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Knee

“BUNDLE BUSTERS”: WHO IS AT RISK FOR EXCEEDING THE TARGET PAYMENT, AND CAN THEY BE OPTIMIZED?

The Knee Society (TKS) 2018 Members Meeting, Saint Louis, MO, USA, September 2018.



Abstract

Introduction

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.”

Methods

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.

Results

Of the 412 patients, 123 were bundle busters (30%). There was a median institutional loss of $11,797 (IQR, $4,312 – $26,771) for the bundle busters and a median gain of $7,402 ($5,657 – $9,206) for the non-busters. Of the 32 risk factors evaluated, 11 were identified as Independent risk factors for busting the bundle (all, p<0.05). Nine of the 11 (82%) are non-modifiable risk factors and include age, disease specific diagnoses (fracture and avascular necrosis), and medical comorbidities (congestive heart failure, pulmonary circulation disorders, renal disease, cardiac arrhythmia, chronic pulmonary disease, and neurological disorder). The remaining two medical comorbidities are potentially modifiable and include diabetes with complications, and preoperative anemia.

Conclusion

Though modifiable risk factors should continue to be optimized prior to TJA, as they were in this population, there are still many non-modifiable preoperative risk factors that can lead to costs exceeding the BPCI established institutional payment goal. As such, further work with payors may be needed to help fairly and appropriately consider these non-modifiable factors which result in increased costs.