An automated system using keyword searches can help identify candidates for clinical trials on adverse drug reactions.

For patients receiving total knee or total hip replacements at the Nashville VA Medical Center, the use of opioids for inpatient pain management and the total time patients spent in the hospital were both greatly reduced following the January 2016 adoption of sweeping quality improvement measures.

This spring in the U.S., there were widespread delays in elective health care procedures and screenings. Hospitals, in observance of federal guidelines, were, for a time, conserving beds and protective equipment in preparation for a surge in COVID-19 admissions. And, perhaps on a more prolonged basis, patients in many areas of the country stayed away due to anxiety over catching COVID-19 from other patients or their health care team.

Vanderbilt investigators have devised a system to alert health IT teams to deteriorating performance in clinical prediction models.

Six years ago, in Nigeria, funding cutbacks for HIV treatment and the consequent introduction of patient fees were associated with sharp declines in new HIV clinic enrollments and patient adherence to antiretroviral therapy (ART).

False positives on urine drug screens are common and are frequently due to cross-reactivity of these tests to medications. Last year, Vanderbilt University Medical Center researchers Jacob Hughey, PhD, assistant professor of Biomedical Informatics, and Jennifer Colby, PhD, at that time assistant professor of Pathology, Microbiology and Immunology, devised, tested and published a method to systematically identify medications that interfere with screenings for drugs of abuse.