Evaluating the Addition of Clinical and Staging Data to Improve the Pricing Methodology of the Oncology Care Model. Issue 11 (17th November 2022) - Dreyer, Theresa R.F.; Hamilton, Emily; Dahl, Alyssa; Desai, Bijal; Kircher, Sheetal; Polite, Blase; Schroeder, Cynthia; Fukui, Mayumi; Hayes-Lattin, Brandon; Horvath, Keith A.
Abstract : PURPOSE: The Oncology Care Model (OCM) is the largest value-based care model focusing on oncology, but the current pricing methodology excludes relevant data on the cancer stage and current clinical status, limiting the precision of the risk adjustment. METHODS: This analysis evaluated 15, 580 episodes of breast cancer, lung cancer, and multiple myeloma, starting between July 1, 2016, and January 1, 2020, with data from a cohort of OCM practices affiliated with academic medical centers. The authors merged clinical data with claims for OCM episodes defined by the Center for Medicare and Medicaid Innovation to identify potential quality improvement opportunities. The regression model evaluated the association of the cancer stage at initial diagnosis and current clinical status with variance to the OCM target price. RESULTS: Cancer stage at the time of initial diagnosis was significant for breast and lung cancers, with stage IV episodes having the highest losses of –6, 700 (USD) for breast cancer ( P < .001) and –18, 470 (USD) for lung cancer ( P < .001). Current clinical status had a significant impact for all three cancers in the analysis, with losses correlated with clinical complexity. Breast cancer and multiple myeloma episodes categorized as recurrent or progressive disease had significantly higher losses than stable episodes, at –6, 755 (USD) for breast ( P < .001) and –19, 448 (USD) for multiple myeloma ( P < .001). Lung cancer episodes categorized as initial diagnosis had significantly fewer losses than stable episodes, at –3, 751 (USD) ( P = .001). CONCLUSION: As the Center for Medicare and Medicaid Innovation designs and launches new oncology-related models, the agency should adopt methodologies that more accurately set target prices, by incorporating relevant clinical data within cancer types to minimize penalizing practices that provide guideline-concordant cancer care.