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.
This research will develop and validate advanced statistical and machine learning methods to assess and improve representational semantic integrity of terminologies in large clinical databases.
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 research will adapt and implement a digital mental health intervention to improve care coordination of mental health services in ambulatory care settings.
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.
