North Ogden

Researchers are evaluating the adaptation and implementation of an innovative clinical decision support (CDS) tool called ePNa, originally validated in emergency departments, for use in urgent care clinics to improve pneumonia diagnosis and treatment. This study aims to combine ePNa with Stanford's CheXED artificial intelligence model to classify chest images quickly and accurately. The goal is to enhance pneumonia care in ambulatory settings where high patient volumes may benefit from updated and efficient diagnostic tools, especially as COVID-19 evolves. The study involves adapting ePNa for use in urgent care clinics by modifying it to work with the more limited patient data available in these settings and incorporating a chest imaging prompt. The combined ePNa-CheXED tool will be piloted among "super user" clinicians during clinic shifts. One of two clusters of urgent care clinics will receive the adapted tool, while the other will serve as usual care control. Additionally, a physician survey will assess clinicians' experiences with ePNa-CheXED six months after implementation. Participants will be patients aged 12 and older diagnosed with pneumonia, confirmed by radiographic imaging. Physicians and advanced practice clinicians in selected urgent care clinics will also participate by completing surveys. The study will monitor outcomes including the accuracy of pneumonia diagnosis, changes in transfer rates to emergency departments, unplanned subsequent ED visits or hospitalizations, and overall healthcare resource use. The study will continue for up to three years, with ongoing evaluation of ePNa use and impact in the urgent care clinical environment.