This policy brainstorm white paper is part of Global Policy Lab: Decoding Cancer.
A transformation is underway in cancer care, with both drugs and treatment strategies increasingly personalized for individual patients. The genetic profile of a tumor can tell us which medicines are most likely to work. Teams of specialists will collaborate to work out the best course of treatment for a specific tumor. Therapies like CAR-T even promise to re-engineer patients own cells to fight their specific cancer.
Health systems are not transforming at the same rate. High costs and novel approaches to treatment are confounding old systems that werent designed to pay for them.
The transformation also means operating with a higher degree of uncertainty: Its urgent to give patients a fighting chance, but sometimes drugs are so new, or tested in such small populations, that we dont really know what were getting — or if the results were measuring are actually those most important to patients and their families.
POLITICO Global Policy Lab has been looking at how to move the cancer care system ahead of the science, including ways to make sure advances dont leave some patients behind. In this brainstorm white paper, we lay out the key questions facing policymakers, industry leaders and society as the fight against tumors gets increasingly personal.
The problem: Breakthrough cancer drugs are expensive: They cost a lot to discover, and in many cases, theyre designed for just a small sliver of patients, driving developers to seek high prices. Health systems are struggling to adapt — and cost isnt the only factor. Cancer experts increasingly look to fight tumors together in teams, and new therapies like CAR-T can only be provided in specialized centers. Insurance systems arent necessarily set up to pay for these approaches.
The question: How can health systems provide access to innovative new therapies? What can we learn from the current experience with the newly approved CAR-T treatments Kymriah and Yescarta?
The problem: Regulators are changing their approach to approving cancer drugs for patients with no other options, allowing treatments to enter the market based on smaller studies, though often with more dramatic initial results. The idea is that “real world data” will help us reassess those drugs in the future. For the health systems and insurance funds that have to pay for these drugs, however, determining a fair price amid so much uncertainty is a challenge.
The question: How does the drug price negotiation process need to change to accommodate experimental drugs?
The problem: Some view artificial intelligence as an increasingly important tool for diagnosis and treatment of cancer. But winning the trust of patients, and perhaps more crucially, the doctors whose livelihoods could be transformed by AI, could be a barrier. Likewise, concerns about data protection and confusion about ownership of patient information stand as barriers to personalized medicine that relies on genetic data.
The question: How do we find the right balance of embracing data and AIs potential without undermining trust, privacy and quality of care?
The problem: Theres a growing consensus that specialized cancer centers — where doctors have a lot of experience and can collaborate with experts across different fields of medicine — are key to improving outcomes. But studies also show that living far away from a cancer hospital can lead to later diagnosis and lower-quality care. This tension is especially acute for treating rare and childhood cancers, where there may only be a few experts in Europe.
The question: What are the unique sources of disparities in cancer research, treatment and long-term care, and how do we address them? What are the key barriers to treating rare and childhood cancers, and how can we overcome them?
The problem: “Patient-centered care” has become a buzzword, but its definition is fuzzy. While many clinical studies focus on improving survival, the effect of new drugs on quality of life is often an afterthought in the development process. As new treatments lead to longer lives, many cancer survivors find they face discrimination and long-term health effects.
The question: How can patients priorities be measured and incorporated throughout the treatment process, and beyond?