Search found 34 items
This project will develop and evaluate an electronic health record-embedded clinical decision support tool that draws upon the strength of analytical and naturalistic decision-making to optimize the use of blood cultures in critically ill children.
This project will evaluate and compare different tools within electronic health records to assist pediatric primary care clinicians with providing higher quality childhood obesity care to help slow weight gain in children with obesity.
This project will apply machine learning against a large data set to develop a model to both understand and predict surgical cancellations on individual pediatric patients at two pediatric surgical sites.
This project developed, implemented, and evaluated a program that includes clinical decision support to improve diagnosis of hypertension in children.
This project integrated a validated anxiety-specific screening tool in an existing clinical decision support system and tested it with a randomized feasibility pilot that found the tool did not increase detection of anxiety in pediatric primary care.
This project seeks to develop an understanding of the cognitive work of clinician teams and family members involved in pediatric trauma care transitions in order to design usable and useful health information technologies.
This project will create and evaluate the impact of immunization reminders using information from an electronic health record combined with an immunization information system.
The project team will develop a set of mHealth tools capable of collecting health behavior information and evaluate whether providing clinical feedback on these behaviors reduces obesity and improves health behaviors among at-risk families.
This project developed a tool to promote activation, communication, engagement, and self-management of pediatric blood and marrow transplant patients and their parents and found that patient-centric tools can successfully engage caregivers in hospital care.
This project enhanced the Children’s Electronic Health Record Format (Format) by identifying a high priority set of 47 functional requirements from the initial larger set, and creating a list of 16 recommended uses of the Format along with implementation notes.