Tech & Health Archive — Page 15 of 20

Janet Shouse and Beth Malow, MD, MS, are among a team of Vanderbilt Kennedy Center researchers seeking to improve access to care for adults with autism. (photo by Steve Green)
October 29, 2020

Telementoring project aims to improve access for adults with autism

A team at Vanderbilt University Medical Center and Vanderbilt Kennedy Center is launching a program to improve access to primary care for adults with autism.

October 29, 2020

New tool rapidly identifies health records for studies

Electronic health records (EHR) are increasingly a resource for biomedical discovery, and automated searches for records that reflect a phenotype of interest, typically a disease, are a common starting point.

September 21, 2020

Throwing weight around on the internet

What users mention in online weight loss forum tracks with how much weight they lose.

William “Bill” Stead, MD, is stepping down from his role as Vanderbilt University Medical Center’s Chief Strategy Officer.
September 17, 2020

Stead to step down from Chief Strategy Officer role after decades of remarkable contributions

Visionary — someone who thinks about the future or advancements in a creative and imaginative way, a person who is ahead of her or his time and who has a powerful plan for change in the future. Such a person is William “Bill” Stead, MD, Vanderbilt University Medical Center’s Chief Strategy Officer, McKesson Foundation Professor of Biomedical Informatics and Professor of Medicine.

September 17, 2020

Center for Genetic Privacy lands major grant renewal

The National Human Genome Research Institute (NHGRI) has awarded a four-year, $4 million grant renewal to Vanderbilt University Medical Center’s Center for Genetic Privacy and Identity in Community Settings (GetPreCiSe).

August 27, 2020

Study uses AI to sort patient messages by complexity

Taking an interest in electronic message threads between surgical patients and their health care teams, a research group at Vanderbilt University Medical Center has tested how well certain commonly used machine learning algorithms can classify such exchanges according to their clinical decision-making complexity.