Predictive Medicine

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Today, Clalit utilizes the C-Pi platform in preventing and treating Hepatitis C, Diabetes and Osteoporosis, among other conditions:

• Hepatitis C is a virus transmitted through blood-to-blood contact, which can lead to chronic infection of the liver. Over time, the virus can cause damage, making it a leading cause of liver cancer and liver failure. Half of the carriers remain undiagnosed since they have never been tested for the virus. However, in recent years, new drugs have been developed, leading to a cure rate of over 97%. The C-Pi platform is helping Clalit’s clinicians detect individuals at high risk of carrying the virus. The model has been highly effective in identifying undiagnosed carriers and helped identify dozens of new carriers that can now receive the lifesaving treatment. In addition, the C-Pi Platform assists Clalit’s clinicians in the treatment and monitoring process of every patient identified as a carrier. For example, the platform can detect patients who tested positive for virus antibodies many years before the availability of the current effective drugs and alert the clinicians that effective treatment is now recommended and available. For every patient who has ever tested positive for the virus, whether many years ago or only recently, C-Pi offers the most tailored treatment recommendations based on the patient's underlying medical conditions. In complex cases, where the platform identifies existing liver injury or other associated illness that complicates the treatment process, it will also recommend referring the appropriate patients to a specialized liver clinic in order to provide each patient with the best and most personalized treatment for their specific condition.
   

• Diabetes is a disease characterized by high levels of sugar in the blood, which can lead to complications in the heart, kidneys, vision, nervous system, and more. The C-Pi platform provides Clalit’s clinicians with personalized follow-up and treatment recommendations for diabetic patients based on their full medical history, previous and current treatments, and the latest lab test results. C-Pi also provides the clinicians with proactive recommendations if a change to the worse in the patients’ latest lab results is identified. The platform generates recommendations according to clinical pathways written by diabetes specialists and primary care physicians according to the international diabetes guidelines, and updates regularly according to the latest research and global recommendations. It includes personalized recommendations for lab tests, referrals to relevant specialists, guidance on how to use advanced technologies for monitoring that can contribute to balancing sugar levels, as well as detailed treatment recommendations.
   

• Osteoporosis is a disease that causes a decrease in bone density and an increased tendency for bone fractures. The disease is very common, especially among women after menopause. Osteoporosis is a "silent" disease and often goes unnoticed until the fracture occurs. The typical fractures associated with the disease occur in the hip, spine, arm, and wrist. Some of these fractures can lead to mobility difficulties, nerve damage, and even death and disability in non-negligible percentages. Therefore, it is crucial to prevent bone density deterioration. By early identification of patients at high risk for osteoporotic fractures, clinicians can offer suitable preventive measures and treatments, which will decrease the number of fractures and improve the quality of life as well as the life expectancy of these patients. To identify patients who are at high risk of developing bone fractures complications, the C-Pi platform uses an AI-based prediction model that uses large amount of data collected regularly from digital medical records, including age, sex, previous fractures, underlying diseases, bone density test results, and more. The model even takes into account risk factors for falls in patients, such as eye diseases and the use of drugs that cause drowsiness or confusion. By analyzing all the data in AI-based algorithms, the risk of each patient can be predicted with great accuracy and personalization. The platform also provides the clinicians with personalized recommendations on how to reduce the risk among those patients. Primary care physicians and endocrinologists at Clalit have defined a series of detailed decision algorithms to characterize the most accurate follow-up and treatment course for each patient. These decision algorithms use almost 100 different parameters from each patient's digital medical records to tailor the follow-up and treatment characteristics in the best possible way, based on the most up-to-date knowledge in medical literature.