Prealize Health:

The most advanced predictive analytics in healthcare. We are powering the future of healthcare with confidence and accuracy in action

Founded by two industry thought leaders from Stanford University and through continuous partnerships with Stanford, Prealize is committed to transforming healthcare from reactive to proactive, reducing healthcare costs, and enabling more people to live healthier lives.

Our predictions include:

Knowing each member’s future health events and when those events will happen
Identifying members who are most likely to engage and their preferred engagement channels
Predicting trends and future costs and how they impact group risk, premium underwriting, and stop loss

Our AI-powered predictions are up to 5x more accurate than other solutions

This precision in prediction uses our industry-leading MetisAI Model, developed at Stanford University. MetisAI sets a new standard for accuracy in healthcare prediction

Trains on client data, resulting in higher accuracy and precision
Consistently delivers more accurate predictions than traditional models
Predicts the likelihood of a health event AND when the event will occur: Time-To-Event (TTE)
At Prealize, we’re enabling healthcare from reactive to proactive, creating a new paradigm for healthcare predictive analytics.

The Prealize Advantage:
What Sets Us Apart

A Proactive AI Foundation Model Solution
Built and trained exclusively on healthcare data, our model accurately predicts the next health event and Time-To-Event (TTE) to enable effective action
Validated Technology with Proven Accuracy and Precision
Developed at Stanford University and through a continued partnership, we deliver 5x accuracy over traditional predictive and AI models
Continuous Learning
Healthcare is dynamic and so are our models. We’re committed to ongoing refinement and improvement, ensuring our predictions remain ahead of the curve

Accurately predict financial risk, enabling more precise underwriting for fully insured, level funded, PEO’s and stop-loss

Financial Risk Management
Accurately predict financial risk, enabling more precise underwriting, and stop loss
Care and Condition Management
Precisely identify who will have health events, drivers of events, timing and cost of events
Member Engagement
Unambiguously determine members’ propensity to engage, channel preference and drivers of engagement

MetisAI:
A state of the art healthcare claims Foundation Model

In order to power our Prealize predictions, we have developed a state of the art foundation model based on MOTOR (https://iclr.cc/virtual/2024/poster/18777), a recently published method from Stanford University that was presented as a Spotlight Poster at International Conference on Learning Representations 2024. Developed by the primary author of MOTOR (and incorporating various additional improvements), MetisAI is carefully tuned to predict long term health issues by using a self-supervised transformer that was pretrained on thousands of important time-to-event prediction tasks. As a result, we are able to provide more precise predictions compared both to traditional machine learning techniques and alternative foundation models.

Latest News

January 16, 2025
Introducing the New Prealize Health Website Showcasing the Most Advanced Predictive Analytics in Healthcare
At Prealize Health, we’ve always been driven by a singular vision: transforming healthcare from reactive to proactive. Today, we’re…
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December 10, 2024
From Guesswork to Precision: Solving the Challenges of New Group Health Underwriting with AI
For health plan underwriters, one of the most complex challenges is assessing the risk of new groups for employers or organizations that lack…
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October 21, 2024
Clarify Health and Prealize Health Partner to Revolutionize Predictive Analytics for Health Plans and Providers
Unleashing Unparalleled Precision in Forecasting Patient Outcomes and Healthcare Utilization  Clarify Health, an award-winning healthcare…
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Join the Proactive Health Movement

Request a demo today and witness the transformative power of accurate healthcare predictions.

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