Optimizing colonoscopy screening for colorectal cancer prevention and surveillance

Fatih Safa Erenay, Oguzhan Alagoz, Adnan Said

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

Abstract

Millions of Americans undergo colonoscopy screening for colorectal cancer (CRC) prevention and surveillance every year. The efficiency of colonoscopy operations depends on how often patients are screened, which is a complex and controversial decision, as reflected by the discrepancy between clinical practice and guidelines. We develop a partially observable Markov decision process to optimize colonoscopy screening policies for the objective of maximizing total quality-adjusted life years. Our model incorporates age, gender, and risk of having CRC into the screening decisions and therefore provides a novel framework for personalized CRC screening. In addition to deriving the maximum attainable benefit from colonoscopy screening, which reflects the opportunity cost of following current guidelines, our results have several policy implications. Using clinical data, we show that the optimal colonoscopy screening policies may be more aggressive than the guidelines under some conditions. Optimal screening policies recommend that females with CRC history undergo colonoscopy more frequently than males. In contrast, females without CRC history should be screened less frequently than males. This result, which was not recognized before, signifies the role of gender in optimal CRC screening decisions.

Original languageEnglish (US)
Pages (from-to)381-400
Number of pages20
JournalManufacturing and Service Operations Management
Volume16
Issue number3
DOIs
StatePublished - 2014
Externally publishedYes

Funding

Keywords

  • Cancer screening
  • Colonoscopy
  • Colorectal cancer prevention and surveillance
  • Operations research applications in healthcare
  • Partially observable Markov decision processes
  • Stochastic modeling

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research

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