Emergency Department

This research will develop a SMART on FHIR version of an existing pneumonia clinical decision support tool and evaluate it in a new setting with a different electronic health record.

This research is developing and testing VR-Choice, an immersive virtual reality experience designed to increase patient willingness to engage in shared decision-making for medications for opioid use disorder after being treated for an opioid-related overdose in the emergency department.

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 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 use a consensus conference format during the 2023 Society for Academic Emergency Medicine conference to develop and publish an actionable research agenda for precision emergency medicine with a focus on improving patient outcomes by using a patient’s genomic, biologic, environmental, and public health data.

This research will evaluate the safety, usability, diagnostic, and triage accuracy of a popular symptom checker app and new diagnostic algorithms, and their ability to affect patients’ decisions to seek care, particularly for transient ischemic attacks and stroke.

This research will enhance an augmented reality headset used to provide real-time feedback on pediatric chest compressions. Researchers will evaluate the usability and user experience of the augmented reality cardiopulmonary resuscitation tool in an international multicenter randomized simulation study, with the aim of improving the quality of chest compressions and saving lives.

This research will culminate in a randomized controlled trial of the Peer PLUS (People Leveraging Urgent Support) mobile app and companion web-based platform that supports communication between those with substance use disorder and peer recovery coaches to determine if it contributes to long-term recovery for these individuals.

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.

This study created, trained, and tested machine learning (ML) algorithms to predict emergency department returns and morbidity or mortality among returns for COVID-19 patients, with results showing ML’s potential to inform clinicians on whether admission to the hospital may be needed for the prevention of complications and to prioritize resources for higher risk patients.