This research will develop, implement, refine, and evaluate an app to support clinical decisions for ST-Elevation Myocardial Infarction care in rural areas by emergency medical services providers, reducing the time between first medical contact and reperfusion therapy to reduce morbidity and mortality, and improve health outcomes.
This research will use deep learning models to move a reactive full capacity protocol (FCP) for emergency department (ED) overcrowding interventions into a proactive FCP by predicting patient flow measures so that interventions may be activated to avoid ED overcrowding.
This research will implement, evaluate, and disseminate DDInteract, a shared decision making clinical decision support tool that aims to reduce harm from oral anticoagulant drug-drug interactions.
This research will design, implement, and evaluate an emergency department triage machine learning algorithm, with an emphasis on predicted patient acuity and complexity, and incorporate it into a clinician-facing clinical decision support tool to promote safer, higher quality, and more equitable care.
This research will develop, validate, and evaluate a decision support tool using a machine learning algorithm to standardize the approach to discontinuing disease-modifying therapies for multiple sclerosis.
