As oncology care becomes increasingly complex, traditional attribution models used in value-based care programs may no longer fully reflect how patients are treated in the real world. In this BioPharma Boardroom interview, Jill Hellmann, MSN, RN, Transformation Lead, Value Based Care at The US Oncology Network, discusses the limitations of claims-based cancer attribution, the challenges posed by patients with concurrent cancers, and the opportunities to modernize oncology reimbursement models through the integration of clinical data. She also shares how community oncology can help shape future value-based care strategies and why success should be measured by more than cost reduction alone.

Q: Your presentation challenges traditional approaches to cancer attribution in value-based care models. What are the key limitations of current attribution frameworks?

A: Historically, attribution frameworks in value-based care models focus on administrative claims data. They utilize E&M visits and coding around those visits to decide which type of cancer the patient should be attributed to in a model.

• As cancer treatment evolves, there's a growing population of patients with concurrent cancers. When you have one treatment and one cancer type, attribution aligns well. However, when you have concurrent cancers and one cancer type is being treated with an active treatment regimen and another cancer maybe being managed with hormonal therapy, we can find discordancy in claims between the active treatment diagnosis and the attributed cancer bundle.
• Our abstract highlights the potential gap between the current administrative attribution methodology and the clinical reality of concurrent cancers.  

Q: How can oncology value-based care models better account for the complexity and longitudinal nature of cancer care?

A: The key takeaway from our abstract is that as cancer treatment continues to evolve, there is an opportunity to modernize and evolve attribution logic. A key opportunity would be expanding attribution logic to include administrative claims methodology plus clinical data points to help reconcile any discrepancies behind the scenes between what a patient is being treated for and what the E&M codes are showing as their attributed cancer type.

Q: What unintended consequences can arise when attribution models fail to accurately reflect patient care pathways?

A: In a total cost of care model – for example the EOM – when a patient is attributed to a cancer type that misaligns with the diagnosis associated with their active treatment, their care is evaluated against an inaccurate benchmark. This can become significant if the attributed cancer type carries a lower benchmark price than the cancer type tied to the active treatment. The misalignment can cloud the actual financial and clinical outcomes within the model making it challenging to determine where a participant should target interventions.

Q: How are community oncology networks uniquely positioned to help evolve value-based oncology reimbursement strategies?

A: Community oncology really aligns clinical, operational, and financial levers for driving quality cost effective patient-centered care. This alignment makes community oncology a great space for value-based care opportunities and source of feedback on these models. For instance, The US Oncology Network is appreciative of the EOM team’s willingness to solicit and receive feedback from the Network on its model. By participating in programs and continuing to provide candid feedback on where there are opportunities, benefits, and challenges within value-based care models, community oncology can help inform future models and strategies. 

Q: Looking ahead, what metrics should define success in oncology value-based care beyond cost reduction?

A: This is a really great question. When you begin thinking about what could be applied to value-based care outside of cost, you have a very broad scope of what could be used to define success. It raises the question of whether there is a possibility for value-based care models to move outside of the active treatment episode framework and look to capture quality metrics and patient outcomes that are outside active treatment like surveillance, supportive care treatment, or survivorship – for example.

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