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

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)2313-2338
Number of pages26
JournalProduction and Operations Management
Issue number12
StatePublished - Dec 2018
Externally publishedYes


  • breast cancer
  • dynamic programming
  • healthcare analytics
  • medical decision-making
  • preferences
  • risk-sensitive Markov decision processes
  • utility theory

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Management of Technology and Innovation


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