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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 integrated a validated anxiety-specific screening tool in an existing clinical decision support system and tested it with a randomized feasibility pilot that found the tool did not increase detection of anxiety in pediatric primary care.
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 expanded and modified the Child Health Improvement through Computer Automation (CHICA) system to assist pediatricians in identifying and managing four common medical-legal problems that may adversely impact child health, and found initial findings to be inconclusive.
The project sought to determine if a computer decision support system integrated with routine care could improve standardized developmental screening during early well-child visits and surveillance for developmental disabilities at all pediatric visits.
This project evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.