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 developed and tested a tablet-based decision aid to assist primary care providers in applying patient-reported outcomes to smoking cessation and found that the tool facilitated more conversations about smoking cessation between patients and providers.
This research explored the effectiveness of integrating behavioral tools into an evidence-based software to improve access to behavioral treatment strategies for children with attention deficit hyperactivity disorder.
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 research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
