Timing the Use of Breast Cancer Risk Information in Biopsy Decision-Making

Mehmet Ulvi Saygi Ayvaci, Mehmet Eren Ahsen, Srinivasan Raghunathan, Zahra Gharibi

Research output: Contribution to journalArticlepeer-review

Abstract

Available clinical evidence is inconclusive on whether radiologists should use the patient risk profile information when interpreting mammograms. On the one hand, risk profile information is informative and can improve radiologists’ performance, but on the other hand, it may impair their judgment by introducing biases in mammography interpretation. Therefore, it is important to assess whether and when profile information use translates into improved outcomes. We model the use of profile information in mammography, using a decision theoretic approach and explore the value of profile information using three process design choices: mammography only, unbiased, and biased reading. We estimate the parameters of our model using clinical data and find that using profile information along with the mammography information can achieve a better performance than not using the profile information. However, the better performance is contingent on the weight assigned to the profile information as well as the extent of bias due to profile information. Translating our findings into clinical practice would require properly designed experiments aiming to quantify the effect of the timing and the use of profile information on performance while accounting for radiologist and patient characteristics. When conducting an experiment is not feasible, a uniform operational sequence for interpreting mammograms and related guidelines may be a useful starting point to improve the quality of mammography operations.

Original languageEnglish (US)
Pages (from-to)1333-1358
Number of pages26
JournalProduction and Operations Management
Volume26
Issue number7
DOIs
StatePublished - Jul 2017
Externally publishedYes

Keywords

  • anchoring
  • breast cancer
  • decision bias
  • decision theory
  • mammography

ASJC Scopus subject areas

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

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