AI aids efforts to cut nuisance alerts for health care teams: study

A new study from Vanderbilt University Medical Center demonstrates the promise of artificial intelligence to help refine and target the myriad computerized alerts intended to assist doctors and other team members in day-to-day clinical decision-making.

Team uses COVID-19 to test automated acute disease profiling

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.

ChatGPT tested for clinical decision support

The problem with the problem list

Algorithms to infer missing problems and suggest that they be added to electronic health records improved problem list completeness, with benefits for clinical care, patient comprehension of health conditions and population health.

Machine learning predicts delirium

Using machine learning based on electronic health records of ICU patients predicted new-onset delirium with 82% sensitivity, Vanderbilt researchers found.

PheWAS reveals post-COVID-19 diagnoses

Using a high-throughput informatics technique and electronic health records, Vanderbilt researchers found that COVID-19 survivors had an increased risk for more than 40 new diagnoses.