A team led by PC3I Innovation Faculty and Associate Director Ravi Parikh, MD, MPP, FACP, has found that machine learning-triggered behavioral nudges quadrupled the rates of conversations between patients and their clinicians about patients’ end-of-life care preferences, as well as decreased end-of-life chemotherapy by 25%.
While serious illness conversations (SICs) are an opportunity for patients to share their preferences and values and can lead to improved quality of life and reduce aggressive end-of-life care, most patients with advanced cancer die without a documented SIC.
Through a stepped-wedge randomized clinical trial, patients at risk for 6-month mortality were identified via a machine learning algorithm. These patients’ clinicians received text messages prompting SICs before their next encounter with the patient, in addition to weekly lists of all high-risk patients under their care. Conversation rates nearly quadrupled from 3.4% to 13.5%.
Further information on the implications of these findings can be found in a press release by Penn Medicine. The study, “Long-term Effect of Machine Learning-Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients with Cancer: A Randomized Clinical Trial,” was published in JAMA Oncology on January 12, 2023.
In an interview with the Philadelphia Inquirer, PC3I Associate Director Lola Fayanju commented on recently updated breast cancer screening recommendations.
As part of the RadComp study, Penn Medicine and over 20 radiation centers nationwide are comparing the effectiveness of photon therapy and proton therapy to treat breast cancer.
In partnership with the School of Social Policy and Practice, the Coalition for Food and Health Equity secured funding from the American Cancer Society for a community refrigerator at the Abramson Cancer Center.