Imagine what could you achieve if you could comprehensively derive a broad set of actionable and explainable clinical insights from your existing data and if you could confidently identify patients simply in need of routine care or better match intervention or a clinical trial. Imagine if you were able to better prioritize those patients at the highest risk and if you could optimize the speed of care delivery. Finally, imagine if you had predictive insights at your fingertips to optimize your streams of data or improve the diagnostic power of your existing markers. EarlySign powered solutions enable healthcare professionals across the continuum to make more of their existing data to augment the patient care journey and enhance the value brought to the people under their care.
Accelerate your digital transformation and make more informed clinical and precise health management decisions by deploying AlgoMarkers, EarlySign’s advanced AI-powered predictive models to allow for case-finding, stratification, prioritization, tracking, and monitoring of your population all running off our AlgoAnalyzer middleware. Â
As one of the two finalists in the recent CMS AI Challenge, EarlySign was recognized for how well our solutions could easily explain results from their clinical predictive models to both clinicians and patients. We understand what it takes for successful AI deployment, and we are aligned with our partners with the goal of making insights explainable.
EarlySign’s AlgoMedical Framework contains a comprehensive set of tools and pipelines to manage the holistic clinical machine learning development cycle. Starting with semi-manual data preparation tools through cohort design and all the way through performance evaluation and fully automated models packaging, the framework enables our data scientists to cull through a sea of data to glean the most meaningful insights available and allow for the most informed decision making.
Through AI-powered AlgoAnalyzer and AlgoMarkers, EarlySign can seamlessly integrate a variety of machine learning-based insights into existing clinical workflows and external delivery platforms, collect operational and performance data, track progress, and provide actionable analytics for ongoing improvement.
