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Measuring hospital output: A review of the service mix and case mix approaches

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  • Tatchell, Michael

Abstract

This paper presents a survey of the literature on the definition and measurement of hospital output concentrating on those studies that have used either 'service-mix' or 'case-mix' as their basic output measure. The service-mix approach is described as a supply side analysis in which the nature of a hospital's output is determined by the character and range of its facilities and services. In contrast, the case-mix approach is essentially a demand side analysis which regards each hospital's case load as being determined by the needs and demands of the population served, with facilities and services the dependent variables. It is also argued that hospital output measurement has rarely been tackled directly. Typically such work has been secondary to the principle research objectives which have related to the study of hospital cost structure and economies of scale. The units developed have been, for the most part, partial measures which satisfy the requirements of their particular investigators but which, until recently, have had little potential for wider application. The call for more effective cost control in health care through reimbursement systems and review procedures, particularly in the United States, has led to an upsurge of interest in hospital output measurement and to a consequent improvement in techniques.

Suggested Citation

  • Tatchell, Michael, 1983. "Measuring hospital output: A review of the service mix and case mix approaches," Social Science & Medicine, Elsevier, vol. 17(13), pages 871-883, January.
  • Handle: RePEc:eee:socmed:v:17:y:1983:i:13:p:871-883
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    Cited by:

    1. Spetz, Joanne & Maiuro, Lisa Simonson, 2004. "Measuring levels of technology in hospitals," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(3), pages 430-447, July.
    2. Halkos, George E. & Tzeremes, Nickolaos G., 2011. "A conditional nonparametric analysis for measuring the efficiency of regional public healthcare delivery: An application to Greek prefectures," Health Policy, Elsevier, vol. 103(1), pages 73-82.
    3. repec:rdg:wpaper:em-dp2004-19 is not listed on IDEAS
    4. Chang, Hsihui & Cheng, Mei-Ai & Das, Somnath, 2004. "Hospital ownership and operating efficiency: Evidence from Taiwan," European Journal of Operational Research, Elsevier, vol. 159(2), pages 513-527, December.
    5. P. Prezerakos & N. Maniadakis & D. Kaitelidou & N. Kotsopoulos & J. Yfantopoulos, 2007. "Measuring Across Hospital Efficiency and Productivity: The Case of Second Regional Health Authority of Attica," European Research Studies Journal, European Research Studies Journal, vol. 0(1-2), pages 83-96.
    6. Ismet Sahin & Yasar Ozcan & Hacer Ozgen, 2011. "Assessment of hospital efficiency under health transformation program in Turkey," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 19(1), pages 19-37, March.
    7. F. Patrick Hallagan, 1996. "Are Big Hospitals the Best Medicine," The American Economist, Sage Publications, vol. 40(1), pages 31-39, March.
    8. Hongliang Liu & Jinpeng Tan & Kyongson Jon & Wensheng Zhu, 2022. "A New Case-Mix Classification Method for Medical Insurance Payment," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
    9. Alessandra Ferrari, 2006. "NHS Reforms and efficiency of hospital services: a stochastic distance function apporach," Economic Analysis Research Group Working Papers earg-wp2006-06, Henley Business School, University of Reading.

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