Skip to main content

Advertisement

SpringerLink
Log in

A fuzzy logic approach toward solving the analytic enigma of health system financing

  • Original Papers
  • Published:
The European Journal of Health Economics, formerly: HEPAC Aims and scope Submit manuscript

Abstract

Improved health, equity, macroeconomic efficiency, efficient provision of care, and client satisfaction are the common goals of any health system. The relative significance of these goals varies, however, across nations, communities and with time. As for health care finance, the attainment of these goals under varying circumstances involves alternative policy options for each of the following elements: sources of finance, allocation of finance, payment to providers, and public-private mix. The intricate set of multiple goals, elements and policy options defies human reasoning, and, hence, hinders effective policymaking. Indeed, "health system finance" is not amenable to a clear set of structural relationships. Neither is there a universe that can be subject to statistical scrutiny: each health system is unique. "Fuzzy logic" models human reasoning by managing "expert knowledge" close to the way it is handled by human language. It is used here for guiding policy making by a systematic analysis of health system finance. Assuming equal welfare weights for alternative goals and mutually exclusive policy options under each health-financing element, the exploratory model we present here suggests that a German-type health system is best. Other solutions depend on the welfare weights for system goals and mixes of policy options

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Similar content being viewed by others

References

  1. Arrow KJ (1963) Uncertainty and the welfare economics of medical care. Am Econ Rev 53:41–973

  2. Beck K, Spycher S, Holly A, Gardiol L (2001) Risk adjustment in Switzerland. Presented at the Risk Adjustment Network Meeting. Amsterdam, April

  3. Buchner F, Wasen J (2001) Needs for further improvement: risk adjustment in the German health insurance system. Presented at the Risk Adjustment Network Meeting. Amsterdam, April

  4. Chernichovsky D (1995) Health system reforms in industrialized democracies: an emering paradigm. Milbank Q 73:339–372

  5. Chernichovsky D (1995) What can developing economies learn from developed economies? Health Policy 32:79–91

  6. Chernichovsky D (2000) The public-private mix in the modern health care system: concepts, issues, and policy options revisited. NBER working paper no W7881, September

  7. Chernichovsky D (2002) Pluralism, choice, and the state in the emerging paradigm in health systems. Milbank Q 80:5–39

  8. Chernichovsky D, Chinitz D (1995) The political economy of health system reform in Israel. Health Econ 4:127–141

  9. Chernichovsky D, Potapchik E (1999) Genuine federalism in the Russian health care system; changing roles of government. J Health Polit Policy Law 34:116–114

  10. Chernichovsky D, Shirom A (1997) Equity and the Israeli health care system. In: Kop Y (ed) The allocation to social services in Israel. Jerusalem: Center for Policy Studies

  11. Chernichovsky D, Barnum H, Potapchik E (1996) Health system reform in Russia: the finance and organization perspectives. Econ Transition 4:113–134

  12. Chernichovsky D, Kirsanova T, Potapchik E, Sosenskaya E (1998) Inequality of health finance, resources, and mortality in Russia: potential implications for health and medical care policy. Health Health Care Health Econ, pp 253–270

  13. Clair US, Klir GJ, Yuan B (1997) Fuzzy set theory: foundations and applications. New York: Prentice Hall

  14. Hsiao W (1995) Medical savings accounts: lessons from Singapore. Health Aff (Millwood) 14:260–266

  15. Jang R, Gulley N (1995) Fuzzy logic toolbox for use with MathLab. Mathworks

  16. Kampe de Feriet J (1982) Interpretation of membership functions of fuzzy sets in terms of plausibility and belief. Gupta MM, Sanchez E (eds) Fuzzy information and decision process. Amsterdam: North-Holland, pp 93–98

  17. Kim K, Moody P (1992) More resources better health? A cross-national perspective. J Soc Sci Med 34:837–842

  18. Klir GJ (1993) Developments in uncertainty-based information. In: Yovitis MC (ed) Advances in computers, vol 36. San Diego: Academic, pp 255–332

  19. Lamotte M, Leverat L, Rondeau L, Ruelas R (1997) A defuzzification method respecting the fuzzification. Fuzzy Sets Syst 86:311–320

  20. Macintyre S (1989) The role of health services in relation to inequalities in health in europe. In: Fox J (ed) Health inequalities in European countries. London: Gower, pp 317–332

  21. Massaro T A, Wong YN (1995) Positive experience with medical savings accounts in Singapore. Health Aff (Millwood) 14:266–272

  22. McNeil D, Freiberger P (1993) Fuzzy logic: the discovery of a revolutionary computer technology and how it is changing our world. New York: Touchstone, pp 11–12

  23. Oates WE (1972) Fiscal federalism. New York: Harcourt Brace Jovanovich

  24. Reischauer RD (1995) Medicare: what to do? Brooking Rev 13, no 3

  25. Wagstaff A, van Doorstaer E (1992) Equity in the delivery of health care: some international comparisons. J Health Econ 11:389–411

  26. Wagstaff A, van Doorstaer E (1992) Equity in the financing of health care: some international comparisons. J Health Econ 11:361–387

  27. World Bank (1993) World development report 1993: investing in health. Oxford University Press, New York

  28. Yip WC, Hsiao WC (1997) Medical savings accounts: lessons from China. Health Aff (Millwood) 16:244–251

Download references

Author information

Author notes

  1. Dov Chernichovsky

    Present address: Health, Nutrition, and Population Unit, World Bank, Washington, DC, 20433, USA

Authors and Affiliations

  1. Health, Nutrition, and Population Unit, World Bank, Washington, DC

    Dov Chernichovsky

  2. Department of Health Policy and Management, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Dov Chernichovsky

  3. National Bureau of Economic Research, Israel

    Dov Chernichovsky

  4. Department of Epidemiology, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Arkady Bolotin

  5. Department of Medical Computing, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    David de Leeuw

Authors
  1. Dov Chernichovsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arkady Bolotin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David de Leeuw
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dov Chernichovsky.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chernichovsky, D., Bolotin, A. & de Leeuw, D. A fuzzy logic approach toward solving the analytic enigma of health system financing. HEPAC 4, 158–175 (2003). https://doi.org/10.1007/s10198-003-0168-3

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10198-003-0168-3

Keywords

Search

Navigation