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Technical efficiency in the clinical management of critically ill patients

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  • Jaume Puig‐Junoy

Abstract

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. © 1998 John Wiley & Sons, Ltd.

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  • Jaume Puig‐Junoy, 1998. "Technical efficiency in the clinical management of critically ill patients," Health Economics, John Wiley & Sons, Ltd., vol. 7(3), pages 263-277, May.
    • Handle: RePEc:wly:hlthec:v:7:y:1998:i:3:p:263-277
    DOI: 10.1002/(SICI)1099-1050(199805)7:3<263::AID-HEC320>3.0.CO;2-I
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    Cited by:

    1. Finn R. Forsund, 2002. "Categorical Variables in DEA," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(1), pages 33-44, April.
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    3. Diogo Ferreira & Rui Cunha Marques, 2018. "Identifying congestion levels, sources and determinants on intensive care units: the Portuguese case," Health Care Management Science, Springer, vol. 21(3), pages 348-375, September.
    4. Puig-Junoy, Jaume, 2000. "Partitioning input cost efficiency into its allocative and technical components: an empirical DEA application to hospitals," Socio-Economic Planning Sciences, Elsevier, vol. 34(3), pages 199-218, September.
    5. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    6. Burnett, Royce D. & Hansen, Don R., 2008. "Ecoefficiency: Defining a role for environmental cost management," Accounting, Organizations and Society, Elsevier, vol. 33(6), pages 551-581, August.
    7. Pierre Ouellette & Valérie Vierstraete, 2010. "Malmquist indexes with quasi-fixed inputs: an application to school districts in Québec," Annals of Operations Research, Springer, vol. 173(1), pages 57-76, January.
    8. Thanassoulis, Emmanuel & Silva Portela, Maria C.A. & Graveney, Mike, 2014. "Using DEA to estimate potential savings at GP units at medical specialty level," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 38-48.
    9. Dervaux, B. & Leleu, H. & Minvielle, E. & Valdmanis, V. & Aegerter, P. & Guidet, B., 2009. "Performance of French intensive care units: A directional distance function approach at the patient level," International Journal of Production Economics, Elsevier, vol. 120(2), pages 585-594, August.
    10. Simon Eckermann & Tim Coelli, 2008. "Including quality attributes in a model of health care efficiency: A net benefit approach," CEPA Working Papers Series WP032008, School of Economics, University of Queensland, Australia.
    11. Elenka Brenna & Federico Spandonaro, 2014. "Does federalism induce patients’ mobility across regions? Evidence from the Italian experience," DISCE - Working Papers del Dipartimento di Economia e Finanza def009, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
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    13. Pereira, Miguel Alves & Figueira, José Rui & Marques, Rui Cunha, 2020. "Using a Choquet integral-based approach for incorporating decision-maker’s preference judgments in a Data Envelopment Analysis model," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1016-1030.
    14. Gian Paolo Barbetta & Gilberto Turati & Angelo M. Zago, 2007. "Behavioral differences between public and private not‐for‐profit hospitals in the Italian national health service," Health Economics, John Wiley & Sons, Ltd., vol. 16(1), pages 75-96, January.
    15. Berta, Paolo & Callea, Giuditta & Martini, Gianmaria & Vittadini, Giorgio, 2010. "The effects of upcoding, cream skimming and readmissions on the Italian hospitals efficiency: A population-based investigation," Economic Modelling, Elsevier, vol. 27(4), pages 812-821, July.
    16. Robert Stefko & Beata Gavurova & Kristina Kocisova, 2018. "Healthcare efficiency assessment using DEA analysis in the Slovak Republic," Health Economics Review, Springer, vol. 8(1), pages 1-12, December.
    17. Eckermann, Simon & Coelli, Tim, 2013. "Including quality attributes in efficiency measures consistent with net benefit: Creating incentives for evidence based medicine in practice," Social Science & Medicine, Elsevier, vol. 76(C), pages 159-168.
    18. Thanassoulis, Emmanuel & Silva Portela, Maria & Graveney, Mike, 2016. "Identifying the scope for savings at inpatient episode level: An illustration applying DEA to chronic obstructive pulmonary disease," European Journal of Operational Research, Elsevier, vol. 255(2), pages 570-582.
    19. Nahra, Tammie A. & Mendez, David & Alexander, Jeffrey A., 2009. "Employing super-efficiency analysis as an alternative to DEA: An application in outpatient substance abuse treatment," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1097-1106, August.

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