Ravi B. Parikh, MD, on How Machine Learning-Triggered Reminders Can Improve End-of-Life Care for Cancer Patients

When cancer advances to an incurable stage, some patients may prioritize treatment that will extend their life as long as possible, and others may prefer a care plan that’s designed to minimize pain. Talking to patients about their prognosis and values can help clinicians develop care plans that are better aligned to each patient’s goals. However, it’s essential that the discussions happen before patients become too ill. The results of a long-term clinical trial showed electronic nudges delivered to health care clinicians based on a machine learning algorithm that predicts mortality risk quadrupled rates of conversations with patients about their end-of-life care preferences (JAMA Oncol 2023; doi:10.1001/jamaoncol.2022.6303). The study, published by Penn Medicine investigators, also found that the machine learning-triggered reminders significantly decreased use of aggressive chemotherapy and other systemic therapies at end of life. Oncology Times interviewed study author Ravi B. Parikh, MD, about the results. Parikh is an oncologist and Assistant Professor of Medical Ethics and Health Policy and Medicine in the Perelman School of Medicine at the University of Pennsylvania and Associate Director of the Penn Center for Cancer Care Innovation at Abramson Cancer Center.