ABSTRACT
Using population-level data of 16.7M commercially insured individuals in the US, we examine 1) trends in telemedicine use, 2) variation in telemedicine use by clinical specialty, diagnosis, and patient characteristics, and 3) community factors driving telemedicine use. This analysis uses the machine learning procedure of targeted maximum likelihood estimation.
BIO
Sadiq Patel is a Postdoctoral Research Fellow at Harvard Medical School. He received his PhD and MS from the University of Chicago in Social Work and Biostatistics. Prior to his doctoral education, he was also a senior data scientist at Accenture.
