electronic health records (EHRs)

In a new study across three major U.S. health care systems, researchers developed and tested algorithms to predict who will develop bipolar disorder.

The STAR Clinical Research Network, based at Vanderbilt University Medical Center (VUMC), has added two new partners — Essentia Health, a Minnesota-based rural health care system, and Stanford University School of Medicine, one of the country’s leading academic medical centers.

An automated solution for creating phenotyping algorithms, PheNorm, worked well to identify symptomatic COVID-19 cases in electronic health records, suggesting that automation could speed high-throughput phenotyping of acute disease.

A Vanderbilt study found that automated suicide risk prediction models operating on electronic health records could help clinical teams efficiently identify patients for face-to-face suicide risk screening and prevention.

Vanderbilt's Bryan Shepherd, PhDhas received a MERIT Award, or Method to Extend Research in Time Award, from the National Institute of Allergy and Infectious Diseases.

A review of electronic health record user access logs found that EHRs of adult inpatients from minority racial and ethnic populations on average received lower engagement from health care teams than the records of white adult inpatients.

Vanderbilt researchers have developed a computer algorithm that scans electronic health records, or EHRs, to identify patients who meet criteria for lung cancer screening.