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.

Question Can machine learning algorithms identify oncology patients at risk of short-term mortality to inform timely conversations between patients and physicians regrading serious illness? Findings In this cohort study of 26 525 patients seen in oncology practices within a large academic health system, machine learning algorithms accurately identified patients at high risk of 6-month mortality with good discrimination and positive predictive value. When the gradient boosting algorithm was applied in real time, most patients who were classified as having high risk were deemed appropriate by oncology clinicians for a conversation regarding serious illness. Meaning In this study, machine learning algorithms accurately identified patients with cancer who were at risk of 6-month mortality, suggesting that these models could facilitate more timely conversations between patients and physicians regarding goals and values. Importance Machine learning algorithms could identify patients with cancer who are at risk of short-term mortality. However, it is unclear how different machine learning algorithms compare and whether they could prompt clinicians to have timely conversations about treatment and end-of-life preferences. Objectives To develop, validate, and compare machine learning algorithms that use structured electronic health record data before a clinic visit to predict mortality among patients with cancer. Design, Setting, and Participants Cohort study of 26 525 adult patients who had outpatient oncology or hematology/oncology encounters at a large academic cancer center and 10 affiliated community practices between February 1, 2016, and July 1, 2016.