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Modernizing Risk Adjustment with AI – Smarter Strategies for Healthcare Payers
Modernizing Risk Adjustment with AI – Smarter Strategies for Healthcare Payers
Blog

April 3, 2025

Risk adjustment is central to Medicare Advantage plans—but as CMS ramps up RADV audits and implements extrapolation, the stakes for payers have never been higher. Inaccurate or unsupported HCC coding can now result in significant...

CCAI for Healthcare
CCAI for Healthcare
Page

February 21, 2025

From Intake to Settlement: How AI Simplifies Healthcare Claims Processing for Payers
From Intake to Settlement: How AI Simplifies Healthcare Claims Processing for Payers
Blog

January 9, 2025

The Complexity of Claims Processing Imagine waiting weeks straightforward for claim approval of the health insurance claim process, all because a small piece of data was misplaced. This frustrating delay isn’t a rare exception—it’s an...

Improving Healthcare Quality and Cost Transparency for Employers’ Forum of Indiana
Improving Healthcare Quality and Cost Transparency for Employers’ Forum of Indiana
Case study

November 12, 2024

EFI’s modernization efforts, in collaboration with Quantiphi, culminated in the launch of an upgraded Sage Transparency 2.0 to enhance user experience and functionality. Leveraging AWS breakthrough services, the new platform integrates additional data sources like...

Enhancing Healthcare Support Operations with CCAI Insights and GenAI QA Automation
Enhancing Healthcare Support Operations with CCAI Insights and GenAI QA Automation
Case study

September 5, 2024

Quantiphi developed a virtual agent to handle a broader range of customer queries, thereby reducing call deflection

Unbiased Healthcare: How AI Can Serve Everyone Fairly?
Unbiased Healthcare: How AI Can Serve Everyone Fairly?
Blog

August 14, 2024

The integration of AI in healthcare holds immense potential, offering advancements in diagnosing diseases, optimizing treatment plans, reducing workload for clinicians and more. However, it is crucial to address the potential for algorithmic bias, which...