Search found 7 items
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 assessed provider mental workload and performance while processing electronic abnormal test results and found that a test result tracking mechanism improved physicians’ clinical performance.
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 designed and developed a clinical decision support tool called VisualDecisionLinc (VDL) to aid in the initial treatment strategies for major depressive disorder.
For this project, software engineering practices were developed to guide others in the development of trustworthy electronic health record applications; developed techniques to evaluate the trustworthiness of these applications; and used these techniques to evaluate six products.