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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 took an existing sepsis-related clinical decision support (CDS) and developed, tested, implemented, and validated a knowledge-based artificial intelligence-enhanced sepsis CDS.
This study assessed the usability and impact of inpatient portals on patient experience, engagement, and perceptions of care.
This project developed a tool to promote activation, communication, engagement, and self-management of pediatric blood and marrow transplant patients and their parents and found that patient-centric tools can successfully engage caregivers in hospital care.
Created an information management environment that integrated patient care data, standardized practice variation and use of best practices, and supported the delivery of a seamless continuum of patient care throughout the health system through CPOE.