Machine learning-triggered reminders improve end-of-life care for patients with cancer

The rates of advanced care planning conversations quadrupled, while potentially harmful therapy at end of life decreased by 25% in large randomized study.

Electronic nudges delivered to health care clinicians based on a machine learning algorithm that predicts mortality risk quadrupled rates of conversations with patients about their end-of-life care preferences, according to the long-term results of a randomized clinical trial published by Penn Medicine investigators in JAMA Oncology. The study also finds that the machine learning-triggered reminders significantly decreased use of aggressive chemotherapy and other systemic therapies at end of life, which research shows is associated with poor quality of life and side effects that can lead to unnecessary hospitalizations in their final days.

Doctor at desk in discussion with patient.

For patients when cancer advances to an incurable stage, some may prioritize treatment that will extend their life as long as possible, and others may prefer a care plan that’s designed to minimize pain or nausea, depending on the outlook for their disease. Talking to patients about their prognosis and values can help clinicians develop care plans that are better aligned to each individual’s goals, but it’s essential that the discussions happen before patients become too ill.

“This study demonstrates that we can use informatics to improve care at end of life,” says senior author Ravi B. Parikh, an oncologist and assistant professor of medical ethics and health policy and medicine in the Perelman School of Medicine and associate director of the Penn Center for Cancer Care Innovation at Abramson Cancer Center. “Communicating with cancer patients about their goals and wishes is a key part of care and can reduce unnecessary or unwanted treatment at the end of life. The problem is that we don’t do it enough, and it can be hard to identify when it’s time to have that conversation with a given patient.”

The findings show that after a 24-week follow-up period, conversation rates nearly quadrupled, from 3.4% to 13.5%, among high-risk patients. The use of chemotherapy or targeted therapy in the final two weeks of life decreased from 10.4% to 7.5% among patients who died during the study. The intervention did not have an impact on other end-of-life metrics, including hospice enrollment or length of stay, inpatient death, or end-of-life intensive care unit use.

This story is by Meagan Raeke. Read more at Penn Medicine News.