The Bundled Payments for Care Improvement (BPCI) initiative has identified pathways for improving the value of care. However, patient-specific modifiable and non-modifiable risk factors may increase costs beyond the target payment. We sought to identify risk factors for exceeding our institution’s target payment, the so-called ‘bundle busters’. Using our data warehouse and Centers for Medicare and Medicaid Services (CMS) data we identified all 412 patients who underwent total joint arthroplasty and qualified for our institution’s BPCI model, between July 2015 and May 2017. Episodes where CMS payments exceeded the target payment were considered ‘busters’ (n = 123). Risk ratios (RRs) were calculated using a modified Poisson regression analysis.Aims
Patients and Methods
Recent studies of novel healthcare episode payment models, such as the Bundled Payments for Care Improvement (BPCI) initiative, have demonstrated pathways for improving value. However, these models may not provide appropriate payments for patients with significant medical comorbidities or complications. The objective of this study was to identify risk factors for exceeding our institution's target payment, the so-called “bundle busters.” After receiving an exemption from the Institutional Review Board, we queried our institutional data warehouse for all patients (n=412) that underwent total joint arthroplasty (TJA) of the hip (n=192), knee (n=207), or ankle (n=13), and qualified for our institution's bundled payments model during the study time period (July 2015 – May 2017). Patients with medical conditions that were not well controlled or were potentially optimizable were all sent for preoperative medical optimization prior to surgery. For each 90-day episode, patient characteristics, medical comorbidities, perioperative data, and payments from the Centers for Medicare and Medicaid Services (CMS) were obtained. Episodes where Medicare payments exceeded the target payment were considered “busters”. The busters were older, and had higher comorbidity scores (all, p<0.01). Variables were summarized using descriptive statistics and risk ratios were calculated using a modified Poisson regression analysis.Introduction
Methods
