The Need for a National Rapid Learning System for Cancer: Pioneer Grantee Lynn Etheridge Featured in Health Affairs
Jun 29, 2010, 5:17 AM, Posted by RWJF Blog Team
Editor's note: Dr. Lynn Etheridge is a grantee working on developing "rapid learning" systems to identify the best practices and promising innovations when it comes to treating a variety of diseases. Yesterday, Etheridge and his colleagues published a special article in the Journal of Clinical Oncology, where they proposed implementing a national rapid-learning cancer strategy. Today, the Health Affairs Blog ran an excellent post by Lynn detailing the need for these RL systems, which we have included below in-full:
Cancer is among the most complicated group of diseases to research and treat. The progress in the federal government’s “war on cancer” launched in the 1970s has been frustratingly slow.
A rapid-learning (RL) cancer system is now possible—using the potential of “in silico” research. Traditional health research has relied on “in vitro” and “in vivo” methods—bench science and experiments. In silico research would add large computerized registries and databases, with many millions of records, Internet-connected research networks, and high speed computers—today’s petaflop computers do a quadrillion operations per second. Such new learning capabilities have critical importance for the cancer system—huge amounts of genetic and other clinical data, on patients and tumors, could be recorded, understood and used in research, treatments, and outcomes analyses. By harnessing all of the quickly-accumulating data on cancer and patient experience, a rapid-learning cancer system could develop knowledge about the optimal treatment for each patient and promptly deliver that information to physicians and patients.
I first proposed a rapid-learning cancer system last year in Health Affairs, and National Cancer Institute (NCI) director John Niederhuber wrote in support of the proposal. The Institute of Medicine, through its National Cancer Policy Forum, responded with a two-day workshop and written report, released earlier this month. The workshop report discusses the building blocks of a RL cancer system, including registries, grid computing, comparative effectiveness research, treatment guidelines, and decision tools. It describes prototype RL models and patient-driven learning systems, the considerable challenges that lie ahead, and a federal action agenda.
Yesterday, the Journal of Clinical Oncology published a study, based on IOM’s work. Lead author Amy Abernethy, I, and others proposed that the Department of Health and Human Services, with committed private sector partners, implement a national rapid-learning cancer strategy.
HHS is now well positioned to launch a new cancer learning strategy. It is the major supporter of cancer research and the major payer for cancer care; it regulates cancer drugs, operates a nationwide bio-informatics grid for cancer data-sharing, and funds a national system of cancer registries, as well as national cancer genetics databases. The Obama administration and Congress have added new funds for cancer research, for comparative effectiveness studies, and for electronic health records for all Americans. Francis Collins, the National Institutes of Health director, led the Human Genome Project that provides foundational science for rapid cancer learning; Harold Varmus, nominated to be NCI director, is a Nobel-prize winning cancer genetics researcher.
It is time to make rapid progress on rapid learning.
This commentary originally appeared on the RWJF Pioneering Ideas blog.