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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 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.
This research assessed the use of a health information exchange system in emergency department settings, finding that although overall usage is relatively low, additional functionalities such as single sign on add value to clinical decision making and enable faster retrieval of patient records from external sources compared to traditional methods when embedded into existing workflows.
Evaluated the completeness and accuracy of information on symptoms, disease conditions, medications, and allergies generated by parents using a patient-centered health technology called ParentLink, compared to information documented by emergency department physicians and nurses; and assessed ParentLink's impact on patient safety and quality.