London Daniel A, Vilensky Seth, O'Rourke Colin, Schill Michelle, Woicehovich Lynn, Froimson Mark I
Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio.
Center for Connected Care, Cleveland Clinic, Cleveland, Ohio.
J Arthroplasty. 2016 Apr;31(4):743-8. doi: 10.1016/j.arth.2015.10.014. Epub 2015 Nov 5.
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.
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.
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.
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.
全关节置换术总成本的高达55%来自急性后期护理，其差异很大程度上取决于患者的出院地点。在我们机构，我们确定了一组使用术前出院计划方案的外科医生，该方案强调了在家出院的优点。我们假设使用该方案会增加患者回家出院的几率。
回顾性分析了3年期间进行的14315例全髋关节和膝关节置换术的管理数据，以确定患者出院地点的预测因素。当我们考虑患者、外科医生和医院层面的预测因素时，使用贝叶斯分层逻辑回归模型来解释数据中的复杂多层次结构。还使用了具有逻辑回归分析的简化病例对照数据结构，以更好地理解术前出院计划方案的影响。
多种患者和外科医生层面的变量可预测全关节置换术后患者回家出院的情况，包括患者的住院时间、年龄、疾病严重程度和保险情况，以及外科医生的所属机构。在病例对照数据中，与未接触快速康复方案的患者相比，接触该方案的患者回家出院的几率增加了45%。
虽然已知患者因素在预测出院后目的地方面起作用，但该分析描述了预测出院地点的其他外科医生和医院层面的因素。基于外科医生和医院工作人员与患者的实践和互动方式的外部因素可能会影响出院后决策过程，并提供节省成本的机会。
