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Kaiser Permanente Northwest Engages Medial EarlySign for AI-Based, Patient-Specific Treatment Prioritization

Kaiser Permanente Northwest Engages Medial EarlySign for AI-Based, Patient-Specific Treatment Prioritization

Medial EarlySign have announced that Kaiser Permanente Northwest (KPNW), one of America’s leading integrated health care systems, has decided to engage with Medial EarlySign on development of Medial EarlySign’s AlgoAnalyzer™ insight and risk predictor platform.

Medial EarlySign’s machine learning and mathematical models (AlgoMarkers™) help healthcare organizations identify subpopulations of patients who may benefit from different interventions or may be at high risk for specific medical conditions, including cancers, diabetes, kidney disease and other life-threatening conditions. This information is used by healthcare organizations to optimize resources and improve intervention efficiency and outcomes.

“Kaiser Permanente is among the most advanced and forward thinking healthcare organizations. Their decision to work with us highlights the value of our AI solutions to the future of healthcare in helping to prioritize outcome-based and cost-effective patient care. We are continuing to see significant interest in our solutions from healthcare organizations throughout the U.S. and around the world.” said Ori Geva, Co-founder and CEO of Medial EarlySign.

Using routine Electronic Health Records (EHR) data, the condition-specific AlgoMarkers™ are built to deliver clinical insights where and when patient care decisions are being made.  AlgoMarkers™ support decisions on a variety of potential outcomes, assessing condition-specific risk, determining the best clinical path or identifying patients most likely to respond to intervention. AlgoMarkers™ are designed to create new insights that provide healthcare organizations with opportunities for more precise decision making and implementation of clinically and economically effective work flows and preventative care, helping to prevent or delay progression of illness and improving patient outcomes.

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