Clinical Decision Making

The study will develop and implement a validated machine learning model to optimize voiding cystourethrogram timing and use for diagnosing vesicoureteral reflux (VUR) in children, aiming to reduce the significant health and economic impacts of VUR and recurrent febrile urinary tract infections (fUTIs) by standardizing practices, minimizing unnecessary procedures, and ensuring timely diagnosis for those at highest risk, ultimately seeking to prevent renal injury from fUTIs.

The study will develop, validate, implement, and assess the usability of a machine learning based clinical decision support tool designed to reduce laboratory testing overutilization in pediatric intensive care unit patients.

This study aims to optimize the use of donor hearts for infants and children awaiting heart transplantation by developing predictive models to assess in real-time the potential for transplant success and to evaluate risk. Researchers plan to display these data through intuitive visualizations on a custom-built interface to reduce clinicians’ cognitive burden, enhance decision making confidence, and help ensure the best donor match for pediatric patients.

This research uses large language models and machine learning to retrospectively analyze electronic health records of patients with suspected sepsis and identify patterns in treatment outcomes, with the goal of shaping future clinical guidelines that help doctors select the most effective antibiotics for each patient, reduce unnecessary use of broad-spectrum antibiotics, lower the risks of drug resistance, and ultimately improve patient outcomes.

This study will develop and establish the efficacy of an actionable predictive model to identify pregnant individuals at high risk for postpartum hemorrhage which can be used in combination with a clinical decision support tool to reduce the risk of hemorrhage-related morbidity and improve maternal health outcomes.

This research explores using natural language processing and generative AI to capture and structure social determinants of health from patient narratives, aiming to improve data completeness and quality, enhance clinical decision support, and reduce the manual burden on clinical staff in routine care.

The study will develop and test a vendor-compatible clinical decision support system to support intensive care unit nurses and physical therapists at the point of care in the timely identification of patients suitable for activity interventions and the creation of personalized activity plans.

The study will create and implement digital tools using the SMART on FHIR framework to support Age-Friendly care in clinical practice and institutional decision-making.

This research developed and maintained the CDS Connect platform, including its public repository of CDS resources and tools. Current work explores the potential of public-private collaboration for long-term sustainability.

This research will develop, implement, refine, and evaluate an app to support clinical decisions for ST-Elevation Myocardial Infarction care in rural areas by emergency medical services providers, reducing the time between first medical contact and reperfusion therapy to reduce morbidity and mortality, and improve health outcomes.