Data Visualization

This research develops and evaluates an artificial intelligence-enhanced pretreatment peer-review process in radiation oncology, aiming to improve patient safety by reducing variability among providers in treatment planning, minimizing clinical errors, and enhancing overall treatment outcomes.

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.

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.

This research will develop and validate a clinician-facing longitudinal risk-prediction tool using self-reported data from US military service members and veterans, to assist clinicians in screening, evaluation, and timely intervention in suicide ideation and suicide attempts.