Nursing home care in The Netherlands: a nonparametric efficiency analysis

Kooreman, Peter

doi:10.1016/0167-6296(94)90029-9

Cited by 132 publications

(113 citation statements)

References 9 publications

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“…Input-oriented TE has also been more widely used, relative to the other efficiency measures, in empirical studies on the nursing home sector (e.g. Nyman and Bricker 1989;Nyman et al 1990;Fizel and Nunnikhoven 1992;Chattopadhyay and Heffley 1994;Kooreman 1994;Ozcan et al 1998;Bjorkgren et al 2001;Laine et al 2005;Wang and Chou 2005;Borge and Haraldsvik 2009;Garavaglia et al 2011;Chang and Cheng 2013;DeLellis and Ozcan 2013). 4 Table A1 presents an overview of the studies which applied different techniques for estimating efficiency in the nursing home sector in various countries.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…DEA is the dominant approach to efficiency measurement in healthcare (Hollingsworth 2003(Hollingsworth , 2008. In relation to nursing homes, DEA was used to estimate technical efficiencies in the USA by Nyman and Bricker (1989), Nyman et al (1990), Fizel and Nunnikhoven (1992), Kleinsorge and Karney (1992), Chattopadhyay and Heffley (1994), Chattopadhyay and Ray (1996), Ozcan et al (1998), andDeLellis andOzcan (2013); and by Kooreman (1994) for the Netherlands; Bjorkgren et al (2001), and Laine et al (2005) for Finland; Borge and Haraldsvik (2009) for Norway; Garavaglia et al (2011) for Italy;and Wang and Chou (2005) and Chang and Cheng (2013) for Taiwan.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…However, Wilson (2007, 2011) assert that whatever the second-stage regression technique employed, conventional inference methods fail to give valid inference due to the fact that in the secondstage, true efficiency remains unobserved and must be replaced with DEA estimates of efficiency, and these are serially correlated by construction, and are also biased. 5 Previous two-stage DEA nursing home efficiency studies include Nyman et al (1990), Fizel and Nunnikhoven (1992), Kooreman (1994), Ozcan et al (1998), Wang and Chou (2005), and Garavaglia et al (2011). In these models, the determining factors posited are drawn from the following set of variables: ownership, case-mix (e.g.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…It should be noted, however, that the conceptual output -improved health status, or even more generally, improved quality of life -is difficult to measure (Kooreman, 1994). Furthermore, the concept of 'value-added' as a result of engaging with the 'service' has proved more challenging in health care, because of the much greater heterogeneity of service users and the intrinsic measurement difficulties.…”

Section: Output and Input Variables Outputmentioning

confidence: 99%

“…Nyman and Bricker, 1989;and Fizel and Nunnikhoven, 1993). However, the quality variables used by Kooreman (1994) include the presence of a patients' council, the presence a council of patients' relatives, and the presence of a procedure for handling complaints. Laine et al (2005) analyse the association between quality of care and technical efficiency in long-term care in Finland.…”

Section: Qualitymentioning

confidence: 99%

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Technical and scale efficiency in public and private Irish nursing homes – a bootstrap DEA approach

Luasa

Dineen

Zieba

2016

Health Care Manag Sci

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This article provides methodological and empirical insights into the estimation of technical efficiency in the nursing home sector. Focusing on long-stay care and using primary data, we examine technical and scale efficiency in 39 public and 73 private Irish nursing homes by applying an input-oriented data envelopment analysis (DEA). We employ robust bootstrap methods to validate our nonparametric DEA scores and to integrate the effects of potential determinants in estimating the efficiencies. Both the homogenous and two-stage double bootstrap procedures are used to obtain confidence intervals for the bias-corrected DEA scores. Importantly, the application of the double bootstrap approach affords true DEA technical efficiency scores after adjusting for the effects of ownership, size, case-mix, and other determinants such as location, and quality. Based on our DEA results for variable returns to scale technology, the average technical efficiency score is 62%, and the mean scale efficiency is 88%, with nearly all units operating on the increasing returns to scale part of the production frontier. Moreover, based on the double bootstrap results, Irish nursing homes are less technically efficient, and more scale efficient than the conventional DEA estimates suggest. Regarding the efficiency determinants, in terms of ownership, we find that private facilities are less efficient than the public units. Furthermore, the size of the nursing home has a positive effect, and this reinforces our finding that Irish homes produce at increasing returns to scale. Also, notably, we find that a tendency towards quality improvements can lead to poorer technical efficiency performance.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Output and Input Variables Outputmentioning

confidence: 99%

Section: Qualitymentioning

confidence: 99%

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Technical and scale efficiency in public and private Irish nursing homes – a bootstrap DEA approach

Luasa

Dineen

Zieba

2016

Health Care Manag Sci

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Output Efficiency of Health Maintenance Organizations in Florida

1997

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We use data envelopment analysis (DEA) to measure the relative technical efficiencies of 28 HMOs licensed to practice in the State of Florida in the autumn of 1994. Health care output measures used in the analysis are number of commercial, Medicare and Medicaid members enrolled in each plan. Inputs to the model are capital assets, total expenditures on the provision of medical services and administrative expenses. We find differences in HMO efficiency scores and loss ratios (defined as the ratio of expenses on the provision of medical services to the total expenses incurred by the organization) across individual plans. Differences in efficiency measures across model type (staff, IPA, combination) and ownership types (for-profit, not-for-profit) are small but significant: staff models and for-profits are more efficient. In a multivariate model, we also find that large HMOs are more efficient and HMOs with Medicaid patients are significantly less efficient than other HMOs.

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Technical efficiency in the clinical management of critically ill patients

Puig‐Junoy

1998

Health Econ.

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The purpose of this paper is to obtain empirical measures of performance in the management of critical patients treated in intensive care units (ICUs) and to evaluate the factors associated with performance, in a two stage approach. In the first stage, this paper uses an extended version of Data Envelopment Analysis (non-discretionary and categorical variables, and weight constraints under consideration) to obtain measures of technical efficiency in the treatment of 993 critical care patients in intensive care units in Catalonia (Spain) in 1991-92. The model incorporates accurate individual measures of illness severity from Mortality Probability Models (MPM II0) and quality outcome measures in the input-output set to obtain non-biased efficiency measures. In the second stage, a loglinear regression model is applied to test a number of hypothesis about the role of different environmental factors--such as ownership, market structure, dimension, internal organization, diagnostic, mortality risk, etc.--to explain differences in the efficiency scores.

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Nursing home care in The Netherlands: a nonparametric efficiency analysis

Cited by 132 publications

References 9 publications

Technical and scale efficiency in public and private Irish nursing homes – a bootstrap DEA approach

Technical and scale efficiency in public and private Irish nursing homes – a bootstrap DEA approach

Output Efficiency of Health Maintenance Organizations in Florida

Technical efficiency in the clinical management of critically ill patients

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