BACKGROUND: Readmissions from post-acute care to acute care hospitals is well documented in the literature. Inadequate management of infection is a top readmission diagnosis and is often preventable. Nurse driven, protocolized treatment is the gold standard for treating sepsis patients in post and acute care settings. A quality improvement (QI) project was implemented in a 58-bed inpatient rehabilitation facility (IRF) to determine if a nurse-driven sepsis algorithm would decrease readmission rates to acute care hospitals, increase nursing staff sepsis knowledge, clinical confidence and communication. METHODS: This QI project was performed during a 3-month period in 2020. Readmission findings were compared before and after the study intervention implementation. Changes overtime in sepsis frequency were assessed through run charts. Pre- and post- survey data of sepsis knowledge, clinical confidence caring for a sepsis patient and care provider and clinician communication was assessed using a Wilcoxon Signed Rank Test. INTERVENTION: By utilizing the Systemic Inflammatory Response (SIRs) criteria, the Situation, Background, Assessment and Recommendation (SBAR) tool and Surviving Sepsis Campaign guidelines, a nurse driven sepsis algorithm was created and implemented in the rehabilitation facility. Additionally, pre-and post-surveys were administered to assess nurses’ knowledge of sepsis, clinical confidence in caring for a patient with suspected sepsis and bedside clinician to provider communication. RESULTS: Results show a decrease in readmissions to acute care hospitals post intervention but no statistically significant change in pre-post survey of sepsis knowledge, confidence or communication. CONCLUSIONS: An additional PDSA cycle of this QI project is needed to determine a true sepsis decrease overtime using the sepsis algorithm. A sepsis pre and post-test, and role play simulations may be key to a change in sepsis knowledge, confidence and communication. Keywords: inpatient rehabilitation facility, IRF, readmissions, sepsis, infection, surviving sepsis campaign, algorithm