Breaking down ACO data silos with AI-driven prospective risk

ACOs face significant challenges in unifying risk adjustment efforts due to fragmented provider networks, inconsistent documentation, and data silos across multiple EHR systems. These obstacles limit risk capture, leading to inadequate reimbursement and increased compliance risk as ACOs take on greater financial accountability in value-based care. 

Explore how AI-powered strategies can help ACOs standardize prospective risk adjustment and gap closure across complex systems for more accurate, compliant risk capture.

Learning objectives:  

• Understand how AI can unify HIE and EHR data for more complete patient risk profiles 

• Learn how to standardize risk adjustment and gap closure processes by embedding them into provider workflows 

• Discuss strategies to improve provider collaboration by delivering actionable, real-time insights within the EHR

• Enhance diagnosis accuracy by preparing suspected conditions for validation and reducing false positives
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In partnership with:
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