An upcoming episode of the award-winning program “Information Matrix” hosted by successful actor and philanthropist Laurence Fishburne, will feature new content on the use of AI-Machine Learning in the treatment of critically ill patients.
In recent years, healthcare providers have made massive investments in electronic medical records. These databases hold vital information that can be used to improve patient outcomes and lower costs, yet their complexity and sheer volume makes them unusable for clinicians. Nowhere is this need more pressing than in the understaffed critical care units, where every second counts.
New AI-Machine Learning tools being developed by Israeli startup CLEW – are converting this data to life-saving medical knowledge that can be used to reduce costs and improve outcomes.
One of earliest adopters of AI-Machine Learning technology are TeleICUs – centralized care teams that manage large numbers of geographically dispersed ICU locations. Having delivered significant improvements in patient care over the past two decades, the TeleICU care model requires new advanced technologies to identify high-risk patients or physiological instability in time for early intervention to prevent a critical event or deterioration.
With the optimal integration of CLEW’s machine learning predictive technology in TeleICUs, clinicians can benefit from proactive, predictive assessment, a more collaborative team approach and efficient clinical resource allocation.
“Information Matrix,” hosted by Laurence Fishburne, is a program tackling stories and concepts that are influencing our modern-day society. Each episode breaks down things happening in today’s society, culture, and business through a long-form documentary presentation. Shared by a diverse audience, “Information Matrix” is a leading television series focusing on society, education, and industries.
CLEW (formerly Intensix), is a real time AI analytics platform designed to predict life threatening complications across various medical care settings, and help providers make better informed clinical decisions, improve outcomes and safety, streamline patient care, efficiently handle regulations and penalties, and lower the cost of care. The platform uses machine learning and data science technology to develop physiological, predictive models at the level of each individual patient in order to deliver predictive warnings during all phases of a patient’s stay. Originally developed and proven in the ICU, these models optimize scarce clinical resources and guide health care providers in predicting patient deterioration, across all care settings. For more information visit clewmed.com