Before any medical device (think pacemakers or hip replacements) reaches the market, it has to meet certain safety standards set by the Food and Drug Administration. But these standards are just a first step -- any number of things can happen when the devices hit the clinic. "The safety standards required by the FDA are for initial approval of the device's use" said Nigam Shah, PhD, associate professor of medicine and biomedical data science. "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." Large numbers of health records detailing the experiences of real-world patients hold the answer, Shah said. And he and his team are using AI to mine them. A paper detailing the findings of the study appears in npj Digital Medicine. Shah is the senior author. Alison Callahan, PhD, research scientist, and Jason Fries, PhD, research scientist, are co-lead authors. There are existing methods to report medical device safety issues to the FDA after approval. "But it requires health providers to be really motivated to do so, and sometimes these things end up getting postponed or drawn out," said Callahan. "We also know that self-reported data introduces bias into any data analysis. So it's challenging to get a clear picture of the safety profile of medical devices," she said. There are other obstacles too. Patient information is often spread across multiple databases, making it difficult to detect any signal for a given device's safety profile or success rates. Shah and his team's AI-based monitoring method gets around that by accessing records from former Stanford patients that have been stripped of personal identifying information.
Medical device safety in the real world: Tapping EHR data