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Optimizing a portfolio of health care programs in the presence of uncertainty and constrained resources

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  • Sendi, Pedram
  • Al, Maiwenn J.
  • Gafni, Amiram
  • Birch, Stephen

Abstract

Much research has been devoted to handling uncertainty in cost-effectiveness analysis. The current literature suggests summarizing uncertainty in cost-effectiveness analysis using acceptability curves or net health benefits. These approaches, however, focus only on uncertainty associated with costs and effects of the programs under consideration. In the real world, most decision-makers have to fund a portfolio of health care programs. Therefore, a more comprehensive approach would include in the analysis the uncertainty of costs and effects of all programs supported by the fixed budget. This paper extends the decision rule described by Birch and Gafni (J. Health Econ. 11(3) (1992) 279) within the context of a portfolio of programs when costs and effects are uncertain and resources constrained.

Suggested Citation

  • Sendi, Pedram & Al, Maiwenn J. & Gafni, Amiram & Birch, Stephen, 2003. "Optimizing a portfolio of health care programs in the presence of uncertainty and constrained resources," Social Science & Medicine, Elsevier, vol. 57(11), pages 2207-2215, December.
    • Handle: RePEc:eee:socmed:v:57:y:2003:i:11:p:2207-2215
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    Cited by:

    1. Kirwin, Erin & Meacock, Rachel & Round, Jeff & Sutton, Matt, 2022. "The diagonal approach: A theoretic framework for the economic evaluation of vertical and horizontal interventions in healthcare," Social Science & Medicine, Elsevier, vol. 301(C).
    2. McKenna, Claire & Chalabi, Zaid & Epstein, David & Claxton, Karl, 2010. "Budgetary policies and available actions: A generalisation of decision rules for allocation and research decisions," Journal of Health Economics, Elsevier, vol. 29(1), pages 170-181, January.
    3. Baines, Darrin & Disegna, Marta & Hartwell, Christopher A., 2021. "Portfolio frontier analysis: Applying mean-variance analysis to health technology assessment for health systems under pressure," Social Science & Medicine, Elsevier, vol. 276(C).
    4. Lettieri, Emanuele & Masella, Cristina, 2009. "Priority setting for technology adoption at a hospital level: Relevant issues from the literature," Health Policy, Elsevier, vol. 90(1), pages 81-88, April.
    5. Lettieri, Emanuele, 2009. "Uncertainty inclusion in budgeting technology adoption at a hospital level: Evidence from a multiple case study," Health Policy, Elsevier, vol. 93(2-3), pages 128-136, December.
    6. Pérez Odeh, Rodrigo & Watts, David & Flores, Yarela, 2018. "Planning in a changing environment: Applications of portfolio optimisation to deal with risk in the electricity sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3808-3823.
    7. Qiuzhuo Ma & Krishna P Paudel & Liting Gu & Xiaowei Wen, 2018. "An application of a cardinality-constrained multiple benchmark tracking error model on a plant enterprise selection problem," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(5), pages 677-721.
    8. Feenstra, T.L. & van Baal, P.M. & Jacobs-van der Bruggen, M.A.M. & Hoogenveen, R.T. & Kommer, G.J. & Baan, C.A., 2011. "Targeted versus universal prevention. A resource allocation model to prioritize cardiovascular prevention," Other publications TiSEM da2bcf9c-cba4-4f97-86bf-a, Tilburg University, School of Economics and Management.

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