Evaluating the Potential Impact of AI on Urinary Tract Infection Diagnosis in the Emergency Department Across Demographic Groups: Retrospective Cohort Study.

Background: Urinary tract infection (UTI) is a common emergency department (ED) presentation but can be challenging to diagnose; both overdiagnosis and underdiagnosis are common, and older adults may be at particular risk of misdiagnosis. Artificial intelligence (AI) shows promise in augmenting diagnosis, but performance across patient populations remains underexamined. Objective: We developed an AI model that combined urine culture positivity prediction and natural language processing (NLP) to predict UTI diagnosis using only information available at the time of a patient's ED visit. We then evaluated the model's performance relative to that of physicians in diagnosing UTI across intersectional patient groups. Methods: We conducted a single-center, multisite retrospective analysis of nonpregnant adult ED patients who had a urinalysis and urine culture test performed during their ED visit at 9 EDs in a single US health system from June 2013 to August 2021. Intersectional groups were defined by binned age (18-44, 45-64, 65-84, and ≥85 years), sex, race, and ethnicity. An Extreme Gradient Boosting classifier model was developed to predict culture positivity (≥10,000 colony-forming units per milliliter) from urinalysis data using 5-fold cross-validation and a 80%-20% train-test split. UTI signs and symptoms were identified using a previously described NLP model. UTI was defined as a positive urine culture and at least 1 UTI sign or symptom identified through NLP. Model performance was evaluated using the area under the receiver operating characteristic curve and rates of overdiagnosis (proportion of patients without UTI mistakenly diagnosed with UTI) and underdiagnosis (proportion of patients with UTI who were not diagnosed ). Model over- and underdiagnosis rates were compared to those of physicians, with physician diagnosis inferred from a composite proxy outcome of either explicit UTI diagnosis or prescription of a relevant antibiotic in the absence of an alternative infectious disease diagnosis. Cross-group performance variance was assessed through the coefficient of variation (CV) for accuracy and diagnostic odds ratio (DOR). Results: Of 149,449 included encounters, 22,521 (15.1%) had positive cultures and 20,080 (13.4%) met the definition of UTI. Model area under the receiver operating characteristic curve was 0.93 (95% CI 0.93-0.93). At a diagnostic threshold of 28%, the model had lower rates of overdiagnosis and underdiagnosis than physicians for each intersectional group. The model's cross-group CV was 0.039 (95% CI 0-0.36) for accuracy and 0.48 (95% CI 0.14-0.81) for DOR. Physicians' CV was 0.080 (95% CI 0-0.40) for accuracy and 0.33 (95% CI 0.004-0.66) for DOR. Conclusions: In this proof-of-concept study, an AI model had lower overdiagnosis and underdiagnosis rates than a proxy for physician diagnosis across intersectional groups, with comparable cross-group variance. While AI has the potential to augment physicians' diagnostic accuracy, real-world applications should account for the model's variable performance across patient groups.