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This project will develop and evaluate an electronic clinical decision support tool for care of patients with Acute Respiratory Distress Syndrome.
This project will use Learning Health System methods to systematically apply U.S. Preventive Services Task Force’s evidence-based recommendations with the goal of advancing individualized precision prevention.
This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
This project developed a natural language processing electronic health record search tool that automatically identifies and ranks relevant clinical information based on a patient’s presenting complaint within the emergency department setting.
This project will study the impact of errors in medical documents on quality of care and develop innovative natural language processing methods to automatically detect errors so that physicians can correct the documents before finalizing them in the electronic health record.
This project developed and pilot-tested a novel, outcomes-based emergency department triage tool and found that risk stratification and waiting times were improved for some patients.
This research evaluated the implementation and effectiveness of a clinical decision support tool designed to support the delivery of recommended care to hospitalized patients with heart failure, regardless of the reason for hospitalization.