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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 developed and implemented a large-scale approach to measuring the impact of health information technology on the quality and variability of care in ambulatory settings, and along racial and ethnic lines.
This project designed and pilot tested a dashboard that synthesizes patient data from a registry and found that it decreased the average monthly visit no-show rate.
This project evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.