Academic Medical Center

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 will develop a prototype guide for the safe and effective integration of ambient digital scribes into primary care, providing insights into how this artificial intelligence-driven technology transforms workflows while addressing critical safety, equity, and burnout concerns in diverse healthcare settings.

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.

This research will develop and evaluate the Perioperative Optimization of Senior Health (myPOSH) mobile application that supports care transitions and the needs of older adults undergoing surgery and their care partners.

This research will implement, evaluate, and disseminate DDInteract, a shared decision making clinical decision support tool that aims to reduce harm from oral anticoagulant drug-drug interactions.

This research will culminate in a randomized controlled trial of the Peer PLUS (People Leveraging Urgent Support) mobile app and companion web-based platform that supports communication between those with substance use disorder and peer recovery coaches to determine if it contributes to long-term recovery for these individuals.

This research will create clinical decision support artifacts for three patient-centered outcomes research guidelines around advanced diagnostic imaging using standards to allow them to be shareable, interoperable, and scalable; and implement them in different workflows and settings measuring their impact. 

This research will create the Shared Decision Aid Navigator System to improve the implementation, integration, and scaling of patient shared decision aids into health informatics systems, aiding shared decision making and improving patient-centered outcomes.