Search found 16 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 research linked a web viewer for 3D models of patient homes to an electronic health record so that the models could be viewable and annotated from within a patient’s record for better discharge planning.
This study assessed the usability and impact of inpatient portals on patient experience, engagement, and perceptions of care.
This project evaluated the use of an in-room interactive monitor to improve patient-centered care and family engagement within a pediatric intensive care unit.
The purpose of this project is to evaluate the cognitive and team work involved in venous thromboembolism prevention and management and to develop design requirements for a clinical decision support tool that supports this collaborative work.
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 focused on developing and implementing CDS tools to support nurses in the development of care plans and involvement in quality improvement activities in the area of fall prevention in acute care.
Drs. Pascale Carayon and Ben-Tzion Karsh led a team that studied the existing research related to the impacts of health IT on workflow in outpatient settings and how health IT can be used to assess workflow in these settings. The information led to the development of a toolkit to help small and medium-sized medical practices assess their workflows before implementing a health IT system.