The future of clinical pathways largely depends on alternative payment models such as the Oncology Care Model sponsored by CMS, according to Edward Stepanski, PhD.
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What inspired you to work in oncology?
My decision to move into oncology research was motivated by two major factors. First, I believe that oncology has the strongest, most fully developed science. Molecular genomics is a good example of how work in oncology is driving the leading edge of advances, with a trickle-down effect of these technologies to other therapeutic areas. Secondly, the scope of the challenge and the stakes for patients and their families are extremely high. Tackling the hardest problems is exciting to me. We all have friends and family who are affected, and the personal side of cancer and its treatment is a motivator. My youngest sister is a ‘cancer survivor’, and anyone familiar with the disease understands the special significance of that designation; it has a deeply meaningful status. Conducting work to improve care and outcomes for cancer patients is as professionally satisfying as any work I can imagine.
How have you seen clinical pathways integrated in internal medicine?
There has been a long-standing debate in medicine between empirical ‘cookbook’ approaches to treatment, as compared to decisions made based on the ‘art’ of being a healer. The past two or three decades have seen an increasing reliance on empirically-driven approaches to decision-making, particularly in the treatment of high base-rate chronic diseases. So we see algorithms used that programmatically dictate treatment decisions in the management of hypertension, diabetes, and asthma as good examples. These algorithms do not rise to the level of ‘clinical pathways’ as we understand them in oncology practice, but do represent successful implementation and use of empirically-validated treatment approaches in internal medicine. The concept of clinical pathways as used in oncology generally assumes a linkage to payment, such that off-pathway treatment may be associated with reimbursement repercussions. That dimension is entering into the chronic disease algorithms where use of generic drugs are required at treatment initiation because a payer might require failure on a generic drug before authorizing payment for a new, more expensive agent from the same class. Another distinction is that oncology treatment decision-making is exceptionally complex due both to disease characteristics as well as the severity of treatment-related toxicity. What might be the primary treatment in another disease state is relegated to ‘supportive care’ in the context of oncology care. Therefore, there are many more decision nodes in a comprehensive oncology pathway program, complicating successful implementation and compliance measurement.
What do you believe is the biggest influencer of developing and advancing clinical pathways?
We would like to think that improving treatment outcomes balanced with quality of life endpoints would guide development of clinical pathways programs. These considerations will certainly continue to direct the content of pathways programs, but the primary force behind expanding implementation will be payer-driven initiatives to contain healthcare costs. This influence can be readily appreciated when considering the impact of alternative payment models, such as the Oncology Care Model sponsored by CMS. If payment is based on a bundled care model, will it be sensible to implement and deliver care through a clinical pathways model? Data on outcomes and cost under various approaches to oncology care will have much to do with the future of clinical pathways programs.
What do you believe is the potential of immunotherapy to transform oncology care?
Recent innovations in immuno-oncology (IO) treatment have already had an enormous impact on survival in melanoma, and new therapies are improving outcomes in lung cancer and other tumor types. Importantly, many patients obtain these favorable responses with less toxicity than traditional chemotherapy. It is exciting to see a growing number of patients with advanced melanoma with 5+ years of survival and going strong. The potential of IO to generate more advances with continued discovery about underlying mechanisms and best practices has no ceiling in view. But we also have some of the same limitations observed with earlier classes of drugs—large numbers of patients who do not benefit, with a limited ability to predict response in a given patient. Improvement in assays to measure relevant biomarkers will provide an immediate boost, and continued discovery on how to best manipulate the immune system can hopefully continue successful design of treatment regimens that continue to push out survival times for advanced cancers.
