Case Study: KenSci improves patient risk prediction and saves lives with Microsoft Azure

KenSci is fighting death with data science by taking advantage of the Microsoft cloud and data platform

KenSci is a machine‑learning risk‑prediction company that uses EMR, claims, psychosocial, operational, and patient‑generated data to help payers and providers move to value‑based care. Faced with healthcare’s reliance on imprecise heuristics, fragmented data, time‑pressed clinicians, and strict security and compliance requirements, KenSci set out to improve precision in identifying who will get sick, when, and how severe the outcome may be.

Built on Microsoft Azure and Azure Machine Learning, KenSci’s Risk Prediction platform combines more than 150 prebuilt models with real‑time device streams and hundreds of variables to predict care and cost risks for 17 million patients. The solution can spot rapid deterioration up to eight hours in advance, delivers enterprise‑scale deployments and measurable ROI within a business quarter, and leverages deep Microsoft partnership and AppSource distribution to accelerate adoption while meeting healthcare security and compliance needs.

KenSci

Larry Levy

Head of Business Development