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Evaluating the performance of Brazilian university hospitals

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  • Yasar Ozcan

    ()

  • Marcos Lins

    ()

  • Maria Lobo
  • Angela da Silva
  • Roberto Fiszman
  • Basilio Pereira

Abstract

In order to demonstrate how DEA modeling can be helpful to aid decision making relative to the Brazilian Teaching Hospital Policy by means of hospital performance assessment, we develop a case study with 30 general hospitals linked to Brazilian Federal Universities. We consider data on medical care (Medical Model—MM), teaching and research (Teaching-Research Model—TRM) and use the software IDEAL (Interactive Data Envelopment Analysis Laboratory) as a tool for the units’ efficiency evaluation. IDEAL, developed in Brazil, is unique in providing a 3-D frontiers visualization, assisting in exploratory analysis and allowing a better understanding of the DEA modeling (envelopment and multiplier). Both models are input-oriented and each hospital is categorized according to its relative efficiency in the MM and TRM. In this phase, it is very important to discuss with the decision-makers the results and patterns of the DEA models. Finally, the modelling indicates the necessary changes for the inefficient units and generates recommendations for teaching ratios and public financing. Copyright Springer Science+Business Media, LLC 2010

Suggested Citation

  • Yasar Ozcan & Marcos Lins & Maria Lobo & Angela da Silva & Roberto Fiszman & Basilio Pereira, 2010. "Evaluating the performance of Brazilian university hospitals," Annals of Operations Research, Springer, vol. 178(1), pages 247-261, July.
  • Handle: RePEc:spr:annopr:v:178:y:2010:i:1:p:247-261:10.1007/s10479-009-0528-1
    DOI: 10.1007/s10479-009-0528-1
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    References listed on IDEAS

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    1. Grosskopf, Shawna & Margaritis, Dimitri & Valdmanis, Vivian, 2004. "Competitive effects on teaching hospitals," European Journal of Operational Research, Elsevier, vol. 154(2), pages 515-525, April.
    2. O'Neill, Liam & Rauner, Marion & Heidenberger, Kurt & Kraus, Markus, 2008. "A cross-national comparison and taxonomy of DEA-based hospital efficiency studies," Socio-Economic Planning Sciences, Elsevier, vol. 42(3), pages 158-189, September.
    3. Grosskopf, Shawna & Margaritis, Dimitri & Valdmanis, Vivian, 2001. "The effects of teaching on hospital productivity," Socio-Economic Planning Sciences, Elsevier, vol. 35(3), pages 189-204, September.
    4. Chen, Andrew & Hwang, Yuhchang & Shao, Benjamin, 2005. "Measurement and sources of overall and input inefficiencies: Evidences and implications in hospital services," European Journal of Operational Research, Elsevier, vol. 161(2), pages 447-468, March.
    5. Cook, Wade D. & Zhu, Joe, 2007. "Classifying inputs and outputs in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 180(2), pages 692-699, July.
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    Citations

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    Cited by:

    1. Sebastian Kohl & Jan Schoenfelder & Andreas Fügener & Jens O. Brunner, 2019. "The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals," Health Care Management Science, Springer, vol. 22(2), pages 245-286, June.
    2. Hsihui Chang & Hiu Choy & Iny Hwang, 2015. "An empirical study of returns to scale of CPA firms in the post SOX era," Annals of Operations Research, Springer, vol. 229(1), pages 253-264, June.
    3. Diogo Cunha Ferreira & Rui Cunha Marques, 2016. "Should inpatients be adjusted by their complexity and severity for efficiency assessment? Evidence from Portugal," Health Care Management Science, Springer, vol. 19(1), pages 43-57, March.
    4. Sami Chaabouni & Chokri Abednnadher, 2016. "Cost Efficiency of Tunisian Public Hospitals: a Bayesian Comparison of Random and Fixed Frontier Models," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 7(3), pages 771-781, September.
    5. Akkan, Can & Karadayi, Melis Almula & Ekinci, Yeliz & Ülengin, Füsun & Uray, Nimet & Karaosmanoğlu, Elif, 2020. "Efficiency analysis of emergency departments in metropolitan areas," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    6. Yongjun Li & Xiyang Lei & Alec Morton, 2019. "Performance evaluation of nonhomogeneous hospitals: the case of Hong Kong hospitals," Health Care Management Science, Springer, vol. 22(2), pages 215-228, June.
    7. Bana e Costa, Carlos A. & Soares de Mello, João Carlos C.B. & Angulo Meza, Lidia, 2016. "A new approach to the bi-dimensional representation of the DEA efficient frontier with multiple inputs and outputs," European Journal of Operational Research, Elsevier, vol. 255(1), pages 175-186.
    8. Fengyi Lin & Yung-Jr Deng & Wen-Min Lu & Qian Long Kweh, 2019. "Impulse response function analysis of the impacts of hospital accreditations on hospital efficiency," Health Care Management Science, Springer, vol. 22(3), pages 394-409, September.
    9. Alejandro Arrieta & Jorge Guillén, 2017. "Output congestion leads to compromised care in Peruvian public hospital neonatal units," Health Care Management Science, Springer, vol. 20(2), pages 157-164, June.
    10. Maria Cristina Gramani, 2014. "Inter-Regional Performance of the Public Health System in a High-Inequality Country," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-8, January.
    11. Mehdi Toloo & Mona Barat & Atefeh Masoumzadeh, 2015. "Selective measures in data envelopment analysis," Annals of Operations Research, Springer, vol. 226(1), pages 623-642, March.

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