Doctors, nurse or laptop in night healthcare, planning research or surgery teamwork in wellness hospital. Talking, thinking or medical women on technology for collaboration help or life insurance app

" data-large-file="https://i0.wp.com/keebler.health/wp-content/uploads/2025/11/iStock-1477483038-scaled.jpg?fit=1024%2C625&ssl=1" class="alignnone wp-image-1699 size-full" src="https://i0.wp.com/keebler.health/wp-content/uploads/2025/11/iStock-1477483038-scaled.jpg?resize=1080%2C659&ssl=1" alt="" width="1080" height="659">

 

Between CMS-HCC v28 updates, tougher audits, and mountains of unstructured data, health plans and value-based care organizations are looking for smarter, faster, and more compliant ways to capture the full clinical picture.

The stakes are high. The Centers for Medicare & Medicaid Services (CMS) estimates that improper payments across Medicare Advantage (Part C) totaled about $19.07 billion in 2024—an error rate of 5.61%. Much of this stems from missed or unsupported diagnoses, documentation gaps, and fragmented data—exactly the pain points AI can help solve.

 

1. Keebler Health—AI Risk Adjustment Platform

 

Why it’s worth watching

Keebler Health designed its AI platform specifically for risk adjustment—built from the ground up, not bolted on. Its FHIR-first architecture reads both structured and unstructured data (including scanned notes, PDFs, and external sources) to create a gap-free patient story that tells the complete clinical narrative.

What makes it stand out

• Audit-ready from day one: Every suggested diagnosis links directly back to the source document.
• Handles all data types: Claims, EHRs, handwritten notes—you name it.
• Scales across programs: Including Medicare Advantage, ACO, Medicaid, ACA.

 

2. Innovaccer

 

Why it’s worth watching

Innovaccer’s Healthcare Intelligence Cloud aims to unify healthcare data across claims, EHR, pharmacy, and lab systems. Its risk-adjustment module provides a framework for coding gap closure and population health insights.

Strengths

• Point-of-care visibility: Offers in-workflow prompts to assist documentation.
• Comprehensive member view: Creates a consolidated record of patient data.
• Market momentum: Recognized among health data integration tools.

Considerations

• Implementation effort: Integration across data sources can require significant coordination.
• Workflow balance: Frequent alerts and layered interfaces may add complexity for clinicians.

While Innovaccer delivers strong interoperability, organizations seeking purpose-built, audit-ready risk adjustment may find greater precision and transparency in more specialized platforms.

 

3. Cotiviti 

 

The experienced veteran

Cotiviti has deep roots in the analytics space. Its DxCG Intelligence suite merges actuarial efficiency with NLP to support coding accuracy and compliance initiatives.

Strengths

• Reliability: Processes vast claims data for national payers.
• Compliance-first design: Strong track record in audit preparation.
• Responsible-AI governance: Demonstrates attention to fairness and transparency.

Considerations

• Legacy stack: Less agile compared with newer, cloud-native solutions.
• Complex implementation: Often requires coordination between internal IT and service teams. 

Cotiviti’s long history makes it a stable option for established workflows, though modern AI-native systems—like Keebler’s—typically offer greater speed and scalability without the overhead.

 

4. Navina 

 

Why it’s worth watching

Navina integrates AI into provider workflows to surface potential diagnoses during patient encounters. It emphasizes usability and real-time clinical decision support.

Strengths

• EHR-embedded workflow: Helps reduce screen switching.
• Evidence-based prompts: Ties each suggestion to supporting data.
• Ease of adoption: Clinicians appreciate its simple design.

Considerations

• Scope: Primarily focused on prospective coding, not full-lifecycle compliance.
• Workflow change: Successful adoption depends on tailored rollout.

Navina performs well in prospective capture, yet broader data ingestion and audit documentation—core to risk adjustment accuracy—may call for more comprehensive AI infrastructures.

 

5. Reveleer 

 

Why it’s worth watching

Reveleer focuses on retrospective risk adjustment, leveraging AI to extract and validate diagnoses from medical records for defensible audits.

Strengths

• High-volume processing: Handles large chart review projects.
  Audit-defensible output: Maintains document-linked evidence.
• Backlog clearing: Suited to post-encounter catch-up cycles.

Considerations

• Retrospective orientation: Designed mainly for look-back reviews, not real-time coding support.
• Integration lift: Requires extra coordination across data systems.

Reveleer’s capabilities shine in retrospective review environments, but organizations prioritizing proactive, point-of-care capture and longitudinal accuracy often benefit from a more unified, real-time approach.

 

The Scale of the Challenge

 

Documentation gaps and coding errors aren’t rare—they’re systemic. In one audit, 84% of reviewed diagnosis submissions lacked supporting documentation, leading to over $13 million in overpayments.

Even beyond isolated audits, CMS estimates that approximately 9.5% of all Medicare Advantage payments are improper, largely due to unsupported or inaccurate diagnosis codes used for risk adjustment.

AI tools that can read both structured and unstructured data—like provider notes, imaging reports, or scanned PDFs—are helping close those gaps and flag inconsistencies before they turn into audit risk.

 

Choosing the Right Platform

 

When comparing AI platforms for risk adjustment, look beyond the buzzwords and focus on alignment with your workflows and data environment.

 

What to Evaluate	Why It Matters
Data ingestion breadth	Can it read both structured (claims) and unstructured (notes, PDFs) data? Most coding gaps live in free text.
Workflow integration	Does it plug into your EHR or sit outside it? Adoption depends on simplicity.
Audit readiness	Transparent documentation trails matter—especially with billions in improper payments at stake.
Scalability	Can it handle tens of thousands—or millions—of members without slowing down?
Provider adoption	Even the best AI fails if clinicians or coders don’t use it.
Transparency & fairness	Responsible AI practices ensure trust and explainability.
Regulatory agility	CMS keeps updating models; your platform should keep up.

 

 

Final Takeaway

 

AI is rewriting how risk adjustment works—shifting from reactive correction to proactive insight. But success depends on fit, transparency, and collaboration between people and technology.

Each of the five platforms above brings something distinct: scale, integration, accuracy, or usability. The key is choosing the one that best matches your organization’s data maturity and compliance goals.

At Keebler Health, we believe AI should empower—not replace—the humans who understand context, care, and compliance. Because when people and AI work together, risk adjustment becomes less about catching up and more about getting it right the first time.

 

 

See what complete, audit-ready accuracy looks like.

 

Discover how Keebler Health’s AI-driven platform closes documentation gaps and accelerates compliant risk capture.

Get started with a Risk FREE Trial.