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This research will further disseminate a currently used clinical decision support tool to identify patients at risk for a life threatening, uncommon cardiac arrhythmia.
This project proposes a novel proactive system to reduce alert burden and thereby increase attention to situations in which patient safety is at risk.
This project will design natural language processing algorithms to extract data from free text notes on autism spectrum disorders in electronic health records, and demonstrate the feasibility and usefulness of this approach.
This project developed patient-tailored relevant warnings about drug-drug interactions and found that it reduced irrelevant alerts.
This project will integrate clinical decision support into providers’ workflow in neonatal intensive care units to deliver evidence-based guidelines for early recognition and prevention of necrotizing enterocolitis, a serious complication threatening the life of fragile premature infants.