Pharmaceutical marketers go to great lengths to find the doctors who aren’t prescribing their drugs—and to devise methods to reach them. But MedNetworks, a startup that grew out of the Harvard lab of sociologist and physician Nicholas Christakis, is offering pharmaceutical companies a shortcut. By mining anonymized medical-claims data, the company says, it can identify which doctors may be the strongest influencers of their colleagues.
MedNetworks uses computational tools developed at Christakis’s lab to look at the prescribing patterns of large groups of doctors, build maps of professional ties, and track how the popularity of a new drug grows. The company has found that certain doctors are particularly strong influencers: when these doctors write prescriptions for a newly released drug, colleagues within three degrees of separation soon follow suit. With such historical insights in hand, “we’ve shown that we can predict adoption of pharmaceuticals among doctors,” says MedNetworks cofounder Larry Miller.
This approach could go far beyond aiding drug companies. By gleaning patterns from the seeming chaos of physician partnerships and referrals, patient health records, and prescription-writing records, computational social-analysis tools could also identify which people in a community would be most influential in spreading a public-health message.
To that end, MedNetworks is working on identifying such influencers among citizens in Hermosa Beach, Redondo Beach, and Manhattan Beach, California. Starting with public information such as census data, club rosters, and PTA lists, the company is trying to determine which individuals act as community and neighborhood influencers. The client in that case—Healthways, a consultant for employers trying to drive down health costs—wants to find people who would be most effective in spreading messages about reducing smoking and obesity.
Erik Brynjolfsson, a professor of information technology and director of the MIT Center for Digital Business at the Sloan School of Management, says there are “enormous opportunities” to use data more effectively both within the health-care system and within social-networking research. But, he cautions, “it’s fair to say that sorting out causality in social-network research is very difficult to do.” The best way to determine causality is through rigorous experimentation, with control groups examined alongside experimental ones, he says, and such an approach is rarely possible using retrospective information.
A few years ago, Christakis and colleagues examined data from the Framingham Heart Study—a decades-long study of thousands of people in Framingham, Massachusetts—and studied how obesity, happiness, and other phenomena spread among social networks. Christakis showed that weight gain in individuals increased the likelihood of weight gain for others in that person’s social network, but he could only guess about the mechanism at play.
Marketers already understand that online social networks like Facebook offer new opportunities to expand businesses, and that various software products, such as IBM’s Lotus Connections social software, help people analyze their existing relationships to boost efficiency. But the latest ideas may help them get healthier, too. “There’s a new niche,” says Noshir Contractor, a professor of behavioral sciences at Northwestern University, referring to the application of computational social-science insights to the business world. He has cofounded two consulting companies based on computational social science. “Of most of the social challenges we have, I think health is on the top of the areas that could benefit from network analysis.”
Lauren Cox is a reporter for Technology Review.