Daniel Fabbri Archive — Page 1 of 1
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April 28, 2021
Recognition initiative shares patients’ praise of VUMC employees
A significant focus of Vanderbilt University Medical Center’s mission is “Personalizing the patient experience through our caring spirit and remarkable capabilities.” -
April 19, 2021
Extracting accurate COVID data from scanned forms
A switch to paper forms during the COVID-19 pandemic might have hobbled research, but using software with a novel user interface made it possible to accurately extract information from scanned forms. -
November 26, 2019
VUMC, Nashville Fire Department test real-time medical record system to improve handoff of incoming emergency patients
VUMC and the Nashville Fire Department are testing a system to automatically generate patient acuity scores and abbreviated care records of in-transit patient procedures based on computer interpretations of signals from sensors and video cameras. -
February 21, 2019
Skin diseases study uses crowdsourcing to gather data
In 1906, English statistician Francis Galton happened to visit a livestock fair where fairgoers were invited to guess the dressed weight of an ox scheduled for imminent slaughter. Some 800 attendees took part and afterwards Galton got hold of the contest data. -
October 19, 2017
Team seeks to build EMR system for battlefield scenarios
Daniel Fabbri, Ph.D., assistant professor of Biomedical Informatics and Computer Science, has been awarded a $1.7 million research grant from the U.S. Department of Defense to create an automated clinical documentation system for use in battlefield ambulances and helicopters. -
April 5, 2017
Risky business
Vanderbilt investigators have developed hospital readmission models that may help prevent payment penalties to hospitals when patients are readmitted too soon after discharge. -
June 2, 2016
‘Crowdsourcing’ project aims to refine data extraction from electronic health records
A research team at Vanderbilt University Medical Center (VUMC) will develop a crowdsourcing solution for generating a wide range of labeled data sets from electronic health records (EHRs).