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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 potential severity of specific look-alike, sound-alike drug name substitution errors in a pediatric population and estimated the frequencies of potential look-alike, sound-alike (LASA) substitution errors.
This project evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.