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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 project assessed the clinical and operational implications of electronic health record downtimes and developed a simulation model to support the creation of effective downtime contingency plans.
This study assessed the usability and impact of inpatient portals on patient experience, engagement, and perceptions of care.
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 evaluated the feasibility of primary care physicians to integrate an interactive preventive healthcare record (IPHR), which was created during a previous study, into their practices to deliver preventive services.