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 research aims to integrate an electronic sexually transmitted infection (STI) risk assessment tool for adolescents into four pediatric primary care clinics.
This research aims to adapt a decision support tool that integrates clinical risk information with patient preferences. The goal of this work is to support patients in making informed breast reconstruction decisions together with their clinicians.
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.