This research aims to develop and evaluate a clinical decision support strategy to promote influenza vaccination among children who are hospitalized with the goal to identify insights that broadly apply to clinical decision support for health maintenance interventions in pediatric acute care settings.
This project will develop and evaluate the impact of the Prevent Diabetes Mellitus Clinical Decision Support on clinical outcomes, healthcare process measures, and associated costs.
This study used learning health system methods to systematically apply U.S. Preventive Services Task Force’s evidence-based recommendations with the goal of advancing individualized precision prevention.
This project will develop and disseminate an innovative communication system to identify and mitigate health risks for young African American women before pregnancy as a means of reducing health disparities in birth outcomes.
This research used natural language processing and machine learning to develop algorithms to recognize diagnostic criteria in free text for autism spectrum disorder, to increase earlier diagnosis and treatment.
This project created a natural language processing-enabled clinical decision support system to pull patient information and determine recommendations for cervical cancer screening, and demonstrated improvement in overall screening and surveillance rates.
This research assessed the use of a multi-risk adolescent interactive health assessment screening tool in pediatric primary care settings, which found an increased rate of clinician counseling for endorsed behaviors, but no significant change in reported risk behaviors or patient satisfaction.
This project applied a user-centered design process to understand the needs and attitudes for sharing patient-collected lifelog data with providers to improve management and decision making.
This research created and evaluated the impact of immunization reminders using information from an electronic health record combined with an immunization information system.
This project developed and tested a smartphone application of an alcohol intervention for college freshmen and found that it worked as well as a traditional in-person approach.