Algorithm predicts short-term mortality among patients with cancer, may foster timely discussions of goals

Machine learning algorithms identified patients with cancer who were at risk for short-term mortality and could benefit from immediate discussions about end-of-life preferences, according to results of a prospective study presented at Supportive Care in Oncology Symposium and published simultaneously in JAMA Oncology. "On any given day, it's actually pretty difficult to identify which patients in my clinic would benefit most from a proactive advanced care planning conversation," Ravi B. Parikh, MD, MPP, instructor of medical ethics and health policy at University of Pennsylvania and staff physician at Corporal Michael J. Crescenz VA Medical Center, said in a press release. "Patients oftentimes don't bring up their wishes and goals unless they are prompted, and doctors may not have the time to do so in a busy clinic. Having an algorithm like this may make doctors in clinic stop and [ask themselves], 'Is this is the right time to talk about this patient's preferences?'" Previous studies have shown that machine learning algorithms, using electronic health record data, can accurately predict short-term mortality among patients in general medicine settings and, with oncology-specific algorithms, among those starting chemotherapy.