Penn Today: Nudges and machine learning triples advanced care conversations

According to a randomized study of nearly 15,000 patients, an electronic nudge to clinicians tripled the rate of end-of-life conversations, targeting patients with cancers. The electronic nudges are supported by an algorithm that uses machine learning methods to flag patients with cancer who would most benefit from the exchange. “Within and outside of cancer, this is one of the first real-time applications of a machine learning algorithm paired with a prompt to actually help influence clinicians to initiate these discussions in a timely manner, before something unfortunate may happen,” says co-lead author Ravi B. Parikh, an assistant professor of medical ethics and health policy and medicine in the Perelman School of Medicine and a staff physician at the Corporal Michael J. Crescenz VA Medical Center. “And it’s not just high-risk patients. It nearly doubled the number of conversations for patients who weren’t flagged—which tells us it’s eliciting a positive cultural change across the clinics to have more of these talks.” Read more at Penn Today.