Urinary tract infections (UTIs) are very common in women, who have a lifetime risk of 30% to 50% of developing onem, and up to 10% experience recurrent infections. They also occur in men, but are less frequent because of anatomical differences. 

Common symptoms include a strong, frequent urge to urinate (even a small amount each time); cloudy, dark, bloody or strange-smelling urine, feeling tired or shaky, fever or chills (a sign that the infection may have reached the kidneys) and a painful and burning sensation when urinating. It is usually diagnosed based on symptoms and the testing of a urine sample. 

A kidney infection is, in essence, a UTI that has spread into the kidneys. While this type of infection is rare, it’s also very dangerous, so a doctor must be consulted immediately. If untreated, a UTI can eventually travel through the body, becoming very dangerous, even deadly. 

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.

However, researchers and physicians at the Technion-Israel Institute of Technology in Haifa and Maccabi Healthcare Services – the second largest of Israel’s four public health maintenance organizations – introduced an advanced tool based on artificial intelligence to improve treatment of UTIs. It resulted in a 35% drop in the need to switch antibiotics following the development of bacterial resistance to the drug prescribed. 

The team developed the new algorithm to advise doctors in the process of deciding on personalized antibiotic treatment for patients. This development is significant because accuracy in the choice of antibiotics is far greater thanks to the new technology. In light of the success of this new development in the treatment of UTI, Maccabi has begun working on the development of additional detection systems that will help to treat other infectious diseases that require personalized treatment with antibiotics.

The automated system 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 given.

The unique algorithm was developed by Prof. Roy Kishony and Dr. Idan Yelin of the Technion Faculty of Biology, in cooperation with KSM, Maccabi’s Research and Innovation Center, headed by Dr. Tal Patalon, and was introduced and implemented among Maccabi’s doctors by the health maintenance organization’s medical informatics team and chief physician’s department. 

According to 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.”

Dr. Shira Greenfield, director of medical informatics at Maccabi Healthcare Services, concluded that “the significance of administering personalized antibiotic treatment is that it lowers the risk of antibiotic resistance developing – a global problem which all healthcare entities are working to solve.”