AIMTo determine social, logistical and demographic factors that influence time to discharge in a short stay pathway (SSP) by following total knee arthroplasty (TKA).METHODSThe study included primary TKA’s performed in a high-volume arthroplasty center from January 2016 through December 2016. Potential variables associated with increased hospital length of stay (LOS) were obtained from patient medical records. These included age, gender, race, zip code, body mass index (BMI), number of pre-operative medications used, number of narcotic medications used, number of patient reported allergies (PRA), simultaneous bilateral surgery, tobacco use, marital status, living arrangements, distance traveled for surgery, employment history, surgical day of the week, procedure end time and whether the surgery was performed during a major holiday week. Multivariate step-wise regression determined the impact of social, logistical and demographic factors on LOS.RESULTSEight hundred and six consecutive primary SSP TKA’s were included in this study. Patients were discharged at a median of 49 h (post-operative day two). The following factors increased LOS: Simultaneous bilateral TKA [46.1 h longer (P < 0.001)], female gender [4.3 h longer (P = 0.012)], age [3.5 h longer per ten-year increase in age (P < 0.001)], patient-reported allergies [1.1 h longer per allergy reported (P = 0.005)], later procedure end-times [0.8 h longer per hour increase in end-time (P = 0.004)] and Black or African American patients [6.1 h longer (P = 0.047)]. Decreased LOS was found in married patients [4.8 h shorter (P = 0.011)] and TKA’s performed during holiday weeks [9.4 h shorter (P = 0.011)]. Non-significant factors included: BMI, median income, patient’s living arrangement, smoking status, number of medications taken, use of pre-operative pain medications, distance traveled to hospital, and the day of surgery.CONCLUSIONThe cost of TKA is dependent upon LOS, which is affected by multiple factors. The clinical care team should acknowledge socio-demographic factors to optimize LOS.