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Preference‐Sensitive Management of Post‐Mammography Decisions in Breast Cancer Diagnosis

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  • Mehmet Ulvi Saygi Ayvaci
  • Oguzhan Alagoz
  • Mehmet Eren Ahsen
  • Elizabeth S. Burnside

Abstract

Decision models representing the clinical situations where treatment options entail a significant risk of morbidity or mortality should consider the variations in risk preferences of individuals. In this study, we develop a stochastic modeling framework that optimizes risk‐sensitive diagnostic decisions after a mammography exam. For a given patient, our objective is to find the utility maximizing diagnostic decisions where we define the utility over quality‐adjusted survival duration. We use real data from a private mammography database to numerically solve our model for various utility functions. Our choice of utility functions for the numerical analysis is driven by actual patient behavior encountered in clinical practice. We find that invasive diagnostic procedures such as biopsies are more aggressively used than what the optimal risk‐neutral policy would suggest, implying a far‐sighted (or equivalently risk‐seeking) behavior. When risk preferences are incorporated into the clinical practice, policy makers should bear in mind that a welfare loss in terms of survival duration is inevitable as evidenced by our structural and empirical results.

Suggested Citation

  • Mehmet Ulvi Saygi Ayvaci & Oguzhan Alagoz & Mehmet Eren Ahsen & Elizabeth S. Burnside, 2018. "Preference‐Sensitive Management of Post‐Mammography Decisions in Breast Cancer Diagnosis," Production and Operations Management, Production and Operations Management Society, vol. 27(12), pages 2313-2338, December.
    • Handle: RePEc:bla:popmgt:v:27:y:2018:i:12:p:2313-2338
    DOI: 10.1111/poms.12897
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    Cited by:

    1. Naumzik, Christof & Feuerriegel, Stefan & Nielsen, Anne Molgaard, 2023. "Data-driven dynamic treatment planning for chronic diseases," European Journal of Operational Research, Elsevier, vol. 305(2), pages 853-867.
    2. Robert Kraig Helmeczi & Can Kavaklioglu & Mucahit Cevik & Davood Pirayesh Neghab, 2023. "A multi-objective constrained partially observable Markov decision process model for breast cancer screening," Operational Research, Springer, vol. 23(2), pages 1-42, June.
    3. Sun, Huan & Wang, Haiyan & Steffensen, Sonja, 2022. "Mechanism design of multi-strategy health insurance plans under asymmetric information," Omega, Elsevier, vol. 107(C).

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