is a mobile-health, physician-prescribed platform, that uses advanced machine-learning tools to reduce the diabetes conversion risk of the most cost-effective pre-diabetes patients.
is a personalized and adaptive intervention program that helps make lifestyle behavioural change.
will be able to target patients with the highest risk for diabetes conversion and highest chances for diabetes prevention in a time horizon that enables intervention to be effective.
Pre-diabetes is a state in which blood glucose levels are higher than normal but not yet high enough to be diagnosed as diabetes.
79 Million US and 63 Million EU adults are pre-diabetic. 70% of these will convert to diabetes in 7-10 years. Currently, there is no method to predict who will convert and who will not and at what time horizon.
Diabetes is the leading cause of heart disease, stroke, kidney failure, non-traumatic lower-limb amputations, and new cases of blindness among adults.
While a diabetic patient costs $9677 per patient per year, pre-diabetic patient costs only $443 per patient per year. Total yearly cost of diabetes is $194B in the U.S and €101B in E.U.
Achieving 5% weight reduction and 150 active minutes per week has proven, in large clinical trials, to reduce pre-diabetes to diabetes conversion dramatically.
Though very effective, this intervention wasn't deployed on large scale due to high cost and scalability challenges.
Vast experience in leading product divisions. Dana was part of the management team in several leading mobile startups, among them Playtech, Snaptu and Win. Dana also served as the deputy director-general of Clalit Health Services Digital-Health Division.
A technology entrepreneur with extensive experience in designing and developing digital interactive systems. In 2009 Mr. Lichtenfeld co-founded Ideomobile, a leading provider of m-Health and m-Banking solutions which developed Maccabi Health Services, Bank Hapoalim, Bank Discount, Janssen mobile platforms.
An Associate Professor at the University of Texas at Austin. Key Opinion leader of machine learning and data mining algorithms for data-driven intelligence, decision making and data-driven predictive modeling techniques. Dr. Saar-Tsechansky serves on the editorial board of the Machine Learning Journal and is an Associate Editor at the INFOMRS Journal on Computing.
Clinically active Family Physician, a key opinion leader in the fields of e-Health, m-Health & Tele-Care. Founder, former manger of Clalit Health Services Digital Health Division – one of the largest e-Health activities in the world. Serves as an advisory board member of several Digital-Health companies, Mentor at Microsoft's Think-Next and Design Healthcare Barcelona Accelerators, Editorial Board Member at eHealth Law & Policy Journal.