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This project will examine health information exchange (HIE) usage patterns, the barriers and facilitators of HIE use, and the impact of HIE use by emergency department clinicians.
The goal of this project is to design and test an information technology called Power to the People to support self-care management among older patients with chronic heart failure.
This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
This project will formulate evidence-based recommendations for clinical decision support used by community pharmacist delivering medication therapy management. The goal is to reduce medication-related problems and improve health outcomes for chronically ill patients.
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 integrated a validated anxiety-specific screening tool in an existing clinical decision support system and tested it with a randomized feasibility pilot that found the tool did not increase detection of anxiety in pediatric primary care.
This project tested a pediatric voice therapy telehealth system and found that it was feasible to implement and well accepted by children and their families.
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.