Physician Data Scientist
(Internal Medicine + Computer Science)
Jonathan H. Chen (MD, PhD) leads the HealthRex research group that empowers individuals with the collective experience of the many, combining human and artificial intelligence approaches that will deliver better care than either can do alone. Dr. Chen continues to practice medicine for the concrete rewards of caring for real people and to inspire research focused on discovering and distributing the latent knowledge embedded in clinical data.
Dr. Chen’s research (featured in publications, presentations, and press) spans the areas of electronic health records, data-mining, recommender systems, collaborative filtering, observational research, medical decision making, machine learning, artificial intelligence, secondary analysis, and clinical decision support. He has published influential work in the New England Journal of Medicine, JAMA, JAMA Internal Medicine, Annals of Internal Medicine, Journal of the American Medical Informatics Association and more, with awards and recognition from the NIH Big Data 2 Knowledge initiative, National Library of Medicine, American Medical Informatics Association, Yearbook of Medical Informatics, and American College of Physicians, among others. His expertise has been featured in multiple popular press outlets, including The New York Times, NPR, STAT News, Kaiser Health News and more.
In the face of ever escalating complexity in medicine, informatics solutions are the only credible approach to systematically address challenges in healthcare. Tapping into real-world clinical data, such as electronic medical records, with machine learning and data analytics will reveal the community's latent knowledge in a reproducible form. By delivering this back to clinicians, patients, and healthcare systems as clinical decision support, he aims to uniquely close the loop on a continuously learning health system.
To gain perspective tackling societal problems in healthcare, he completed training in Internal Medicine and a Research Fellowship in Medical Informatics.