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Patient's Social Network Predicts Drug Outcomes


Networking for cures: James Heywood

Technology Review

Earlier this month, the journal Lancet Neurology published a study showing that the generic drug lithium did nothing to slow the course of amyotrophic lateral sclerosis (ALS), a devastating neurological disease. The findings would likely have been a disappointment to patients--they refuted an earlier, much smaller study suggesting that lithium could alter the disease's rapid decline--but many already suspected this outcome. Eighteen months earlier, PatientsLikeMe, a for-profit patient networking site and data aggregator based in Cambridge, MA, had come to a similar conclusion, much more quickly and at much less cost.

The site, part social networking and part health 2.0, has gathered a wealth of data on its 65,000 members, which span 16 different disease communities, including epilepsy, fibromyalgia, and depression. It provides users with tools to track their health status and communicate with other patients, and then removes the personal details and sells the data to pharmaceutical companies and others. The company's cofounder, James Heywood, believes the site will ultimately change the way drugs and other interventions are evaluated. Heywood, his brother Ben, and a former MIT classmate, Jeff Cole, founded PatientsLikeMe in 2006 as a way to help a third brother, Stephen, who was diagnosed with ALS in 1998.

The approach won't replace clinical trials, at least anytime soon. But some experts do believe it could have enormous benefits, highlighting how different types of patients use drugs, when they stop, or what side effects they experience. "The beauty of observational trials is that you can see how an intervention works in the real world," says Mark Roberts, a physician and professor of Health Policy and Management at the University of Pittsburgh. For example, many trials eliminate patients with secondary ailments, such as renal failure or chronic obstructive pulmonary disorder. "All my patients have those things, so how do I know it works in people I see?" he says.

From Technology Review
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