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CASE STUDY:
AI-Powered Medical Coding Transformation

Risk Adjustment Automation for a National Healthcare Network

Background

Accurate medical coding is critical to value-based care. Risk adjustment coding directly impacts RAF scores, reimbursement, and compliance in Medicare Advantage and commercial value-based contracts. Historically, this national healthcare network relied on manual coding to process documentation across multiple EMRs. These methods were time-intensive, error-prone, and unable to scale with the volume of unstructured clinical data being generated.

Problem:

The network faced:

  • High administrative burden from manual chart reviews and coder workloads.
  • Missed risk capture due to overlooked HCC diagnoses across disparate EMRs and unstructured notes.
  • Audit exposure from incomplete MEAT-compliant documentation.
  • Escalating costs tied to RPA scripts and manual abstraction processes.

Leaders sought a modernized approach that could reliably automate risk adjustment coding at scale, improve RAF accuracy, and reduce compliance risk.

Solution:

The network partnered with Odesso to deploy an AI-driven risk adjustment platform powered by LLMs, GenAI, and advanced automation. Using OCR, NLP, and AI Agents, the system ingests millions of clinical documents across EMRs and:

  • Extracts diagnoses from structured and unstructured text with natural language understanding.
  • Validates MEAT compliance to ensure audit readiness.
  • Maps ICD-10 codes to HCCs for accurate RAF scoring.
  • Leverages Hyperautomation and AI agents to orchestrate workflows, replacing rigid software.
  • Provides real-time suspecting to surface potential HCCs directly in coding workflows.

RESULT: Risks Converted to Rewards

  • Better coding accuracy led to revenue integrity and fair reimbursement.
  • Coders shifted focus from manual review to higher-value exception handling.
  • Automation supported the network’s Stars and HEDIS quality goals by closing documentation gaps faster.
  • Platform now supports millions of patient records with strong governance and security.

Outcomes

Reduced cycle times for coding reviews from weeks to days.

Improved RAF accuracy by capturing thousands of previously missed HCCs.

70% reduction in reliance on RPA scripts, lowering technical debt and support costs.

Audit-ready documentation with MEAT compliance validation built-in.

Scalable automation enabling coding across multiple states and EMRs.

Odesso delivers autonomous healthcare automation solutions for risk adjustment coding, HEDIS/Stars quality measures, and EMR data workflows. With AI agents, GenAI, NLP, OCR, and hyperautomation, Odesso helps health systems and provider networks modernize operations and succeed in value-based care.

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