This research will assess the validity of patient-level computable social factor phenotypes used to predict a patient’s risk of increased healthcare utilization and costs.
An Electronic Health Record-Based Screening Tool to Support Safe Discharges of COVID-19 Patients in the Emergency Department
This research will develop and validate a COVID-19 emergency department (ED) return screening tool that will provide ED clinicians a risk assessment to guide admissions and discharges to reduce morbidity and mortality associated with acute respiratory syndrome coronavirus 2 infection.
This research will compare the use of predictive modeling versus traditional questionnaires to identify those with unmet social needs, use the superior method to inform the development of a clinical decision support tool, and evaluate the tool’s impact on referrals to social providers.
Transforming Kidney Care in the Emergency Department Using Artificial Intelligence-Driven Clinical Decision Support
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 is a questionnaire designed to be completed by caregivers in an urgent care, emergency department, or ambulatory care setting. The tool includes questions to assess attitudes of telemedicine.
Improving Healthcare Processes and Outcomes by Directly Integrating Health Information Exchange Data in the Clinical Workflow
The research team will implement and evaluate an integration application that incorporates relevant health information exchange data directly into the electronic health record in the emergency department.
Telehealth and Emergency Medicine: A Consensus Conference to Map the Intersection of Emergency Medicine and Telehealth
The 2020 Society for Academic Emergency Medicine Consensus Conference, “Telehealth and Emergency Medicine: A Consensus Conference to Map the Intersection of Emergency Medicine and Telehealth” developed a research agenda to support future clinical practice and evidence-based investigation at the intersection of telehealth and emergency medicine.
This research will further scale clinical decision support aimed at preventing the prescription of inappropriate medications to older adults upon discharge from the emergency department.
This research will examine the evidence around the value of order sets, while uncovering clinician perceptions that hinder their efficient use.
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.