Digital data processing of medical and health information promises to save lives and reduce healthcare costs. By examining the health records and data produced by wearable Health Tech devices from thousands or even millions of people, Big Data analytics should be able to identify correlations that lead to better prevention, early detection, and more effective treatment of disease and other health problems. The good news is that this is already working.

In a research paper published last week on the online peer-reviewed PLOS One site, a team led by researchers from the Stanford Center for Biomedical Informatics Research report that the use of a common heartburn treatment increases the risk of heart attack for some patients. The study used “de-identified” data from electronic health records (EHRs) from Stanford University hospitals and from Practice Fusion, a company that provides free online EHR services for small medical practices. They accessed information from nearly 3 million patients.

The study looked for a correlation between the use of certain types of heartburn medication — proton pump inhibitors (PPI) — by patients diagnosed with acid reflux and the occurrence of heart attacks. They found that the risk of dying from a heart attack doubled for this group compared with patients who used a different type of heartburn medication. The study also cites a number of questions about the results; for example, it could be that those patients using PPI medication might be sicker in general than the others. As with almost all scientific studies, this research points out the need for further study, but its basic conclusions appear to raise valid concerns about this class of medications for this population.

In other words, it looks a lot like another win for Big Data in medical research.