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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 project tested a pediatric voice therapy telehealth system and found that it was feasible to implement and well accepted by children and their families.
This project evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.
Expands upon an electronic medical records-sharing initiative for high-risk infants and their families in Mississippi, linking new health centers and clinics and serving a rural area that spans 17 counties; uses telemedicine technologies to enhance evidence-based developmental care for newborns in acute care hospitals; and creates Web-based decision support resources for physicians who care for infants.
Developed, implemented, and evaluated a cooperative effort for using health IT to facilitate a continuum of appropriate medical and developmental care, from the time infants are admitted to Neonatal Intensive Care Units (NICUs), through the transition process to community-based health care services for infants most at-risk for long-term neurodevelopmental problems.