This research is developing and evaluating a machine learning algorithm that uses existing electronic health record data to predict childhood asthma treatment response and better inform personalized treatment.
The aim of this research is to implement a clinical decision support tool to provide clinicians patient-specific and evidence-based treatment recommendations regarding the recognition and management of high blood pressure and hypertension in children and adolescents.
This research aims to determine the feasibility, acceptability, and outcomes associated with the use of Cloud Care, a cloud-based multidisciplinary care plan for children with medical complexity.
This research aims to integrate an electronic sexually transmitted infection (STI) risk assessment tool for adolescents into four pediatric primary care clinics.
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.
This research study addressed the overuse of blood cultures to diagnose sepsis by developing an electronic health record-embedded clinical decision support tool that draws upon the strengths of analytical and naturalistic decision making.
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 research created, piloted, and evaluated FIQS, the Family Input to Quality and Safety tool, that allows pediatric patients and their caregivers to provide safety reports regarding their inpatient care.
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 research applied machine learning to develop a model predicting surgical cancellations among pediatric patients, and found the feasibility in using these algorithms as a cost-effective quality-improvement measure.
