, enterprises in the automotive and equipment manufacturing industries have the lowest technical efficiency, and the difference between these two sectors is up to 10%. The paper also makes some recommendations to policy makers to improve production efficiency in these firms.
Provider network structure has been linked to hospital cost, utilization, and to a lesser degree quality, outcomes; however, it remains unknown whether these relationships are heterogeneous across different acute care hospital characteristics and US states. The objective of this study is to evaluate whether there are heterogeneous relationships between hospital provider network structure and hospital outcomes (cost efficiency and quality); and to assess the sources of measured heterogeneous effects. We use recent causal random forest techniques to estimate (hospital specific) heterogeneous treatment effects between hospitals’ provider network structures and their performance (across cost efficiency and quality). Using Medicare cost report, hospital quality and provider patient sharing data, we study a population of 3061 acute care hospitals in 2016. Our results show that provider networks are significantly associated with costs efficiency (P < .001 for 7/8 network measures), patient rating of their care (P < .1 in 5/8 network measures), heart failure readmissions (P < .01 for 3/8 network measures), and mortality rates (P < .02 in 5/8 cases). We find that fragmented provider structures are associated with higher costs efficiency and patient satisfaction, but also with higher heart failure readmission and mortality rates. These effects are further found to vary systematically with hospital characteristics such as capacity, case mix, ownership, and teaching status. This study used an observational design. In summary, we find that hospital treatment responses to different network structures vary systematically with hospital characteristics..
A sizable literature related to the efficiency of the U.S. hospital sector has been produced over the past 30 years. Much of this research is based on stochastic frontier analysis. This approach is problematic for a number of reasons. For one, a functional form for a hospital’s cost function must be assumed, and a limited number of forms are tractable. Second, inefficiency is measured as the expectation of a random variable with a pre-determined distribution, with no theoretical justification for the underlying assumption, that observed cost equals minimum cost plus some non-negative, random amount. Thus, the conclusion reached by most of these studies, that U.S. hospitals are inefficient, may not be foregone. Using an entirely different methodology that obviates these shortcomings, the current study suggests that whether or not hospitals are efficient, their revenues have not been increasing in proportion to the minimum cost of providing their services. This study’s estimates of the impact of input prices and technology on production costs indicate that between 2000 and 2017, hospital revenues increased at a substantially higher rate than hospital costs. This study suggests that hospitals are pricing their services well above average cost. As a result, in 2017 over $200 billion could have been transferred from patients to the hospital sector, whether due to the proclivity of hospital administrators to purchase more inputs than are necessary for production, or to subsidize activities other than patient care.
Data envelopment analysis (DEA) has been widely recognised as a powerful tool for performance analysis over the last four decades. The application of DEA in empirical works, however, has become more challenging, especially in the modern era of big data, due to the so-called ‘curse of dimensionality’. Dimension reduction has been recently considered as a useful technique to deal with the ‘curse of dimensionality’ in the context of DEA with large dimensions for inputs and outputs. In this study, we investigate the two most popular dimension reduction approaches: PCA-based aggregation and price-based aggregation for hospital efficiency analysis. Using data on public hospitals in Queensland, Australia, we find that the choice of price systems (with small variation in prices) does not significantly affect the DEA estimates under the price-based aggregation approach. Moreover, the estimated efficiency scores from DEA models are also robust with respect to the two different aggregation approaches.
This paper reports on a study of expense preference behavior in a conditional sample of hospitals (before and after adoption of contract‐management arrangements) using an extension of Mester's (1989) test. To identify expense preference parameters, input demand equations are considered in addition to the cost function and are estimated jointly with the cost function as a system of nonlinear equations. Based on this test, contract managers do not appear to be cost minimizers, although they tend to exhibit lower expense preference behavior than salaried managers. The importance of our results, however, goes beyond a single industry because we have shown that estimates of expense preference depend critically upon the particular input demand being studied. Studies that hitherto have relied on single input demand equations or on the cost function alone may have to be reinterpreted in this light.
OBJECTIVE: To examined from an empirical point of view, the efficiency of public hospitals in Sudan-Gezira state using the most recent advances in the empirical literature on the measurement of efficiency. A sample consists of 10 Gezira state public hospitals for which relevant data is available over the period 2011-2016 were selected. METHODS: The technique employed in the analysis, is the non-parametric Data Envelopment Analysis (DEA) method, which utilizes the idea of the distance functions to handle the case where a production unit produces more than one output with a vector of inputs. This technique was applied DEA-type Malmquist efficiency for estimating public hospitals efficiency, we have also estimated the contributions of technological, technical, and scale changes to productivity growth, and identified the major sources of productivity gains or losses For the DEA technique, economic efficiency is decomposed into allocative efficiency and technical efficiency, while the latter is further decomposed into pure and scale efficiencies. Furthermore, we have calculated the economies of size and investigated whether public hospitals are operating along their long-run cost curves. RESULTS: The most important results concerning economic (cost) efficiency are summarized as follows the overall average cost efficiency is estimated at 24 percent, implying an average cost inefficiency of 76 percent, results on productivity growth are public hospitals haven't been able to achieve productivity improvement for becoming more technologically advanced (average techch is-17 percent. These results suggest that the total factor productivity change (tfpch) refers to the technological backwardness and technical inefficiency change (teffch). Such a decline in technology may reflect the use of ancient equipment and shortage of trained human resources for the health sector. Finally, the results concerning scale economy, based on the parametric method of the DEA, suggest that the most public hospitals in Sudan-Gezira state (60 percent) are in conditions of constant returns to scale thus, have the required optimal size. Furthermore, 40 percent of public hospitals in the sample are having size problems. CONCLUSIONS: In particular, these hospitals stretch or expand their activities to the extent that they become subject to decreasing returns to scale. Hospitals could reduce inefficiency by human capital development through training and qualifying health manpower and supporting staff internally and externally and also by decreasing administrative expenses. In addition to that, efficiency savings could augment the gains from user fees in terms of mobilizing additional resources, and increase cost-recovery ratios.
Jordan is presently facing difficulties in providing adequate health care for its citizens and the increasing number of refugees settled in the country. This study estimates the efficiency of public hospitals in Jordan. It examines determinants of public hospitals' efficiency in Jordan and how much is the average yearly efficiency for each hospital during the study period, and how much is the average efficiency for different groups of hospitals with diverse characteristics. Also, it estimates the changes in the average yearly efficiency for all public hospitals. The results are obtained by performing a production stochastic frontier analysis on the Jordanian public hospitals data set. The empirical results for a panel of 29 hospitals during the period 2006–2015 indicates that the efficiency of public hospitals in Jordan fluctuates widely among hospitals with a range between 94.43 and 11.72% and an average that equal to 54.67%. However, the average yearly efficiency for all public hospitals did not change during the study period.
Does sensation seeking—as a personality trait that is closely related to risky driving—affect a driver's hazard perception, and if so, under what circumstances? Using hazard locations and vehicle signals as cues, this study classifies traffic situations into four types: danger ahead with cues, danger ahead without cues, danger behind with cues, and danger behind without cues. Based on these classifications, we examine the characteristics of hazard perception, decision-making criteria, sensitivity, and visual attention patterns of drivers with different sensation-seeking tendencies. Fifty-two experienced drivers were recruited for experiments in Dalian, China. Using computer, participants were shown videos taken from the driver's perspective and were asked to respond to hazards by pressing keys on the left mouse button. Their eye movements were recorded, and a multi-dimensional driving-style scale was used to divide participants into higher and lower sensation-seeking groups. The results showed that the higher sensation-seeking drivers had a lower hazard detection rate, fewer fixation counts, and shorter glances when there were cues, and they responded more slowly when there was danger ahead. For dangers ahead with cues and dangers behind without cues, the more attention the higher sensation-seeking drivers paid to the danger, the slower their response and the lower their hazard detection rate. The results indicated that higher sensation-seeking drivers have poor hazard perception in situations when there are vehicle signals as prompting clues and dangers ahead. In particular, for a danger with clues, the visual agility of the higher sensation-seeking drivers was worse than that of the lower sensation-seeking drivers. The more attention they paid to the danger the worse their hazard perception. The results of this study can provide guidance to teach drivers hazard-perception skills.
provides the economic approaches to hospital efficiency
Purpose
Improved performance in operational (lean) and environmental (green) dimensions has been extremely critical to the global competitiveness of organizations. As the performance of small- and medium-sized enterprises (SMEs) is influenced by various external and internal factors, the purpose of this paper is to analyze the lean–green performance of Indian manufacturing SMEs by investigating the influential relationships of various factors along with the set of lean and green practices adopted by the firms.
Design/methodology/approach
The study employs a holistic approach by integrating multiple case study and data envelopment analysis (DEA) in eight manufacturing SMEs to verify a set of five propositions relating issues such as organizational factors, quality and environmental management certifications, implementation of lean and green practices with operational and environmental performance in Indian SMEs. Within-case analysis and cross-case analysis are used for a qualitative investigation of cases while DEA with four input variables, two desirable output variables and one undesirable output variable, is used for quantitative investigation with returns to scale (RTS) and damages to scale (DTS) analysis.
Findings
The RTS/DTS results suggest that Indian SMEs exhibit decreasing RTS and increasing DTS, implying that they need to decrease their operational sizes in order to improve the operational and environmental performance. The possible alternative and more practical strategy could be to introduce new technology innovation and holistic adoption of manufacturing excellence initiatives such as lean and green.
Originality/value
The research findings provide insights into the lean and green performance enhancement approach in the context of SMEs. The study extends key managerial implications and policy-related guidelines.
Background:
As physician groups consolidate and value-based payment replaces traditional fee-for-service systems, physician practices have greater need to accurately measure individual physician clinical productivity within team-based systems. We compared methodologies to measure individual physician outpatient clinical productivity after adjustment for shared practice resources.
Methods:
For cardiologists at our hospital between January 2015 and June 2016, we assessed productivity by examining completed patient visits per clinical session per week. Using mixed-effects models, we sequentially accounted for shared practice resources and underlying baseline characteristics. We compared mixed-effects and Generalized Estimating Equations (GEE) models using K-fold cross validation, and compared mixed-effect, GEE, and Data Envelopment Analysis (DEA) models based on ranking of physicians by productivity.
Results:
A mixed-effects model adjusting for shared practice resources reduced variation in productivity among providers by 63% compared to an unadjusted model. Mixed-effects productivity rankings correlated strongly with GEE rankings (Spearman 0.99), but outperformed GEE on K-fold cross validation (root mean squared error 2.66 vs 3.02; mean absolute error 1.89 vs 2.20, respectively). Mixed-effects model rankings had moderate correlation with DEA model rankings (Spearman 0.692), though this improved upon exclusion of outliers (Spearman 0.755).
Conclusions:
Mixed-effects modeling accounts for significant variation in productivity secondary to shared practice resources, outperforms GEE in predictive power, and is less vulnerable to outliers than DEA.
Implications:
With mixed-effects regression analysis using otherwise easily accessible administrative data, practices can evaluate physician clinical productivity more fairly and make more informed management decisions on physician compensation and resource allocation.
The error term in the stochastic frontier model is of the form (v–u), where v is a normal error term representing pure randomness, and u is a non-negative error term representing technical inefficiency. The entire (v–u) is easily estimated for each observation, but a previously unsolved problem is how to separate it into its two components, v and u. This paper suggests a solution to this problem, by considering the expected value of u, conditional on (v–u). An explicit formula is given for the half-normal and exponential cases.
In the estimations, cross-section data for 1955 and 1970 are analyzed using the translog cost function. It was found that in 1955 there were significant scale economies available to nearly all firms. By 1970, however, the bulk of U.S. electricity generation was by firms operating in the essentially flat area of the average cost curve. It is concluded that a small number of extremely large firms are not required for efficient production, and that policies designed to promote competition in electric power generation cannot be faulted in terms of sacrificing economies of scale. 28 references. (auth)
This study explores a new approach to estimating the cost of inpatient and outpatient services provided by hospitals. Data from a nationwide survey of non-federal, short-term, U.S. hospitals are used to make cost estimates based on a multiple-output cost function. The results provide information on the structure of hospital costs, and include estimates of the marginal and average incremental cost of outpatient care. Because of the innovative specification of the cost function, the study is of interest for its methodology as well as empirical results.
This paper focuses on the political economy of U.S. farm policy since the Uruguay Round trade negotiations concluded in 1994 and established the WTO. The continued ability of the powerful farm lobby in the United States to elicit support in the political arena is evident from this analysis. Yet there have been some substantial changes in policy that have reduced their distortionary effects, as well as some setbacks to liberalizing reform. New Doha Round commitments could put further constraints on subsidies provided by some U.S. policy instruments. And despite the ability of the farm lobby to retain its support programs through 2012, there are several political uncertainties about the alignments that have allowed U.S. farm support to endure.
Previous studies of the so-called frontier production function have not utilized an adequate characterization of the disturbance term for such a model. In this paper we provide an appropriate specification, by defining the disturbance term as the sum of symmetric normal and (negative) half-normal random variables. Various aspects of maximum-likelihood estimation for the coefficients of a production function with an additive disturbance term of this sort are then considered.
The debate concerning quality of care in hospitals, its "value" and affordability, is increasingly of concern to providers, consumers, and purchasers in the United States and elsewhere. We undertook an exploratory study to estimate the impact on hospital-wide costs if quality-of-care levels were varied. To do so, we obtained costs and service output data regarding 300 U.S. hospitals, representing approximately a 5% cross section of all hospitals operating in 1983; both inpatient and outpatient services were included. The quality-of-care measure used for the exploratory analysis was the ratio of actual deaths in the hospital for the year in question to the forecasted number of deaths for the hospital; the hospital mortality forecaster had earlier (and elsewhere) been built from analyses of 6 million discharge abstracts, and took into account each hospital's actual individual admissions, including key patient descriptors for each admission. Such adjusted death rates have increasingly been used as potential indicators of quality, with recent research lending support for the viability of that linkage. The authors then utilized the economic construct of allocative efficiency relying on "best practices" concepts and peer groupings, built using the "envelopment" philosophy of Data Envelopment Analysis and Pareto efficiency. These analytical techniques estimated the efficiently delivered costs required to meet prespecified levels of quality of care. The marginal additional cost per each death deferred in 1983 was estimated to be approximately $29,000 (in 1990 dollars) for the average efficient hospital. Also, over a feasible range, a 1% increase in the level of quality of care delivered was estimated to increase hospital cost by an average of 1.34%. This estimated elasticity of quality on cost also increased with the number of beds in the hospital.
In this paper, we report the results of an analysis of hospital expenses in 43 large SMSAs between 1980 and 1984. We found that hospital rate regulation--specifically Medicare's TEFRA and PPS and state multi-payer systems--was the single most important factor leading to the slowdown in the rate of increase in hospital costs between 1980 and 1984. In 1984, hospital costs covered by Medicare's PPS were 12.5% lower than they would have been in the absence of rate regulation, and in the four states covered by all-payer rate regulation, hospital costs were between 11% and 15% lower. In contrast, changes in the proportion of people either covered by employer-group health insurance or enrolled in HMOs, reduced hospital costs by less than 1%. Measures of competition suggest that hospital costs are higher where there is more competition. We also found that almost all of the effect of regulation on costs came from gains in the efficiency of producing hospital care and/or from reductions in the quality of care. It appears that controlling hospital payment rates gave hospitals a strong incentive to provide care at lower cost.
The Health Care Financing Administration (HCFA) publishes hospital mortality rates each year. We undertook a study to identify characteristics of hospitals associated with variations in these rates. To do so, we obtained data on 3100 hospitals from the 1986 HCFA mortality study and the American Hospital Association's 1986 annual survey of hospitals. The mortality rates were adjusted for each hospital's case mix and other characteristics of its patients. The mortality rate for all hospitalizations was 116 per 1000 patients. Adjusted mortality rates were significantly higher for for-profit hospitals (121 per 1000) and public hospitals (120 per 1000) than for private not-for-profit hospitals (114 per 1000; P less than 0.0001 for both comparisons). Osteopathic hospitals also had an adjusted mortality rate that was significantly higher than average (129 per 1000; P less than 0.0001). Private teaching hospitals had a significantly lower adjusted mortality rate (108 per 1000) than private nonteaching hospitals (116 per 1000; P less than 0.0001). Adjusted mortality rates were also compared for hospitals in the upper and lower fourths of the sample in terms of certain hospital characteristics. The mortality rates were 112 and 121 per 1000 for the hospitals in the upper and lower fourths, respectively, in terms of the percentage of physicians who were board-certified specialists (P less than 0.0001), 112 and 120 per 1000 for occupancy rate (P less than 0.0001), 113 and 120 per 1000 for payroll expenses per hospital bed (P less than 0.0001), and 113 and 119 per 1000 for the percentage of nurses who were registered (P less than 0.0001).
Urban Institute Report 91-10, Determinants of hospital costs: outputs, inputs, and regulation in the 1980s
- J Hadley
- S Zuckerman
Hadley, J. and S. Zuckerman, 1991, Urban Institute Report 91-10, Determinants of hospital costs: outputs, inputs, and regulation in the 1980s (Washington, DC).
The econometric approach to efficiency measurement The measurement of productive efficiency: Techniques and applications
- W Green
Green, W., 1992, The econometric approach to efficiency measurement, in: H. Fried, CA. Knox Love11 and S. Schmidt, eds., The measurement of productive efficiency: Techniques and applications (Oxford University Press).
Technical and allocative inefficiencies of United States hospitals under a stochastic frontier approach, for presentation at the Midwest Economics Association Fifty-fifth Annual Meeting
- R A Hofler
- S T Folland
Hofler, R.A. and S.T. Folland, 1991, Technical and allocative inefficiencies of United States hospitals under a stochastic frontier approach, for presentation at the Midwest Economics Association Fifty-fifth Annual Meeting (St. Louis, MO).
Returns to scale in electricity supply Prospective Payment Assessment Commission
- M Nerlove
Nerlove, M., 1963, Returns to scale in electricity supply, in: C. Christ et al., eds., Measurement in economics (Stanford University Press), 167-198. Prospective Payment Assessment Commission [ProPAC], 1992, Report to Congress, Medicare and the American Health Care System (Washington, DC).
Introduction to the rationale and methods of disease staging
- Systemetrics
- Inc
SysteMetrics, Inc., 1991, Introduction to the rationale and methods of disease staging (SysteMetrics, Inc., Santa Barbara, CA).
Medicare hospital mortality information: lY87
- L W Sullivan
- G R Wilensky
Sullivan, L.W. and G.R. Wilensky, 1991, Medicare hospital mortality information: lY87, 1988, 1989 (Health Care Financing Administration, Washington, DC).
Estimating hospital costs
- Grannemann
The econometric approach to efficiency measurement
- Green