ABSTRACT - UCSF’s 30-day unplanned readmission rates of 9-11% over the past several years is above the target of being in the top decile of peer Academic Medical Centers. There are several readmission reduction programs at UCSF, but enrollment is driven by patient insurance or primary care provider rather than risk of readmission. Our proposal is to use a Machine Learning (ML) model to predict risk of unplanned readmission for patients after discharge from UCSF, and to use this model to enroll high-risk patients into targeted discharge support programs. Our goal is to reduce 30-day unplanned readmissions by 15%. Currently, all Accountable Care Organization patients discharged from UCSF are enrolled in a discharge support program through the Office of Population Health (OPH) that provides an average of one phone call per week in the first month after discharge. If this proposal is selected, we would identify a subset of ACO patients at high-risk for readmission and leverage existing OPH infrastructure to design and provide higher-touch post-discharge support to these patients. Adjusting our ML model to match the resource constraints for this high-touch program, we plan to set our model at a sensitivity of 70%, and a positive predictive value of 17%. Over the course of one year, we estimate we can prevent 63 unplanned readmissions in this small pilot ACO population. This equates to savings of 403 bed days, resulting in nearly $1.2 Million/year of additional contribution margin from backfill. If successful, we aim to expand use of this model beyond ACO patients to the 28,000 annual discharges at UCSF, which would multiply the above benefits five-fold.