What can big data tell us about the predictability of medical conditions? A new study by MIT researchers published in the journal Scientific Reports digs into this question by looking at anonymous data from over 500,000 patients. Among the findings is that our electronic medical records contain data that is up to 90 percent predictable — although this level of predictability is only attainable in theory. However, it can guide algorithmic designers and practitioners on what is possible in principle. The co-authors of the paper are Carlo Ratti, director of MIT’s Senseable City Laboratory, and two former computer science researchers at the lab, Dominik Dahlem (who is the lead author) and Diego Maniloff. The data originated with General Electric, which collaborated with Senseable City on a 2011 project on visually plotting health care data. MIT News spoke with Ratti about the new study.