This research will use digital health tools leveraging patient-reported outcomes and data from electronic health records to engage individuals with multiple chronic conditions to improve understanding of individualized risk of adverse events during care transitions.
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.
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 project aims to refine and develop methods to address missing electronic health record data to improve data quality and research validity.
This research is adapting and scaling a previously piloted mobile application for asthma symptom tracking that has been enhanced with functionality relevant to the COVID-19 pandemic to assist primary care clinicians in better managing their patients with asthma.
This research applied machine learning to develop a model predicting surgical cancellations among pediatric patients, and found the feasibility in using these algorithms as a cost-effective quality-improvement measure.
