Search found 11 items
This research will further disseminate a currently used clinical decision support tool to identify patients at risk for a life threatening, uncommon cardiac arrhythmia.
This project proposes a novel proactive system to reduce alert burden and thereby increase attention to situations in which patient safety is at risk.
This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
This project will apply machine learning against a large data set to develop a model to both understand and predict surgical cancellations on individual pediatric patients at two pediatric surgical sites.
This study assessed the usability and impact of inpatient portals on patient experience, engagement, and perceptions of care.
This project developed patient-tailored relevant warnings about drug-drug interactions and found that it reduced irrelevant alerts.
This project will integrate clinical decision support into providers’ workflow in neonatal intensive care units to deliver evidence-based guidelines for early recognition and prevention of necrotizing enterocolitis, a serious complication threatening the life of fragile premature infants.
This project sought to reduce the use of emergency department services for non-urgent care by improving access to primary care physicians for Medicaid patients via the electronic medical record.
This project used a mixed-method approach to investigate the validity of using electronic health record data for diabetes performance measures.
This project evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.