The project team successfully developed and implemented an automated system for measuring the rate of adverse drug events in pediatric patients.
The investigators used a mixed-methods approach to incorporate quantitative and qualitative research in developing and validating a health IT adaptation survey.
The research team developed and tested algorithms that can predict postoperative adverse outcomes with a high degree of accuracy.
This study adapted and implemented an electronic sexually transmitted infection (STI) risk assessment program in general pediatric practices in the St. Louis metropolitan area, successfully increasing screening and testing among adolescents in this high-incidence area.
This study tested the effectiveness of the BREASTChoice breast reconstruction clinical decision support tool on decision quality, surgical choice, and shared decision making. Findings from the use of the tool showed improved patient knowledge and reduced immediate breast reconstruction following mastectomy.
This research will develop and evaluate a machine learning-augmented and telemedicine-augmented sociotechnical intervention for postoperative handoffs to reduce the risks of patient complications and improve patient-centered care.
