Daniel A London1, Seth Vilensky2, Colin O'Rourke3, Michelle Schill4, Lynn Woicehovich4, Mark I Froimson4. 1. Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio. 2. Center for Connected Care, Cleveland Clinic, Cleveland, Ohio. 3. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio. 4. Euclid Hospital, Cleveland Clinic, Euclid, Ohio.
Abstract
BACKGROUND: Up to 55% of total joint arthroplasty costs come from post-acute care, with large variability dependent on a patient's discharge location. At our institution, we identified a group of surgeons using a preoperative discharge planning protocol emphasizing the merits of home discharge. We hypothesized that using the protocol would increase patients' odds for discharge home. METHODS: Administrative data from 14,315 total hip and knee arthroplasties performed over a 3-year period were retrospectively analyzed to determine predictors of patient discharge location. Bayesian hierarchical logistic regression modeling was used to account for the complex multilevel structure within the data as we considered patient-, surgeon-, and hospital-level predictors. A simplified case-control data structure with logistic regression analysis was also used to better understand the impact of the preoperative discharge planning protocol. RESULTS: A variety of patient- and surgeon-level variables are predictive of patients being discharged home after total joint arthroplasty including a patient's length of stay, age, illness severity, and insurance, as well as surgeon's affiliation. In the case-control data, patients exposed to the rapid recovery protocol had 45% increased odds of being discharged home compared to patients not exposed to the protocol. CONCLUSIONS: Although patient factors are known to play a role in predicting postdischarge destination, this analysis describes additional surgeon- and hospital-level factors that predict discharge location. Exogenous factors based on how surgeons and hospital staff practice and interact with patients may impact the postdischarge decision-making process and provide a cost savings opportunity.
BACKGROUND: Up to 55% of total joint arthroplasty costs come from post-acute care, with large variability dependent on a patient's discharge location. At our institution, we identified a group of surgeons using a preoperative discharge planning protocol emphasizing the merits of home discharge. We hypothesized that using the protocol would increase patients' odds for discharge home. METHODS: Administrative data from 14,315 total hip and knee arthroplasties performed over a 3-year period were retrospectively analyzed to determine predictors of patient discharge location. Bayesian hierarchical logistic regression modeling was used to account for the complex multilevel structure within the data as we considered patient-, surgeon-, and hospital-level predictors. A simplified case-control data structure with logistic regression analysis was also used to better understand the impact of the preoperative discharge planning protocol. RESULTS: A variety of patient- and surgeon-level variables are predictive of patients being discharged home after total joint arthroplasty including a patient's length of stay, age, illness severity, and insurance, as well as surgeon's affiliation. In the case-control data, patients exposed to the rapid recovery protocol had 45% increased odds of being discharged home compared to patients not exposed to the protocol. CONCLUSIONS: Although patient factors are known to play a role in predicting postdischarge destination, this analysis describes additional surgeon- and hospital-level factors that predict discharge location. Exogenous factors based on how surgeons and hospital staff practice and interact with patients may impact the postdischarge decision-making process and provide a cost savings opportunity.
Authors: Ran Schwarzkopf; Jerry Arraut; Mark Kurapatti; Thomas H Christensen; Joshua C Rozell; Vinay K Aggarwal; Kenneth A Egol Journal: Arch Orthop Trauma Surg Date: 2022-02-24 Impact factor: 3.067
Authors: Kevin T Pritchard; Ickpyo Hong; James S Goodwin; Jordan R Westra; Yong-Fang Kuo; Kenneth J Ottenbacher Journal: J Am Med Dir Assoc Date: 2020-10-09 Impact factor: 7.802