Swedish Cancer Institute, Edmonds, WA; American Society of Clinical Oncology, Alexandria, VA; Center for Cancer and Blood Disorders, Weatherford, TX; WellRithms, Portland, OR; Parkland Health System; University of Texas Southwestern, Dallas; University of Texas MD Anderson Cancer Center, Houston, TX; and University of Chicago, Chicago, IL.
INTRODUCTION:This analysis evaluates the impact of bundling drug costs into a hypothetic bundled payment. METHODS:An economic model was created for patient vignettes from: advanced-stage III colon cancer and metastatic non-small-cell lung cancer. First quarter 2016 Medicare reimbursement rates were used to calculate the average fee-for-service (FFS) reimbursement for these vignettes. The probabilistic risk faced by practices was captured by the type of patients seen in practices and randomly assigned in a Monte Carlo simulation on the basis of the given distribution of patient types within each cancer. Simulations were replicated 1,000 times. The impact of bundled payments that include drug costs for various practice sizes and cancer types was quantified as the probability of incurring a loss at four magnitudes: any loss, > 10%, > 20%, or > 30%. A loss was defined as receiving revenue from the bundle that was less than what the practice would have received under FFS; the probability of loss was calculated on the basis of the number of times a practice reported a loss among the 1,000 simulations. RESULTS:Practices that treat a substantial proportion of patients with complex disease compared with the average patient in the bundle would have revenue well below that expected from FFS. Practices that treat a disproportionate share of patients with less complex disease, as compared with the average patient in the bundle, would have revenue well above the revenue under FFS. Overall, bundled payments put practices at greater risk than FFS because their patient case mix could greatly skew financial performance. CONCLUSION:Including drug costs in a bundle is subject to the uncontrollable probabilistic risk of patient case mixes.
J Oncol Pract (Journal of oncology practice)
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