A new algorithm developed by the Technion – Israel Institute of Technology helps doctors select more effective, personalized antibiotic treatments for patients. After doctors at Maccabi Healthcare Services adopted the algorithm for patients with urinary tract infections (UTIs), they saw a 35% drop in the need to switch a patient’s antibiotic regimen.

Maccabi first applied the algorithm to patients with urinary tract infections (UTIs) – the most common bacterial infection among women. Around 30% of women suffer from UTIs at least once during their lifetimes, and up to 10% experience recurrent infections. Until now, in most cases general treatment has been administered based on clinical guidelines and medical judgment. Sometimes, the bacteria prove to be antibiotic resistant, resulting in the need to change the treatment plan.

Yet after treating tens of thousands of cases with the new algorithm, Maccabi doctors saw a significant drop in the number of patients who need to switch antibiotic regimens, demonstrating that the algorithm is successfully identifying the right antibiotic for the patient.

Professor Roy Kishony

The algorithm, developed by Professor Roy Kishony and Dr. Idan Yelin of the Technion Faculty of Biology in partnership with KSM (Kahn-Sagol-Maccabi), the Maccabi Research and Innovation Center, recommends the most suitable antibiotic treatment for the patient to the doctor, based on clinical guidelines and other criteria such as age, gender, pregnancy status, residence in an assisted living facility, and personal history of UTI and antibiotics administered.

According to Prof. Kishony, “The algorithm we developed together with Maccabi’s experts is a major milestone in personalized medicine on the way to AI-based antibiotic treatments, which are personally tailored to the patient according to the prediction of treatment response and mitigate the development of resistant bacteria.”

Based on the success of Prof. Kishony and Dr. Yelin’s algorithm in treating patients with UTIs, Maccabi is now developing additional detection systems that will help physicians develop personalized antibiotic treatment plans for infectious diseases.

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Roy Kishony
Technion Professor, Roy Kishony
Roy Kishony