PROJECT SUMMARY/ABSTRACT: Survivors of critical illness often face significant challenges, such as neurocognitive disorders, physical disabilities, and respiratory limitations. Healthcare disparities by race heighten such challenges. Risk prediction tools designed to identify high-risk patients for interventions such as post-acute care clinics may exacerbate existing bias among racial and ethnic groups. Intending to address bias in prediction tools for survivors of critical illness, our team brings expertise in health services research focusing on healthcare disparities, statistical modeling, and causal inference. In previous work, we have highlighted racial differences in patients with cardiovascular disease and COVID-19 across different healthcare systems, including the Veterans Health Administration (VHA). Furthermore, we have begun exploring outcomes among survivors of critical illness including mortality and re-admission rates and developed an innovative post-ICU care model showing early indications of reducing hospital readmissions, increasing hospital-free days, and reducing mortality across diverse patient populations. As an F32 grant recipient, I will integrate and build on the expertise of my mentorship to identify and characterize racial disparities within three datasets of critical care illness survivors as defined by mortality, 90-day re-admissions, and hospital-free days (HFDs) at 90 days. In parallel, I will compare bias within two statistical models used to stratify patients by one-year mortality: (1) the Care Assessment Needs (CAN) score, a mortality risk model widely used to guide interventions among Veterans, and (2) the PREDICT score, a simplified one-year mortality risk model used at first patient contact to guide interventions such as palliative care consultation. Statistical fairness is an emerging concept geared toward reducing bias within statistical models and algorithms. To address any identified bias within our models, our team will employ novel approaches to achieve statistical fairness, including double prioritization. In addition to identifying healthcare disparities within a population of increasing care complexity, my proposal investigates statistical models as underlying contributors to healthcare disparities, aiming to rectify such inequalities through refined and equitable modeling approaches. In doing so, we propose establishing a fair care delivery framework for critical illness survivors. With close mentorship from an experienced team in healthcare disparities research, healthcare delivery, and innovative research methodologies at the University of Pittsburgh, this training plan forms a foundation for a Career Development Award and a future career as a physician-scientist.