Integration of a learning prostate cancer risk prediction tool into the electronic medical record
Prostate cancer will account for 26% of new cancer diagnoses in American men in 2015. Most diagnoses are made with a biopsy performed after consideration of a patient’s perceived risk based on demographics, family history, physical exam, and lab testing. Existing risk prediction models are inadequate because they are fixed, poorly calibrated, and do not account for differences in practice patterns. Additionally, modern electronic health records require statistical models to be hard-coded in ways that make model updates extremely cumbersome. We aim to address the grand challenge of improving risk stratification through the following goals: 1) develop a new statistical learning model that continuously calibrates to achieve better risk prediction; 2) build a learning health platform to contain and continuously update the model; and 3) integrate the learning health platform with the electronic health record to provide this information seamlessly to clinicians at the point of care.
Presented at the American Urological Association 2017 Annual Meeting
Evaluation of prostate cancer risk calculators for shared-decision making across diverse urology practices in Michigan
Published in Urology
Developed a prostate cancer risk calculator, askMUSIC, which is online at http://ask.musicurology.com. MUSIC is a statewide quality improvement collaborative in urology. Our tool, askMUSIC, was presented at the statewide MUSIC meeting held in October...