Uncovering preferences from patient list data using benefit efficient models

Jan  Ubøe; Jostein  Lillestøl

doi:10.4236/jbise.2010.38106

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JBiSE Vol.3 No.8 , August 2010

Uncovering preferences from patient list data using benefit efficient models

Jan Ubøe, Jostein Lillestøl

Abstract: In this paper it is shown how the benefit efficient patient list model of Ubøe and Lillestøl [1] can be used to infer strength of preferences from patient list data. It is proved that the model allows the construction of unique sets of preferences replicating the observed allocations. To illustrate how this theory can be applied in practice, preferences are uncovered from a small data set, obtained from the Norwegian patient list system.

Keywords: Patient Lists; Efficient Welfare; Statistical Distributions

Cite this paper: nullUbøe, J. and Lillestøl, J. (2010) Uncovering preferences from patient list data using benefit efficient models. Journal of Biomedical Science and Engineering, 3, 799-806. doi: 10.4236/jbise.2010.38106.

References

[1] Ub?e, J. and Lillest?l, J. (2007) Benefit efficient statistical distributions on patient lists. Journal of Health Economics, 26(4), 800-820.

[2] Varian, H.R. (2006) Revealed preferences. in Szenberg, M., ed., Samuelsonian Economics and the 21st Century , Oxford University Press, 99-115.

[3] Roth, A.E. and Peranson, E. (1999) The redesign of the matching market for American physicians: Some engineering aspects of economic design. The American Economic Review, 89(4), 748-780.

[4] Bishop, Y.M.M. Fienberg, S.E. and Holland, P.W. (1975) Discrete multivariate analysis: Theory and practice. The MIT Press, Cambridge Mass.

[5] Lillest?l, J., Ub?e, J., R?nsen, Y. and Hjortdahl, P. (2007) Patient allocations according to circumstances and preferences. Discussion paper, Norwegian School of Economics and Business Administration, Bergen.

[6] Bregman, L.M. (1967) The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Computational Mathematics and Mathematical Physics, 7, 200-217.