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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 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.
Developed approaches to share data on patient clinical and diagnostic information across systems and created an implementation plan for systems integration.