This project will implement and evaluate a previously developed, interactive, patient-centered discharge toolkit to improve the transition of care from the inpatient to outpatient settings.
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 will develop a natural language processing search tool that will automatically identify and rank relevant clinical information in an electronic health record based on a patient’s presenting complaint.
This project will study health information technology adaptation using sociotechnical theory.
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 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 project supported a 2-day conference called the Ethics in Investigational and Interventional Uses of Immersive VR (e3iVR), hosted by the Living Environments Laboratory at the University of Wisconsin-Madison.
This project will develop a mobile health application to improve screening, intervention, and referrals in the care of pregnant women.
This project will optimize and implement a vendor-agnostic eLearning program that supports older adults in using patient portals for their care.
This project will use machine learning to enhance an existing sepsis clinical decision support tool to improve the early detection of sepsis.