Sweetch as a Digital Therapeutics Platform, delivers a personalized intervention tool for you and your patients. Our solution provides you and your medical team with insight into your patient’s health information, so you can monitor your patient throughout their journey to better health.
Our easy-to-use dashboard interface, will enable personalized and just-in time follow-up with customized reports. The dashboard will strengthen the relationship between you and your patients, while improving measurable clinical outcomes, patient satisfaction, treatment efficacy and revenue.
Sweetch developed a proprietary machine-learning prediction platform that identifies and quantifies an individual’s risk for developing chronic disease.
This risk stratification is critical for early intervention and effective resource allocation by healthcare providers and payers. Identifying individuals at higher risk of developing chronic disease will allow tailored intervention and drive patients to action.
Sweetch's uniqueness lies in its AI-powered Behavioral Intelligence Engine. It converts data collected from the patient’s smartphone, other connected devices and patient reported outcomes into engaging health recommendations at the right time, place, context, and tone-of-voice – humanizing our experience with technology.
By applying predictive analytics, Sweetch automatically and continuously optimizes patient’s interactions, goals, and messages, to fit the patient’s daily life and real-world capabilities, in a way that increases probability of action.
In a clinical trial conducted by Johns Hopkins Endocrinology, Diabetes and Metabolism Division, Sweetch achieved 86% patient retention, and clinically significant outcomes on all measures evaluated:
“The fact that the study demonstrated both weight and A1C reductions at only three months suggests that long-term effects will be comparable, if not superior, to existing interventions. Sweetch's machine learning technology enables fully automated intervention; hence, supporting larger-scale deployment with greater cost-effectiveness potential when compared with human-based diabetes prevention solution”.
Published by JMIR 2/27/18