Amol Navathe Amol Navathe

Amol Navathe, MD, PhD

Assistant Professor, Center for Health Incentives and Behavioral Economics at the Leonard Davis Institute

Amol Navathe, MD, PhD, is an Assistant Professor of Health Policy and Medicine and a Senior Fellow at the Leonard Davis Institute for Health Economics at the University of Pennsylvania. He is a Commissioner of the Medicare Payment Advisory Commission (MedPAC), a non-partisan agency that advises the U.S. Congress on Medicare policy. He also directs the Payment Insights Team.

As a practicing physician, health economist, and engineer, Dr. Navathe has extensive expertise in policy analysis and design, physician and hospital economic behavior, and application of informatics and predictive analytics to health care. His work on advanced health data analytics and technology to improve healthcare delivery has been implemented at numerous large insurers and health systems. He has applied his skills to payment and delivery transformation, including applications of behavioral economics to clinician decision making, and federal policy for health care evidence development and data infrastructure. Dr. Navathe completed his medical training at the University of Pennsylvania School of Medicine and his post-graduate medical training at the Brigham and Women’s Hospital at Harvard Medical School. He obtained his PhD in Health Care Management and Economics from The Wharton School at the University of Pennsylvania.

Dr. Navathe is a leading scholar on payment model design and evaluation, particularly bundled payments. His scholarship is unique because of its bi-directional translation between basic scientific discovery and real-world practice, including focus on: (1) the impact of value based care and payment models, such as cardiac bundles, on health care value; (2) financial and non-financial incentive design, including applications of behavioral economics, to drive clinician practice change; and (3) a mix of pragmatic clinical trials and observational data analyses. Current projects include design and evaluation of primary care payment and specialty bundles models, optimal use of performance feedback (e.g., to decrease opioid prescribing), and applying predictive models to drive behavior change.