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This research will enhance and test a tool which uses natural language processing to provide a real-time assessment of dialog during motivational interviewing training.
This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
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 study assessed the usability and impact of inpatient portals on patient experience, engagement, and perceptions of care.
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 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 sought to reduce the use of emergency department services for non-urgent care by improving access to primary care physicians for Medicaid patients via the electronic medical record.
This project used a mixed-method approach to investigate the validity of using electronic health record data for diabetes performance measures.
This project evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.