RESEARCH SUMMARY / ABSTRACT Despite overall declines in breast cancer mortality in the United States, significant disparities in breast cancer treatment and outcomes persist. Access-related, biological, psychosocial, and provider-specific factors previously demonstrated to be associated with disparities after breast cancer diagnosis are, on the whole, static systemic or individual features that are difficult or impossible to change. As an alternative approach to addressing disparities in breast cancer, we propose creating a risk prediction model for Breast cancer Risk of Inferior Survival and Care (BRISC) that incorporates dynamic, modifiable risk factors to identify women at greatest risk for compromised care and worse survival after diagnosis with breast cancer. After development and validation, we will use the BRISC model to conduct statistical simulations and costing activities to estimate the improvement in value (i.e., outcomes achieved per health-care dollar spent) achieved via implementation of high-impact, risk-modifying interventions to facilitate receipt of guideline-concordant breast cancer treatment. Finally, we will develop a parsimonious, clinic-based data collection tool to be completed by patients and providers for the purpose of operationalizing the BRISC model as a component of routine oncologic care.

PROJECT NARRATIVE We believe the Breast cancer Risk of Inferior Survival and Care (BRISC) prediction model will be an important contributor to shared, clinical decision-making for breast cancer patients. In the future, we hope to develop a smartphone- and tablet-enabled app and web-based calculator that will, respectively, facilitate clinic-based data collection and calculation of the BRISC index as part of routine oncologic care. The BRISC model will allow us to proactively identify some of our most vulnerable patients soon after diagnosis, thereby enabling interventions that can facilitate their receipt of guideline-concordant care and mitigate their risk of avoidable recurrence and cancer-specific mortality.