Inadequate treatment of infections frequently leads to complications that cause new visits to the doctor, lengthen hospital stays and can lead to sepsis, even causing the death of affected patients. Several scientific studies have documented that up to 20%-30% of antibiotic prescriptions are incorrect and do not cover the microorganism causing the infection. iAST® is a simple antibiotic prescribing aid tool that applies complex algorithms based on the latest artificial intelligence technologies to accurately predict the best specific antibiotic for a patient, before knowing the definitive microbiological results (bacterial identification and antibiogram). The objective of the present trial is to demonstrate the non-inferiority of iAST® with respect to physicians for the appropriate choice of empiric and semi-directed therapy of common infectious diseases, including sepsis, urinary tract infections and ventilator-associated pneumonias or tracheobronchitis. The adequacy of the medical prescription and the iAST® prediction will be compared taking the antibiogram report as a reference. The study design is retrospective, so that no intervention will be done on the patients. The investigators will conduct a retrospective search for infection cases and note the antibiotic treatment prescribed by the doctors. In parallel, they will enter basic patient data such as age, sex, service where they were treated, type of infection and microorganism (in the case of semi-directed treatment evaluation) into the iAST® software and will write down the first three treatment options recommended by the tool. The treatments of both arms (medical treatment and iAST® prediction) will be compared with the microbiological results and the success rate of each of them will be calculated.
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To demonstrate the non-inferiority of iAST® compared to physicians for the prescription of the empiric and semitargeted antibiotic therapy in patients with common infectious diseases.
Timeframe: 4 months