I learned about Eliaso at the always amazing 2015 Exponential Medicine Conference. A patient, named Mark, came on stage and talked about having a rare, aggressive cancer—squamous cell carcinoma of the thymus. It had progressed despite what his doctors, from some of the leading centers in the US, believed to be the very best treatment for his tumor. His prognosis was grim.
But, I thought to myself as I looked at this healthy-appearing man on stage, he really doesn’t look like a man dying of his disease. And, of course, he wasn’t. Mark went on to tell the audience it was because he had met Ran Goshen, MD, Ph.D., an Israeli physician and entrepreneur on a mission to change how cancer treatment plans are formulated. What’s up with that, I wondered. So I asked Ran to spend some time explaining what he was doing.
The Eliaso approach
Eliaso’s approach does not offer up wonder drugs or miracle cures. Instead, it is based on a process Ran calls “collaborative decision making.” It involves not only gathering all of the world’s knowledge about the disease at hand, but also learning from the patient how he makes decisions, what is important to him, and what frightens him.
Currently, the process is resource-intense involving an ad hoc team of people who spend thousands of hours painstakingly pulling together data about every aspect of the tumor and coming up with “out of the box” conclusions based on that information. The team works in what Ran says is a utopian way, ignoring the misaligned incentives that are inherent in today’s healthcare systems. They don’t have to focus on guidelines or regulations and they can ignore costs. They simply pull together the knowledge and organize it in a way that allows new conclusions to be drawn.
The Eliaso team then presents the data and conclusions to the patient and treating team so that they can make an informed collaborative decision of how to go forward. Ran emphasizes that Eliseo is not acting as the patient’s healthcare provider, rather, the team is playing the role of clinical advocate.
In Mark’s case, the analysis suggested an old drug—readily available but not previously used for Mark’s type of tumor—could be efficacious. When the information was presented to Mark’s medical team, they didn’t accept it—in fact, they said it was stupid—leaving Mark to pay out-of-pocket for the test that was needed to convince them. Because he could, Mark paid $2,000 to determine that the drug, indeed, was the best treatment for him. He eventually got it and the result led this healthy, now cancer-free man standing on the stage at Exponential Medicine telling his story.
Can the process be scaled?
I asked Ran how this expensive, resource intense utopian process could be made available to everybody, not just the select few with the ability to access and pay for it. He said he hopes that big data companies (Google, Microsoft, Intel, Dell, etc.) will get involved and help to automate the process making it less expensive and more readily available. But, he points out, this type of approach is not for everybody. Some people will prefer to delegate their treatment decisions instead of directing them.
My feeling is that this is not only a potentially life-saving approach for some patients, but it also produces an extremely valuable side benefit: the generation of new knowledge, including “new druggable targets,” for specific cancers, that will almost certainly be used to help pave the superhighway to hyper-personalized cancer care—and that is a very good thing.