This research will enhance, refine, and evaluate an artificial intelligence-supported tool that collects patient-reported outcomes to inform care and improve glycemic control for patients with poorly controlled diabetes.
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 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 improve upon and evaluate a telehealth lung cancer surveillance program that combines patient-reported outcomes and assessment of remotely completed surveillance lung imaging with telehealth visits, to reduce barriers to lung cancer surveillance care.
This research will adapt and extend Intervention INC, a family-centered, interactive, health promotion tool, and evaluate its effectiveness on reducing childhood obesity risk.
This research will use artificial intelligence and machine learning to create prediction tools integrated into visualization dashboards to guide critical care pharmacists in preventing adverse drug events.
This research will adapt a pain management virtual reality application for use on a smartphone to manage pain during dressing changes in pediatric burn patients.
To assist pregnant individuals with pre-pregnancy type 2 diabetes with Medicaid coverage in reaching and maintaining normal blood sugars, this research will develop, test, and evaluate a digital health solution called ACHIEVE that includes a mobile health application, a provider dashboard, continuous glucose monitoring, and team-based coaching for medical needs and nonmedical health-related social needs.
This research evaluates an artificial intelligence risk predictive tool called CoMET that uses visual outputs of patient data to serve as an early warning system for patients at risk of cardiac decompensation to allow for earlier intervention and reduction in morbidity and mortality.
This research aims to examine changes in nursing clinical documentation from 2019 through the COVID-19 pandemic timeline to observe the impact of electronic health record burden on patient care.
