Worldwide, more than 422 million adults are diabetic, resulting in a death from the disease every six seconds. At least $825 billion is spent annually on diabetes-related costs globally, including treatment, complications, hospitalizations, and lost productivity. In the U.S., UK, and China, more than one-third of all adults have prediabetes. The diabetes epidemic is crying out for early intervention, prevention, and treatment.
No standard exists to predict and stratify a prediabetic’s risk for becoming diabetic within a specific time frame. Medial EarlySign’s groundbreaking work is the first of its kind, developing an IT-based clinical predictor that can isolate a small subset of a prediabetic population that is most likely to become diabetic within 12 months. This offers healthcare organizations (‘HCO’) an actionable timeframe where early intervention could be more likely to deliver better outcomes, creating opportunities for preventative care by delaying or halting progression of the disease. Built with our validated machine learning-based AI platform, our prediabetes to diabetes algorithm is designed to provide HCOs the ability to proactively flag and stratify prediabetic risk, deliver timely care management, and support improved allocation of their diabetes-prevention resources.
In a retrospective clinical data study involving 645,000 patients, including 491,156 controls and 153,921 cases, Medial EarlySign’s algorithm used more than 25 parameters derived from routine medical data and lab results to find prediabetic patients who had the greatest probability of becoming diabetic within one (1) year.
Contact Medial EarlySign for more details and information about the prediabetes to diabetes algorithm study.