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This research applied machine learning to develop a model predicting surgical cancellations among pediatric patients, and found the feasibility in using these algorithms as a cost-effective quality-improvement measure.
This project will enhance an existing Web-based portal, myADHDportal.com, to integrate behavioral tools alongside existing medication management tools for attention deficit hyperactivity disorder (ADHD).
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 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.
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.
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.