How A.I. is helping doctors triage patients in urgent care
How A.I. is helping doctors triage patients in urgent care
IT Data Solutions
Healthcare experts are hopeful that artificial intelligence could help doctors figure out which patients need urgent care.
At Stanford, physicians at the university’s healthcare facilities will debut, in a month or so, an experimental machine learning-powered triage system. Unlike the machine learning-powered chatbot used by healthcare giant Providence Health & Services that helps patients schedule appointments, Stanford’s software is intended to be used by internal staff to deal with sudden influxes of patients, which can overburden physicians.
The heart of the triaging system is a machine-learning algorithm developed by healthcare firm Epic that analyzes data stored in a patient’s electronic health records. Using data like a patient’s respiratory rate, blood count, and heart rate, the machine learning software can predict whether a patient warrants a visit to the intensive care unit.
Stanford physician Ron Li told Fortune that the machine learning model isn’t “telling us something we wouldn’t know.” Doctors studying the same charts that the software is looking at would likely derive the same conclusion. After all, it doesn’t take a brain surgeon to deduce that patients would probably need immediate help if their heart rate suddenly skyrockets.
When the system debuts, doctors and nurses will receive alerts from their smartphones and computers that the technology has identified a high-risk patient that may need attention, Li said. If a particularly ill patient is staying overnight, for instance, a doctor and nurse handling the nightshift may get an alert from the software to convene at the patient’s bedside to discuss what to do next. Part of the software’s appeal is that it could help nurses and clinicians, who may have differing opinions about interpreting the health of patients, get on the same page about the best way to treat patients in the heat of the moment, a notion that Li refers to as “a shared a mental model.”
Li sees the ultimate value of the software as changing the way Stanford’s clinicians behave in a way that leads to better care for patients. He acknowledged that it’s hard to quantify behavioral change into some form of statistic that indicates a return on investment, or ROI. Indeed, many companies are struggling to report seeing any “value” from their A.I. investments, since it’s difficult to quantify the worth of a successful project beyond merely reducing costs or generating sales.
Clearly, if Stanford’s upcoming test, or pilot, of the new software leads to a reduction in deaths, that would be a success. “I hope it does,” Li said.
Even if that’s not the case, merely getting clinicians and nurses on the same page will be beneficial, Li said. It’s a smaller milestone than reducing deaths, but a more realistic outcome.
