OR Practice–Data Analytics for Optimal Detection of Metastatic Prostate Cancer

Selin Merdan (), Christine L. Barnett (), Brian T. Denton (), James E. Montie () and David C. Miller ()
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Selin Merdan: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Christine L. Barnett: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Brian T. Denton: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
James E. Montie: Department of Urology, University of Michigan, Ann Arbor, Michigan 48109; Michigan Urological Surgery Improvement Collaborative, Ann Arbor, Michigan 48109
David C. Miller: Department of Urology, University of Michigan, Ann Arbor, Michigan 48109; Michigan Urological Surgery Improvement Collaborative, Ann Arbor, Michigan 48109

Operations Research, 2021, vol. 69, issue 3, 774-794

Abstract: We used data-analytics approaches to develop, calibrate, and validate predictive models, to help urologists in a large statewide collaborative make prostate cancer staging decisions on the basis of individual patient risk factors. The models were validated using statistical methods based on bootstrapping and evaluation on out-of-sample data. These models were used to design guidelines that optimally weigh the benefits and harms of radiological imaging for the detection of metastatic prostate cancer. The Michigan Urological Surgery Improvement Collaborative, a statewide medical collaborative, implemented these guidelines, which were predicted to reduce unnecessary imaging by more than 40% and limit the percentage of patients with missed metastatic disease to be less than 1%. The effects of the guidelines were measured after implementation to confirm their impact on reducing unnecessary imaging across the state of Michigan.

Keywords: programming: nonlinear; applications; statistics; healthcare; OR practice; healthcare; prostate cancer: radiographic staging; semisupervised learning; class imbalance problem; cost-sensitive learning; verification bias (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:69:y:2021:i:3:p:774-794

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