Using Innovative Digital Healthcare Solutions to Improve Quality at the Point of Care (R21/R33 - Clinical Trial Optional)

This research examines whether remote therapeutic monitoring can improve physical therapy outcomes by increasing patient engagement, adherence to home exercises, and communication with providers, while also assessing its impact on healthcare costs and feasibility for broader implementation.

The study will develop, validate, implement, and assess the usability of a machine learning based clinical decision support tool designed to reduce laboratory testing overutilization in pediatric intensive care unit patients.

This study aims to optimize the use of donor hearts for infants and children awaiting heart transplantation by developing predictive models to assess in real-time the potential for transplant success and to evaluate risk. Researchers plan to display these data through intuitive visualizations on a custom-built interface to reduce clinicians’ cognitive burden, enhance decision making confidence, and help ensure the best donor match for pediatric patients.

This study will develop and establish the efficacy of an actionable predictive model to identify pregnant individuals at high risk for postpartum hemorrhage which can be used in combination with a clinical decision support tool to reduce the risk of hemorrhage-related morbidity and improve maternal health outcomes.

This research explores using natural language processing and generative AI to capture and structure social determinants of health from patient narratives, aiming to improve data completeness and quality, enhance clinical decision support, and reduce the manual burden on clinical staff in routine care.

The study will develop and test a vendor-compatible clinical decision support system to support intensive care unit nurses and physical therapists at the point of care in the timely identification of patients suitable for activity interventions and the creation of personalized activity plans.

The study will create, implement, and test a patient-centric web app to support older adults with chronic conditions in comprehending, managing, and acting upon their lab test results.

The study will create and implement digital tools using the SMART on FHIR framework to support Age-Friendly care in clinical practice and institutional decision-making.

This research will develop, evaluate, and test the efficacy of Empower NICU – A Bridge to Resources for Adjusting and Coping with Emotions (EmBRACE), a mobile health application designed to screen and monitor psychological symptoms in parents of infants hospitalized in the neonatal intensive care unit, identify those at risk, and connect parents with services, information, support, and resources.

This research will develop, implement, refine, and evaluate an app to support clinical decisions for ST-Elevation Myocardial Infarction care in rural areas by emergency medical services providers, reducing the time between first medical contact and reperfusion therapy to reduce morbidity and mortality, and improve health outcomes.