Amit Kaushal, MD, PhD is Clinical Assistant Professor of Medicine (Stanford-VA) and Adjunct Professor of Bioengineering at Stanford University. Dr. Kaushal's work spans clinical medicine, teaching, research, and industry.
He helped launch Stanford School of Engineering's undergraduate major in Biomedical Computation (bmc.stanford.edu) and has served as long-time director of the major. The major has graduated over 70 students since inception and was recently featured in Nature (https://go.nature.com/2P2UnRu).
His research interests are in utilizing health data in novel and ethical ways to improve the practice of medicine. He is a faculty executive member of Stanford's Partnership for AI-Assisted Care (aicare.stanford.edu). Recently, he has also been working with public health agencies to improve scale and speed of contact tracing for COVID-19.
He has previously held executive and advisory roles at startups working at the interface of technology and healthcare.
He continues to practice as an academic hospitalist.
Dr. Kaushal completed his BS (Biomedical Computation), MD, PhD (Biomedical Informatics), and residency training at Stanford. He is board-certified in Internal Medicine and Clinical Informatics.
Adjunct Professor, Bioengineering
Executive Director, Biomedical Computation Major (2011 - Present)
Associate Director, Biomedical Computation Major (2003 - 2011)
Honors & Awards
Recipient, Paul and Daisy Soros Fellowship
Residency, Stanford University, Internal Medicine
PhD, Stanford University, Biomedical Informatics
MD, Stanford University
BS, Stanford University, Biomedical Computation
- Can Contact Tracing Work At COVID Scale? Health Affairs Blog. 2020
- Wiring Minds Successfully applying AI to biomedicine requires innovators trained in contrasting cultures NATURE 2019; 576 (7787): S62–S63
Beyond duty hours: leveraging large-scale paging data to monitor resident workload
NPJ DIGITAL MEDICINE
2019; 2: 87
Monitoring and managing resident workload is a cornerstone of policy in graduate medical education, and the duty hours metric is the backbone of current regulations. While the duty hours metric measures hours worked, it does not capture differences in intensity of work completed during those hours, which may independently contribute to fatigue and burnout. Few such metrics exist. Digital data streams generated during the usual course of hospital operations can serve as a novel source of insight into workload intensity by providing high-resolution, minute-by-minute data at the individual level; however, study and use of these data streams for workload monitoring has been limited to date. Paging data is one such data stream. In this work, we analyze over 500,000 pages-two full years of pages in an academic internal medicine residency program-to characterize paging patterns among housestaff. We demonstrate technical feasibility, validity, and utility of paging burden as a metric to provide insight into resident workload beyond duty hours alone, and illustrate a general framework for evaluation and incorporation of novel digital data streams into resident workload monitoring.
View details for DOI 10.1038/s41746-019-0165-2
View details for Web of Science ID 000484610000001
View details for PubMedID 31531394
View details for PubMedCentralID PMC6733865