Due to the implementation of legislation from the Centers of Medicare and Medicaid, acute care hospitals can no longer afford delayed patient discharges due to non-medical reasons. Even so, discharge planning in a hospital setting remains fragmented and inefficient, resulting in an extended length of stay by 2-4 days (Stefanacci, 2019). The purpose of this study was to identify variables that can predict post-acute discharge destinations and create a prediction model to improve care efficiency. A retrospective cohort consisting of 8,324 patients was examined from a level one trauma center in the southeast. Home with no services (HNS), home with services (HCS), skilled nursing facility (SNF), and inpatient rehabilitation (IRF) were the identified discharge settings. Final results demonstrated that age, insurance coverage, and physical limitations described by the 6-clicks scores were significant p < .05 predictors of discharge status. Relative to HNS, HCS, SNF, and IRF had decreased odds of having higher “6-clicks scores” increased odds of being older, and increased odds of having Medicare, Medicaid, Private, and Medicare Advantage payor source relative to self-pay. Receiver operator curves were run to determine sensitivity and 1-specificity for the 6-clicks scores. The area under the curve was significant at .84, and a raw cutoff score of 17.5 differentiated home from SNF and IRF discharge.