HEALTH[at]SCALE’s machine intelligence is designed by a leading team of current and former machine learning and clinical faculty from MIT, Harvard, Stanford and U-Michigan. The company’s platform and applications integrate fundamentally new machine learning capabilities for deeply personalized prediction to enable health plans, provider systems and self-insured employers to match every patient to the right treatment by the right provider at the right time.
HEALTH[at]SCALE’s machine intelligence predictively optimizes complex care decisions to transform both the economics of healthcare and patient outcomes. The company’s products service a broad range of use cases, including: early targeted prediction and prevention of adverse outcomes; optimizing care planning and guiding effective treatment decisions; designing, improving and navigating provider networks; and reducing fraud, waste and abuse. Since its launch, HEALTH[at]SCALE’s technologies have grown to be among the largest deployments to date of machine learning technologies for enterprise healthcare, helping payers and providers manage tens of millions of individuals and find actionable insights in massive datasets hosted in public and on-premise cloud deployments.
“For many priority health conditions, the challenge is not a lack of treatment options but the ability to proactively and accurately determine what the most effective treatment is, who should deliver it and when it should be initiated,” said Zeeshan Syed, CEO of HEALTH[at]SCALE. “Machine learning is unique in its capacity to enable precision medicine with the necessary precision delivery to maximize impact.”