Analysis of Mammography Screening Policies under Resource Constraints

Mucahit Cevik, Turgay Ayer, Oguzhan Alagoz*, Brian L. Sprague

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Breast cancer, the leading cause of cancer death for women, can be detected at earlier stages through mammography screening. Therefore, most developed countries implemented population-based mammography screening programs. However, cost of mammography and limited resources in terms of number of trained personnel and diagnostic machines prevent mammography screening to be adopted by many other countries. In fact, even in resource-rich countries, there is a growing concern about cost of mammography screening. In this study, we investigate the optimal allocation of limited mammography resources to screen a population. We propose a constrained partially observable Markov decision process (CPOMDP) model that maximizes total expected quality-adjusted life years of the patients when they are allowed only a limited number of mammography screenings. We use a variable resolution grid-based approximation scheme to convert the CPOMDP model into a mixed-integer linear program and conduct several numerical experiments using breast cancer epidemiology data. We observe that as mammography screening capacity decreases, patients in the 40–49 age group should be given the least priority with respect to screening. We further find that efficient allocation of available resources between patients with different risk levels leads to significant quality-adjusted life year gains, especially for the patients with higher breast cancer risk.

Original languageEnglish (US)
Pages (from-to)949-972
Number of pages24
JournalProduction and Operations Management
Issue number5
StatePublished - May 2018
Externally publishedYes


  • breast cancer screening
  • constrained partially observable Markov decision processes
  • health care applications
  • medical decision making

ASJC Scopus subject areas

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


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