Nudges with machine learning triples advanced care conversations among cancer patients

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Nudges with machine learning triples advanced care conversations among cancer patients

Press releases may be edited for formatting or style | October 16, 2020 Artificial Intelligence
PHILADELPHIA--An electronic nudge to clinicians--triggered by an algorithm that used machine learning methods to flag patients with cancer who would most benefit from a conversation around end-of-life goals--tripled the rate of those discussions, according to a new prospective, randomized study of nearly 15,000 patients from Penn Medicine and published today in JAMA Oncology.

Early and frequent conversations with patients suffering from serious illness, particularly cancer, have been shown to increase satisfaction, quality of life, and care that's consistent with their values and goals. However, today many do not get the opportunity to have those discussions with a physician or loved ones because their disease has progressed too far and they're too ill.

"Within and outside of cancer, this is one of the first real-time applications of a machine learning algorithm paired with a prompt to actually help influence clinicians to initiate these discussions in a timely manner, before something unfortunate may happen," said co-lead author Ravi B. Parikh, MD, an assistant professor of Medical Ethics and Health Policy and 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. "And it's not just high-risk patients. It nearly doubled the number of conversations for patients who weren't flagged--which tells us it's eliciting a positive cultural change across the clinics to have more of these talks."

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Christopher Manz, MD, of the Dana Farber Cancer Institute, who was a fellow in the Penn Center for Cancer Care Innovation at the time of the study, serves as co-lead author.

In a separate JAMA Oncology published in September, the research team validated the Penn Medicine-developed machine learning tool's effectiveness at predicting short-term mortality in patients in real-time using clinical data from the electronic health record (EHR). The algorithm considers more than 500 variables--age, hospitalizations, and co-morbidities, for example--from patient records, all the way up until their appointment. That's one of the advantages of using the EHR to identify patients who may benefit from a timely conversation. It's in real time, as opposed to using claims or other types of historical data to make predictions.

This latest trial combined that algorithm with a behavioral nudge, including texts, emails, or notifications to the clinical team, to determine its ability to both identify patients and prompt conversations around end-of-life planning. The study--which included 14,607 patients and 78 physicians across nine oncology clinics in the University of Pennsylvania Health System--was conducted between June 2019 and November 2019.

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