We used data-analytics approaches to develop, calibrate, and validate predictive models, to help urologists in a large state-wide collaborative make prostate cancer staging decisions on the basis of individual patient risk factors. The models we developed predict the probability a patient who receives radiographic imaging will have metastatic cancer. The models were developed using observational data for patients diagnosed with prostate cancer. We experimented with a variety of machine learning methods and compared their performance at predicting outcomes of imaging. The models were validated using statistical methods based on bootstrapping and subsequent evaluation on out-of-sample data. These models were used to design guidelines that seek to optimally weigh the benefits and harms of radiological imaging for detection of metastatic prostate cancer. The Michigan Urological Surgery Improvement Collaborative, a state-wide 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 post-implementation to confirm their impact on reducing unnecessary imaging across the state of Michigan.
Selin Merdan, Christine Barnett
Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI
James E. Montie, David C. Miller
Department of Urology, University of Michigan, Ann Arbor, MI,
Michigan Urological Surgery Improvement Collaborative, Ann Arbor, MI
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These webinars are an initiative of the INFORMS Practice Section and are organized by Dr. Rajesh Tyagi (RajeshTyagi@yahoo.com).