Adaptation and Pilot Implementation of ePNa Clinical Decision Support for Utah Urgent Care Clinics

Our questionnaire includes questions on respondent demographics and Likert-style questions about respondent experiences with ePNa. We will validate our modified questionnaire by calculating component loadings and Cronbach Alphas (i.e., internal consistency) of Likert questions loading onto the same components.

ePNa-CheXED will incorporate Stanford University's artificial intelligence CheXED model to provide electronic classification of chest images in \<1 second for elements of pneumonia diagnosis and treatment (radiographic pneumonia, single vs multiple lobes, and pleural effusion).