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Scale, efficiency and organization in Norwegian psychiatric outpatient clinics for children

Author

Listed:
  • Hallsteinli, Vidar

    (SINTEF Unimed, Health Services Research)

  • Kittelsen, Sverre AC

    (Ragnar Frisch Centre for Economic Research)

  • Magnussen, Jon

    (SINTEF Unimed, Health Services Research)

Abstract

In this paper, the authors examine the scale, efficiency and organization of Norwegian psychiatric outpatient clinics for children. Their question is whether there is room for improved performance in these clinics, and how much? Assuming that about 5 per cent of the Norwegian population under 18 years sometimes is in need of specialist psychiatric care, it is clear that this group will suffer when we know that psychiatric services were delivered to only 2.1 per cent of the whole Norwegian population (data from 1998). Based on a relatively low number of registered consultations per therapist (1,1 per therapist day) the ministry has stipulated that productivity can increase with as much as 50 per cent. Access to services can be improved by increasing capacity, but also by increasing the utilization of the existing capacity. With an Data Envelopment Analysis (DEA) the authors estimate a best-practice production frontier. The potential for efficiency improvement is measured as the difference between actual and best-practice performance, while allowing for trade-offs between different staff groups and different mixes of service production. Based on 135 observations for the years 1997 to 1999, the DEA tests lead to a model with two inputs, two outputs and variable returns to scale. The outputs are number of hours spent on direct and indirect interventions, while neither the number of interventions nor the number of patients where found to be significant. The inputs are the number of university-educated staff and other staff, but disaggregating the latter group was not significant. The analysis show that a average of estimated clinic efficiencies is 71%. Mean estimated productivity is 64%, but many large clinics have considerably lower performance due mainly to scale inefficiency.

Suggested Citation

  • Hallsteinli, Vidar & Kittelsen, Sverre AC & Magnussen, Jon, 2009. "Scale, efficiency and organization in Norwegian psychiatric outpatient clinics for children," HERO Online Working Paper Series 2001:8, University of Oslo, Health Economics Research Programme.
  • Handle: RePEc:hhs:oslohe:2001_008
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    File URL: http://www.hero.uio.no/publicat/2001/HERO2001_8.pdf
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    References listed on IDEAS

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    1. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    2. GIJBELS, Irène & MAMMEN, Enno & PARK, Byeong U. & SIMAR, Léopold, 1997. "On estimation of monotone and concave frontier functions," LIDAM Discussion Papers CORE 1997031, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Tsybakov, A.B. & Korostelev, A.P. & Simar, L., 1992. "Efficient Estimation of Monotone Boundaries," Papers 9209, Catholique de Louvain - Institut de statistique.
    4. Kittelsen,S.A.C., 1999. "Monte Carlo simulations of DEA efficiency measures and hypothesis tests," Memorandum 09/1999, Oslo University, Department of Economics.
    5. Simar, Leopold & Wilson, Paul W., 2002. "Non-parametric tests of returns to scale," European Journal of Operational Research, Elsevier, vol. 139(1), pages 115-132, May.
    6. Kneip, A & Park, B-U & Simar, L, 1996. "A Note on the Convergence of Nonparametric DEA Efficiency Measures," Papers 9603, Catholique de Louvain - Institut de statistique.
    7. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    9. Fare, Rolf & Knox Lovell, C. A., 1978. "Measuring the technical efficiency of production," Journal of Economic Theory, Elsevier, vol. 19(1), pages 150-162, October.
    10. Valdmanis, Vivian, 1992. "Sensitivity analysis for DEA models : An empirical example using public vs. NFP hospitals," Journal of Public Economics, Elsevier, vol. 48(2), pages 185-205, July.
    11. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
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    Cited by:

    1. Magnussen, Jon & Nyland, Kari, 2008. "Measuring efficiency in clinical departments," Health Policy, Elsevier, vol. 87(1), pages 1-7, July.

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    More about this item

    Keywords

    Health Care; Productivity; Data Envelopment Analysis;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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