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Urinary tract infections (UTIs) impose significant health and financial burdens, AI allows providers to intervene accordingly.
Urinary tract infections (UTIs) are the most common infections treated in outpatient settings in the United States (1). They are also the fifth most common type of hospital-acquired infection, with an estimated 62,000 infections occurring annually in acute care hospitals (2). Each year, UTIs lead to approximately 400,000 hospitalizations, seven million office visits, and one million emergency department visits, resulting in roughly $10 billion in expenditures related to UTI care (3). This significant financial burden is compounded by the 13,000 deaths associated with healthcare-acquired UTIs annually (2), of which 69% of catheter-associated UTIs (CAUTIs) are considered avoidable (6).
"UTIForecast AI" is a predictive model designed to identify the risk of urinary tract infections in patients. Analyzes clinical factors and patient history to provide insight into UTIs, allowing healthcare professionals to preventively implement protective measures and improve patient outcomes, while aiming to reduce costs health care partners.
The synthetic data set used for this model was aligned with clinical research findings to accurately reflect the patient's risk factors for UTIs. Research includes epidemiological studies by Medina and Castillo-Pino (1), CDC guidelines on UTI events (2), hospitalization trends analyzed by Simmering et al. (3), Â patient education provided by the Cleveland Clinic (4), and intervention evaluations by Meddings et al. (5). These studies informed the choice of variables and their importance, equipping the model to provide reliable risk predictions.
