In Hospital ICUs, AI Could Predict Which Patients Are Likely to Die

Hospitals have an understandable goal for their intensive care units: to reduce "dead in bed" events. With streams of data coming from equipment that monitors patients' vital signs, the ICU seems the perfect setting to deploy artificially intelligent tools that could judge when a patient is likely to take a turn for the worse. "A lot of hospitals are interested in developing early warning systems that can predict life-threatening events like sepsis, cardiac arrest, and respiratory arrest," says Priyanka Shah of the ECRI Institute, a nonprofit that evaluates medical procedures, devices, and drugs for the health care industry. Both academic researchers and medical device companies are now trying to figure out which combinations of measurements can provide the best indication of patient deterioration, Shah says. Once that technical challenge is met, researchers will still have to prove "clinical relevance," she says--not just proof that the technology works, but also that it can be integrated into a hospital's workflow and that it will save money.