Algorithm identifies advance care planning needs

Many cancer patients do not get the opportunity to discuss their wishes for treatment until their illness has progressed too far, when it may be too late to have those discussions with loved ones or physicians. Now, a Penn Medicine-led team has developed an algorithm that flags patients who would benefit most from a timely conversation about their end-of-life goals and wishes, in an effort to begin that dialogue sooner.

Cancer patient in wheelchair being pushed by a caregiver who consults with doctor holding a medical chart.

In a new study—published in JAMA Network Open and simultaneously presented at the American Society of Clinical Oncology Supportive Care in Oncology Symposium in San Francisco—researchers found that 51 percent of the patients the algorithm flagged as “high priority” for these conversations subsequently died within six months of their evaluation, compared to less than four percent in the “lower priority” group. These findings suggest the algorithm accurately captures those patients who would benefit most from timely discussions about their goals, values, and preferences for care. 

This study is believed to be one of, if not the first, to look into the application of a machine learning algorithm for oncology patients.

“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,” says the study’s lead author Ravi Parikh, an instructor of medical ethics and health policy at the Perelman School of Medicine and a staff physician at the Corporal Michael J. Crescenz VA Medical Center. “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 think, ‘Is this the right time to talk about this patient’s preferences?’”

Read more at Penn Medicine News.