Sutherland Risk SigmaIQ

Healthcare Risk Adjustment Solution for Payers

CMS has fully implemented HCC Model V28. RADV audits now target every eligible Medicare Advantage (MA) plan every year. And across the industry, most MA members have at least one undocumented, valid HCC — revenue that plans are leaving on the table, waiting to be found. The margin for error has never been smaller.

The Traditional Risk Adjustment Program is Broken

A manual, sampled, retrospective risk adjustment program was built for a different era. It does two things at once in this complex environment: it leaves earned revenue uncaptured, and it leaves unsupported risk on the books for a CMS auditor to find. Both are now expensive.

Sutherland’s Risk SigmaIQ solution combines AI-powered analytics, NLP-driven clinical coding, and 20+ years of healthcare operations expertise to build a risk adjustment program that is precise, productive, and audit-ready. It helps you capture the revenue you have earned and can prove every dollar of it.

AI-Powered Risk Adjustment: 5 Integrated Stages

While traditional solutions benchmark against your past performance, Risk SigmaIQ delivers AI-powered county, member, and diagnosis-level insights that help health plans uncover hidden revenue, strengthen provider performance, and proactively manage market-specific growth risk dynamics at scale.

⦁ We bring together proprietary population analytics, medical and pharmacy claims data, laboratory signals, behavioral health, and social determinants of health (SDOH) to surface HCC opportunities that conventional programs routinely miss.

⦁ Our AI coding engine analyzes 100% of ALL medical charts – not a sample – so health plans capture every legitimate revenue opportunity while maintaining full audit defensibility.

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Risk Disconnect Analytics

Risk Disconnect Analytics

(STAGE 1)

Identify members across the population with understated risk (revenue opportunity) and overstated risk (RADV risk).

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The result is an intelligent risk adjustment program that improves patient RAF accuracy, reduces operational waste, protects against RADV findings, and scales with the plan’s complexity, without placing additional burden on internal teams.

Measurable Outcomes That Improve the Plan’s Financial Health

Every capability in Sutherland’s solution is designed to drive specific, quantifiable outcomes. Below is what health plans consistently achieve:
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Revenue Integrity

Identify hidden revenue opportunities using predictive analytics
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Higher RAF Impact

Up to 25% RAF improvement achieved
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Smarter HCC Capture

More compliant HCCs identified through analytics
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Faster Operations

AI-driven workflows boost coder productivity
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Calibrated for V28 Reality

Unlock analytics engine-based prioritization that reflects today’s value of each HCC.
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Audit-Ready Compliance

RADV-ready documentation at every review stage
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Enhance Visibility

Actionable real-time insights across risk adjustment performance

Why Sutherland

11,000+ Healthcare professionals

100+ Long-term healthcare clients

20+ Years with top MA payers

85+ NPS score

15 Dedicated healthcare delivery locations

Outcome-based model

Accountable for results, not activity

V28-calibrated Engine

Built for the current CMS model

RADV-Ready by Design

Documentation built in from day one

Frequently Asked Questions

What is healthcare risk adjustment and why does it matter for Medicare Advantage payers?

What is the V28 HCC model from CMS and how does it affect our revenue?

What is a RADV audit and how does Sutherland help with RADV readiness?

Can Sutherland support both retrospective and prospective risk adjustment programs?

How quickly can Sutherland deploy and begin delivering results?

Ready to Capture the Revenue You Have Already Earned?

Every month of delay is revenue your plan will not recover. Sutherland’s Risk SigmaIQ solution is built to move fast, document with precision, and deliver results that survive the most rigorous audit. Let’s start the conversation.

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Get in Touch

Get in Touch

Thanks for reaching out!

We look forward to talking soon.