Machine learning identifies patients in need of end-of-life planning

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Penn Medicine researchers have developed a machine learning algorithm that identifies oncology patients at risk of short-term mortality who need end-of-life conversations with clinicians.

In a study of 26,525 patients receiving outpatient oncology care, the algorithm accurately predicted patients with cancer who were at risk of six-month mortality using electronic health records, including whether a patient had high blood pressure as well as laboratory and electrocardiogram data.

The study found that 51 percent of the patients the algorithm identified as “high priority” for end-of-life conversations died within six months vs. fewer than 4 percent in the “lower priority” group.

“Our findings suggest that ML tools hold promise for integration into clinical workflows to ensure that patients with cancer have timely conversations about their goals and values,” concludes the study, which was published in the journal JAMA Network Open.

Initially, researchers developed, validated and compared three ML models—gradient boosting, logistic regression and random forest—to estimate six-month mortality among patients seen in oncology clinics affiliated with a large academic cancer center. However, the random forest model in the study demonstrated the best predictive results.

“We’re excited about the scalability of this decision support method for providers, and not just in oncology,” says Ravi Parikh, MD, lead author and an instructor of medical ethics and health policy at the University of Pennsylvania. “Our process of using machine learning to flag high-risk patients in real time is broadly applicable, and our approach risk-stratifies patients in a usable way that just hasn’t been available to us before.”

According to Parikh, the algorithm is being implemented at a medical center—not part of the original pilot for the study—where the research team will test whether identifying the best patients for conversations actually does prompt the doctors to initiate those discussions. Ultimately, researchers plan to conduct a three- to six-month randomized controlled trial involving about 100 clinicians.

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