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This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
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 study assessed the usability and impact of inpatient portals on patient experience, engagement, and perceptions of care.
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 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 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 developed and pilot tested a screening module and clinical decision support system for adolescents’ behavioral health.