Prealize Health

Prealize Health provides an AI-driven predictive analytics platform for the healthcare industry, aiming to shift the sector from reactive to proactive care. The company was founded in 2015 as Cardinal Analytx by Dr. Arnold Milstein and Dr. Nigam Shah, both hailing from Stanford University. Dr. Milstein is a Professor of Medicine at Stanford, directing the Clinical Excellence Research Center, with a background in designing healthcare delivery models that lower costs. Dr. Shah is a professor at Stanford known for his work in making machine learning models clinically useful. Their shared vision was to use machine learning to predict which individuals would face medical issues in the near future, allowing for early intervention.

The company rebranded from Cardinal Analytx to Prealize Health in January 2020 to better reflect its mission of connecting risk prediction to preventive action. This transition was supported by a $22 million Series B funding round in May 2019. Over its history, Prealize Health has raised a total of $44.4 million in funding. In August 2022, Prealize acquired the intellectual property of CentraForce Health, enhancing its capabilities with a robust library of data on social determinants of health (SDoH).

Prealize Health's business model centers on providing its predictive analytics services to a range of clients, including health plans, specialty care management companies, providers, and employers. The company's core product is its MetisAI platform, a sophisticated AI model developed at Stanford that analyzes vast datasets, including medical claims, prescriptions, and lab data, to forecast future health events, costs, and utilization trends. The platform can predict the likelihood of a health event and, crucially, when it is likely to occur (Time-To-Event), which allows clients to intervene proactively. This technology helps organizations identify high-risk individuals up to 12 months before a diagnosis appears on a claim, manage financial risk more accurately, and improve member engagement by understanding who is most likely to respond to outreach and through which channels.

Keywords: predictive analytics, healthcare analytics, AI in healthcare, proactive healthcare, machine learning, population health management, clinical excellence, health data science, risk prediction, care management, Nigam Shah, Arnold Milstein, MetisAI, social determinants of health, healthcare cost reduction, patient engagement, financial risk management, proactive care, health insights, value-based care