Among patients with a high-predicted mortality risk, conversations in the intervention group occurred in 304 out of 1,999 patient encounters (15.2 percent) compared to 77 out of 2,125 in the control group (3.6 percent). Even when patients were not flagged as high-risk, clinicians in the trial engaged more in these conversations. Among all patient encounters, serious illness conversations occurred in 155 out of 12,170 encounters (1.3 percent) in the control group, while conversations in the intervention group occurred in 632 out of 13,889 encounters (4.6 percent).
"We've taken an algorithm from retrospective validation to real-time validation to actually testing it in the clinic to see if it can shape patient care," said Parikh, who is also part of the Penn Center for Cancer Care Innovation. "Because of its success, I think we've provided of a road map for other institutions that may be thinking of using analytics to drive important behaviors."
The machine learning tool continues to be utilized in Penn Medicine oncology clinics, and further proved its value during the COVID-19 pandemic. The rates of serious illness conversations continued to remain high after the trial ended, despite many of those conversations taking place online through much of 2020, when many clinical visits had to occur through telemedicine to ensure patient safety.
"This is one of the first applications of combing behavioral nudges with machine learning methods in clinical care," said senior author Mitesh S. Patel, MD, director of the Penn Medicine Nudge Unit and an associate professor of Medicine in the Perelman School of Medicine at the University of Pennsylvania and a staff physician at the Corporal Michael J. Crescenz VA Medical Center. "There are many opportunities build upon this work and apply it to other aspects of cancer care and to other areas of medicine."
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