EHRs are a potential treasure trove of information beyond the needs and circumstances of specific patients, but who has time to pore through them to find it? It turns out, perhaps, another machine. That's the thinking behind a study published recently at npj Digital Medicine that specifically explored using deep machine learning methods to extract information regarding the post-surgical safety performance of implanted medical devices, in this case using hip replacements as a test case. To be sure, any medical device has to meet safety standards set by the FDA, but as Nigam Shah, PhD, associate professor of medicine and biomedical data science at Stanford and the study's lead author, pointed out, "The safety standards required by the FDA are for initial approval of the device's use. What we need is a scalable way -- beyond self-reporting -- to see how safe and effective these devices are in a population after years of use."