Statistical challenges in development of equitable approaches to risk-guided cancer screening using EHR data

Oregon Convention Center 

Personalized medicine holds the promise of improving population health by targeting interventions to those individuals most likely to experience benefits. Risk-guided medical practice uses prediction models to identify individuals at high probability of experiencing an outcome of interest or high probability of benefiting from additional medical intervention to support personalized decision making. However, when these risk models are constructed using data from electronic health records, medically-underserved populations and historically marginalized populations may not experience the anticipated benefits of personalization due to underrepresentation in risk-model development data and poorer quality data. These challenges can lead to poorer risk model performance and perpetuation of historical inequities in health outcomes. In this talk we explore statistical challenges in risk-guided cancer screening arising due to selection bias and differential outcome ascertainment in underrepresented race and ethnicity groups using the example of risk-targeted breast cancer screening and propose alternative approaches to minimize these biases.