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This research will study how a safety-net hospital responds to a pandemic, specifically COVID-19, to identify how information needs are met and how decisions are made and communicated to other individuals internal and external to the institution.
This research will develop and evaluate an artificial intelligence-driven clinical decision support system to detect and manage acute kidney injury in the emergency department.
This research aims to develop and evaluate a clinical decision support strategy to promote influenza vaccination among children who are hospitalized with the goal to identify insights that broadly apply to clinical decision support for health maintenance interventions in pediatric acute care settings.
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 research will demonstrate the use of standards, including SMART on FHIR, combined with service-oriented architecture to bring vendor-agnostic clinical decision support (CDS) tools into commercial electronic health records, and provide evidence for how to implement validated CDS for important clinical domains, pulmonary, and venous thromboembolism, including for patients with COVID-19.