AI & Behavioral Insights
Our unit, the first of its kind embedded within the Israeli healthcare system, is dedicated to providing patient-centered healthcare services by utilizing the power of behavioral science and applied computational modelling. The unit’s innovative approach integrates these two fields in collaboration with stakeholders across the ecosystem. We identify the unique needs of each patient and offer personalized interventions that result in significant and sustainable improvements in patient engagement, adherence to medical advice, and clinical outcomes.
As AI advances and takes on a more prominent role in healthcare, we are seeing a worrisome gap between the recommendations of AI and decision support tools and the actions and decisions taken by patients. Through our comprehensive approach to healthcare delivery, and prioritization of patient engagement and adherence, we are working to close this gap. By empowering patients to make informed health-related decisions, we prevent chronic conditions, help manage patient care, reduce healthcare costs, and enhance overall patient satisfaction.
By analyzing large datasets on patients' behavior, such as purchase history, doctor visits, and search queries, we identify patterns and trends in patients’ behavioral profiling and create insights into patients’ health-seeking behavior, preferences and needs. On a system wide level, this analysis brings emerging trends to the forefront and empowers the system to tailor their strategies to create more effective processes and insightful AI-based application.
Every intervention we design is informed by our behavioral infrastructure to encourage and promote a desired health-related behavior (e.g. adherence to medical guidelines, medication adherence, vaccine uptake, appointment attendance, etc.). We go far beyond the ‘one size fits all’ approach, which, while effective to a degree, is not sufficient. Our data-driven machine learning algorithms recognize individual differences and play an important role in personalizing interventions. Thru identifying the distributional effect, these models enable us to tailor behaviourally informed interventions for each patient thus ensuring that each patient gets the optimal intervention.