HAI Monthly Community Building Reception - Compassionate intelligence: Can machine learning bring more humanity to health care?

We will describe the Stanford Medicine Program for AI in Healthcare, which aims to bring AI into clinical use, safely and ethically. The session will begin with an overview of the effort and then focus on describing a project to improve palliative care using machine learning. We will summarize the creation and validation of a mortality prediction model, describe the associated care planning workflow it triggers and the work constraints it needs to function under. We will present  preliminary results on an HAI supported project for understanding and addressing ethical challenges with implementation of machine learning to advance palliative care. Using this real-life example, we will elucidate several of the ethical challenges that need to be studied and addressed when combining artificial intelligence technologies with medical expertise to help doctors make faster, more informed and humane decisions. 

Speakers

Nigam Shah, Associate Professor of Medicine (Biomedical Informatics), Stanford University; Assistant Director, Center for Biomedical Informatics Research

Dr. Shah's research focuses on combining machine learning and prior knowledge in medical ontologies to enable use cases of the learning health system. Shah received the AMIA New Investigator Award for 2013 and the Stanford Biosciences Faculty Teaching Award for outstanding teaching in his graduate class on “Data driven medicine”. Dr. Shah was elected into the American College of Medical Informatics (ACMI) in 2015 and is inducted into the American Society for Clinical Investigation (ASCI) in 2016. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University and completed postdoctoral training at Stanford University.

Danton Char, Assistant Professor Med Center Line, Anesthesiology, Perioperative and Pain Medicine, Stanford University

Dr. Char's K01 from NHGRI examines the ethical challenges of implementing whole genome sequencing in the care of critically ill children, particularly those with congenital cardiac disease. His long-term goal is to continue to identify and address ethical concerns associated with the implementation of next generation technologies to bedside clinical care, like whole genome sequencing and its attendant technologies like machine learning.

Ron Li, Clinical Assistant Professor, Medicine, Stanford University

Ron grew up in New York, went to college and medical school in Chicago at Northwestern, and completed a one year clinical epidemiology research fellowship at Penn before coming to Stanford with his wife for Internal Medicine training. Ron's informatics interests are to work with other clinicians, informaticists, and designers to create, implement, evaluate, and disseminate tools that both help us become better physicians and build a learning healthcare system of the future.

Stephanie Harman, Clinical Associate Professor of Medicine, Stanford University

Dr. Harman graduated from Case Western Reserve University School of Medicine. She then completed a residency in Internal Medicine at Stanford and a Palliative Care fellowship at the Palo Alto VA/Stanford program before joining the faculty at Stanford. She is the founding medical director of Palliative Care Services for Stanford Health Care and a 2017 Cambia Sojourns Scholar Leader Awardee. She is a Clinical Associate Professor in the Department of Medicine and a faculty member in the Stanford Center for Biomedical Ethics. She serves as the clinical section chief of Palliative Care in the Division of Primary Care and Population Health and co-chairs the Stanford Health Care Ethics Committee. Her research and educational interests include communication training in healthcare, bioethics in end-of-life care, and the application of machine learning to improve access to palliative care.