Nurse Practitioner

Using a conference model, this study convenes a multidisciplinary group of experts to explore the integration of human factors engineering approaches in the implementation of artificial intelligence in healthcare, providing an opportunity for ongoing collaboration and research to disseminate knowledge and implement best practices that enhance efficiency, prevent provider burnout, and ultimately improve healthcare quality, safety, and value.

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.