From Hospice News: Mortality predictions and behavioral nudges by machine learning systems are associated with increased advance care planning utilization and reductions in high-acuity care. Researchers at the University of Pennsylvania recently studied the effectiveness of machine learning algorithms in identifying high-risk cancer patients nearing the last six months of life between June 17, 2019 and April 20, 2020. During that time period, clinicians received weekly lists of these patients generated by the system, as well as ongoing email and text message prompts sent to clinicians to initiate goals-of-care discussions. The study’s results mark an important step in the role that artificial intelligence can play in improving end-of-life outcomes, according to researcher Dr. Ravi Parikh, oncologist and assistant professor of medical ethics and health policy and medicine at the University of Pennsylvania’s Perelman School of Medicine. Parikh is also associate director of the Penn Center for Cancer Care Innovation. “This study demonstrates that we can use informatics to improve care at [the] end of life,” Parikh told local news. “Communicating with cancer patients about their goals and wishes is a key part of care and can reduce unnecessary or unwanted treatment at the end of life. The problem is that we don’t do it enough, and it can be hard to identify when it’s time to have that conversation with a given patient.” These machine learning-based interventions led to increased rates of serious illness conversations among 13.5% of the 20,506 cancer patients examined, the study found. This was a “significant increase” compared to 3.4% of patients who held advance care planning conversations prior to deployment of the machine learning algorithms, researchers said. Read more at Hospice News.