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    • Home
    • AI in ACO Project
    • Discussion Forum
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  • Home
  • AI in ACO Project
  • Discussion Forum
  • Library
  • Contributors

Join our Pilot about AI in ACOs

Ask about joining!

Welcome

We are seeking active participants from the ACO community who have interest in leveraging AI technologies to analyze and manage Shared Savings / Shared Risk arrangements.  


Our group has arranged access to a large-scale AI processing capability, together with resources who have done AI Machine Learning for organizations that include Google and LinkedIn.  


These resources are available at no charge for an initial handful of ACOs with an interest in exploring the use of leading-edge technologies.  


Our target knowledge domains are in the administration of ACO Shared Savings and management of downside risk. 

Potential Exercise Domains

Manage Network Leakage

Identify new Opportunities for Administrative AI in ACO

Predict TIN-Level Trends

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What if Machine Learning could predict which patients are likely to embark on care programs that inappropriately use excessive or overly expensive resources?  

Predict TIN-Level Trends

Identify new Opportunities for Administrative AI in ACO

Predict TIN-Level Trends

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Large ACOs with many TINs might benefit greatly from Big Data analytics. 


Join into the discussion, and share you own creative concepts.  Our team will take it from there.  

Identify new Opportunities for Administrative AI in ACO

Identify new Opportunities for Administrative AI in ACO

Identify new Opportunities for Administrative AI in ACO

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 Big Data creates the opportunity to discover unexplored patterns and establish non-linear dependencies. New Machine Learning tools can highlight granular relationships not available to traditional data analytics 

Subscribe

Join our Forum where you can follow discussion threads and share your own ideas along the way.

Download our ACO AI fact sheet

If you need a Quick Reference on what AI might mean in ACO, take a look at our one-page overview

HC AI 1-Pager Working Doc 11-25-19 (pdf)Download

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