Building AI-Enabled Systems for Patient Care at Stanford

September 28th, 2020

Artificial Intelligence (AI) has generated a large amount of excitement in healthcare, mostly driven by the emergence of increasingly accurate machine learning models. However, the promise of AI delivering scalable and sustained value for patient care in the real-world setting has yet to be realized. To bring AI safely and effectively into use in healthcare, we need to rethink how to approach the creation and application of AI and its delivery. This AI development and delivery science will require a broader set of tools, such as quality improvement, user-centered design, and implementation science, as well as a broader definition of what AI will look like in practice, which includes not just machine learning models and their predictions, but also the new systems for care delivery that they enable.

This talk will discuss the fundamental concepts of AI development and delivery science, how they are applied to real-world projects in a health system, and paths towards future growth and scale of AI in healthcare.

Ron Li, M.D.

Ron Li is a Clinical Assistant Professor of Medicine in the Division of Hospital Medicine and Center for Biomedical Informatics Research at Stanford University School of Medicine. He is also the Medical Informatics Director for Artificial Intelligence Clinical Integration at Stanford Health Care. Ron’s work is centered around the design, implementation, and evaluation of novel systems of care delivery that can be enabled by artificial intelligence. His work spans across multiple disciplines, including clinical medicine, data science, digital health, information technology, design thinking, process improvement, and implementation science. Current areas of focus include using machine learning to improve advance care planning, care of clinically deteriorating patients, and e-consults for the health system. He has consulted for various companies in the digital health and artificial intelligence space. He is a practicing hospitalist and attends on the inpatient medicine teaching service at Stanford Hospital.

He received his MD from Northwestern University Feinberg School of Medicine and completed his internal medicine residency and clinical informatics fellowship at Stanford University School of Medicine.

Margaret Smith, MBA

Margaret Smith is the Director of Operations of the Stanford Healthcare AI Applied Research Team (HEA3RT) where she works with industry partners, and clinical and operational leaders at Stanford on the application, development, and implementation of artificial intelligence technologies. Her expertise lies in healthcare quality improvement, complex problem solving, facilitating cross-discipline collaboration, and design thinking. Her passion is building and applying collaborative mix-method approaches developing, integrating, and studying technology solutions in healthcare that works for providers and patients rather than impede care delivery.

Previously, she held senior positions in quality improvement for many years in academic and non-academic medicine garnering extensive experience in a broad set of organizational and incentive structures.

Margaret holds a bachelor’s degree in finance and risk management, a master’s in business administration with a specialization in healthcare management from the Baylor Hankamer School of Business, Robbins Institute for Health Policy and Leadership, and a Lean Six-Sigma master black belt certification.