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Transform Health Outcomes with Precision Care Delivery
Machine intelligence for guiding patients to the right treatments by the right providers at the right times
Right Providers
It is widely established that the choice of provider can have a significant bearing on the outcomes and costs of care. Despite this, traditional approaches for provider selection and network design remain grounded in the assumption that the quality of a provider is the same across all patients.
HEALTH[at]SCALE’s machine intelligence goes beyond star ratings, volume counts and online rankings and instead predictively models how provider outcomes can be expected to vary based on the unique health characteristics of individual patients; to intelligently guide patients to high-value providers and to build networks that are designed for the specific needs of the populations they serve.
Right Times
Many important healthcare outcomes can be prevented through more proactive and preemptive care. These preventable outcomes place an immense burden on patients, providers and payers and impose a significant drain on healthcare resources. However, traditional approaches to predict at-risk patients are inaccurate and focus disproportionately on risk-stratification rather than action.
HEALTH[at]SCALE’s machine intelligence goes beyond approaches focused on high past utilization and instead looks to meaningfully identify which patients may be at elevated risk of adverse outcomes and how outreach efforts can be empowered with precise and actionable information about each patient’s individual trajectory.
Right Times
Many important healthcare outcomes can be prevented through more proactive and preemptive care. These preventable outcomes place an immense burden on patients, providers and payers and impose a significant drain on healthcare resources. However, traditional approaches to predict at-risk patients are inaccurate and focus disproportionately on risk-stratification rather than action.
HEALTH[at]SCALE’s machine intelligence goes beyond approaches focused on high past utilization and instead looks to meaningfully identify which patients may be at elevated risk of adverse outcomes and how outreach efforts can be empowered with precise and actionable information about each patient’s individual trajectory.
Right Treatments
Imprecise, sub-optimal and ad-hoc use of treatments (e.g., drugs, devices, procedures) is a key contributor to poor healthcare outcomes and unsustainable spending. This inability to match patients to treatments is made worse by the widespread incidence of fraud and waste in the healthcare system.
HEALTH[at]SCALE’s machine intelligence goes beyond simple rule-based approaches for guiding treatments and flagging fraud and waste; and instead used deeply contextualized models of personalized responses and provider patterns to deliver real-time decision support for effective and appropriate use of treatments to improve outcomes, adherence and total costs of care.
Personalized. Predictive.
Leading Machine Intelligence for Health Plans, Employers and Provider Systems
Team
Founded and led by top machine learning and clinical faculty and industry experts from MIT, Harvard, Stanford, U-Michigan and Cornell
Mission
Dedicated to using machine intelligence to guide every patient to the right treatment by the right provider at the right time
TECHNOLOGY
Industry-leading predictive and personalized machine intelligence for improving decisions by factoring in tens of thousands of variables
Data
Powered by longitudinal data from over 120 million members covering facility, professional and pharmacy encounters over a decade
Customers
Deployed at scale in real-time operational settings for millions of members for the largest health plans in the country
Recognition
Highlighted by Forbes, Fast Company, Becker’s Hospital Review and UCSF as one of the top machine learning companies in healthcare
