The evaluation and management of outcomes risk has become an essential element of a modern total joint replacement program. Our multidisciplinary team designed an evidence-based tool to address modifiable risk factors for adverse outcomes after primary hip and knee arthroplasty surgery. Our protocols were designed to identify, intervene, and mitigate risk through evidence-based patient optimization. Nurse navigators screened patients preoperatively, identified and treated risk factors, and followed patients for 90 days postoperatively. We compared patients participating in our optimization program (N=104) to both a historical cohort (N=193) and a contemporary cohort (N=166).Background
Methods
Pressure to control health care costs may limit the ability of new implants to enter the market. Customized individually made (CIM) knee implants are produced from CT scans of each patient and may result in improved clinical outcomes based on early data showing less blood loss, reduced bone resection, and better implant function and alignment. Limited economic evidence suggests that the use of CIM technology may result in cost savings, particularly when post-discharge expenses are included. The purpose of this study was to evaluate real-world cost data to determine episode spending in a Medicare population receiving either CIM or off-the-shelf (OTS) implants. The Yale Center for Musculoskeletal Care and Baker Tilly Healthcare Management reviewed episode expenditures among Medicare beneficiaries who received CIM and OTS implants for TKA between 01/01/2015 and 12/31/2015. Episode costs included the pre-operative CT scan, index TKA procedure, and 12-month post-index spending for inpatient (IP), outpatient (OP), emergency room (ER), skilled nursing facility (SNF), and home health (HH) services. CIM patients were identified through a matching process utilizing de-identified patient demographic and procedural information and the presence of a CT scan 28–365 days before index. OTS patients included those without a CT scan within one year of index. CIM and OTS cohorts were propensity matched to produce comparable cohorts at a one-to-five ratio based on age range, gender, race, geographical location, and comorbidities. Average expenditure was used to calculate one-year costs of care differences between the CIM and the OTS technologies. A Generalized Linear Model (GLM) and two-part model (logistics and GLM) were used to test statistical significance.Introduction
Methods
