Connected Health Approaches to Improve Serious Illness Communication Among Patients with Incurable Cancer
Many patients with incurable cancer undergo treatment and acute care utilization that is discordant with their preferences, particularly near the end of life. Early conversations about prognosis and treatment preferences can improve prognostic awareness between cancer patients and their oncologists, increase goal-concordant care, and decrease utilization near the end of life. We have developed and piloted “Conversation Connect”, a machine learning model based on structured electronic health record data to identify patients who may benefit from a timely Serious Illness Conversation (SIC) with their clinician. In this project, we will explore how trends in out-of-clinic patient-reported symptoms and step counts can prompt serious illness conversations.
Providing clinicians with patient-reported symptoms and remotely-monitored functional status will help clinicians better identify palliative care needs and increase rates of documented SICs.
Institute for Translational Medicines and Therapeutics (ITMAT), Abramson Cancer Center, and the Penn Center for Precision Medicine.
Center for Health Incentives and Behavioral Economics (CHIBE) at the University of Pennsylvania
Augmented & Artificial Intelligence
Incentives to Change Behavior