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The scale of hospital production in different settings: One size does not fit all

Author

Listed:
  • Mette Asmild

    (Institute of Food and Resource Economics, University of Copenhagen)

  • Bruce Hollingsworth

    (Division of Health Research, Lancaster University, UK)

  • Stephen Birch

    (Centre for Health Economics and Policy Analysis, McMaster University, Canada)

Abstract

This paper analyses the productive efficiency of 141 public hospitals from 1998-2004 in two Canadian provinces; one a small province with a few small cities and a generally more rural population and the other a large province that is more urban in nature, with a population who mainly live in large cities. The relative efficiencies of the hospitals, the changes in productivity during this time period, and the relationship between efficiency and the size or scale of the hospitals are investigated using data envelopment analysis. The models for the production of health care use case mix adjusted hospital discharges as the output, and nursing hours as inputs. We find clear differences between the two provinces. Making use of ‘own’ and ‘meta’ technical efficiency frontiers, we demonstrate that efficient units in the larger and more urban province are larger than non-efficient units in that province. However, efficient hospitals in the smaller and more rural province are smaller than non-efficient hospitals in that province. Overall, efficient hospitals in the larger more urban province are larger than efficient hospitals in the smaller more rural province. This has interesting policy implications - different hospitals may have different optimal sizes, or different efficient modes of operation, depending on location, the population they serve, and the policies their respective provincial governments wish to implement. In addition, there are lessons to be learned by comparing the hospitals across the two provinces, since the inefficient hospitals in the small rural province predominantly use hospitals from the large urban province as benchmarks, such that substantially larger improvement potential can be identified by inter-provincial rather than intra-provincial benchmarking analysis.

Suggested Citation

  • Mette Asmild & Bruce Hollingsworth & Stephen Birch, 2012. "The scale of hospital production in different settings: One size does not fit all," MSAP Working Paper Series 04_2012, University of Copenhagen, Department of Food and Resource Economics.
  • Handle: RePEc:foi:msapwp:04_2012
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    References listed on IDEAS

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    Cited by:

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    2. Antony Andrews & Omphile Temoso & Sean Kimpton, 2021. "Persistent and Transient Inefficiency of Australian States and Territories in Providing Public Hospital Services: An Application of Bayesian Stochastic Finite Mixture Frontier Analysis," Economic Papers, The Economic Society of Australia, vol. 40(2), pages 104-115, June.
    3. Monica Giancotti & Annamaria Guglielmo & Marianna Mauro, 2017. "Efficiency and optimal size of hospitals: Results of a systematic search," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-40, March.
    4. George Fragkiadakis & Michael Doumpos & Constantin Zopounidis & Christophe Germain, 2016. "Operational and economic efficiency analysis of public hospitals in Greece," Annals of Operations Research, Springer, vol. 247(2), pages 787-806, December.
    5. Cheng, Xiaomei & Bjørndal, Endre & Bjørndal, Mette, 2015. "Optimal Scale in Different Environments – The Case of Norwegian Electricity Distribution Companies," Discussion Papers 2015/22, Norwegian School of Economics, Department of Business and Management Science.
    6. Mitropoulos, Panagiotis & Talias, Μichael A. & Mitropoulos, Ioannis, 2015. "Combining stochastic DEA with Bayesian analysis to obtain statistical properties of the efficiency scores: An application to Greek public hospitals," European Journal of Operational Research, Elsevier, vol. 243(1), pages 302-311.
    7. George Fragkiadakis & Michael Doumpos & Constantin Zopounidis & Christophe Germain, 2016. "Operational and economic efficiency analysis of public hospitals in Greece," Post-Print hal-01414677, HAL.
    8. See, Kok Fong & Ng, Ying Chu, 2021. "Do hospital reform and ownership matter to Shenzhen hospitals in China? A productivity analysis," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 145-155.
    9. Víctor Giménez & Jorge R. Keith & Diego Prior, 2019. "Do healthcare financing systems influence hospital efficiency? A metafrontier approach for the case of Mexico," Health Care Management Science, Springer, vol. 22(3), pages 549-559, September.
    10. Panagiotis Mitropoulos & Panagiotis D. Zervopoulos & Ioannis Mitropoulos, 2020. "Measuring performance in the presence of noisy data with targeted desirable levels: evidence from healthcare units," Annals of Operations Research, Springer, vol. 294(1), pages 537-566, November.
    11. Ashraf Mahate & Samer Hamidi & Fevzi Akinci, 2017. "Measuring the Effect of Size on Technical Efficiency of the United Arab Emirates Hospitals," Global Journal of Health Science, Canadian Center of Science and Education, vol. 9(3), pages 116-116, March.

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    More about this item

    Keywords

    Data Envelopment Analysis (DEA); Scale; Efficiency; Hospitals; Provinces;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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