electronic health records (EHRs)

People who study health care delivery tend to be interested in the ins and outs of patient information — how it’s gathered, how it’s used. One important focus is provider-to-provider communication associated with patient transitions between care settings.

With research and development costs for many drugs reaching well into the billions, pharmaceutical companies want more than ever to determine whether their drugs already at market have any hidden therapeutic benefits that could warrant putting additional indications on the label and increasing production.

In recent years the federal government has used financial incentives and penalties to promote use of electronic health records (EHR), but in these regulations the issue of EHR data interoperability — that is, data standardization — has been left largely unresolved, dimming prospects for data exchange, population health surveillance and systematic improvement across health networks.

Vanderbilt University Medical Center is rolling out a new electronic tool to facilitate the documentation of every patient’s home medications. Called the Med List Tool, this new part of the electronic medical record is a way for providers of all disciplines to accurately collect, record and communicate the medications a patient takes.

Electronic medical records linked to DNA biobanks are a valid resource for defining and understanding the genetic factors that contribute to drug response.

Vanderbilt University Medical Center researchers and co-authors from four other U.S. institutions from the Electronic Medical Records and Genomics (eMERGE) Network are repurposing genetic data and electronic medical records to perform the first large-scale phenome-wide association study (PheWAS), released today in Nature Biotechnology.

Tucked in a data center in the basement of Vanderbilt University Hospital, a new computer the size of a large armoire, called a data warehouse appliance, is delivering a new order of speed to Vanderbilt clinical scientists as they search, filter, analyze and annotate the de-identified medical records of approximately 2 million patients.