Accurate patient coding is essential for improving outcomes and value-based care performance, see how lab data can help

The hierarchical condition category (HCC) coding system plays a critical role in aligning reimbursement with whole-person care management. Each HCC maps to specific ICD-10-CM codes and is assigned a weighting intended to reflect the expected risk presented by the diagnoses. This weighting, or the RAF score, directly determines payment levels under CMS and Medicare Advantage contracts.

As value-based payment models continue expanding across the healthcare landscape, incomplete or inaccurate HCC coding impacts risk scores, obscures patient complexity, and delays care. This is no longer just a documentation issue but a major financial liability. 

Yet many organizations continue to rely primarily on claims and encounter data for risk capture. This approach has fundamental limitations, as claims lag actual clinical events—often by months—hiding emerging disease complexity when early intervention is most important. Additionally, documentation variability often misses chronic disease evidence embedded in lab results, creating a misalignment between coded risk and the clinical picture. 

Annual condition recapture also becomes a race against the clock, with organizations scrambling to document conditions in the final quarter of the year rather than capturing them continuously as clinical evidence emerges. Relying on claims and encounter data alone puts health plans and provider organizations at risk of leaving millions of dollars on the table in CMS payments while simultaneously missing opportunities to identify high-risk patients who need early intervention.

CMS raises the bar on payment accuracy: Here’s what it means for HCC coding

The regulatory environment is shifting dramatically for Medicare Advantage. CMS proposed significant changes for 2027 that fundamentally alter how MA organizations can document diagnoses for risk adjustment. 

Most notably, they intend to exclude diagnosis information from unlinked chart review records—diagnoses not associated with a specific beneficiary encounter—from risk score calculation. As a result, diagnoses that aren’t reported or associated with a service wouldn’t be considered for risk adjustment. 

This policy change is projected to produce a near-flat average payment update (0.09%) and a decrease in MA payments by 1.53%, or over $7 billion, in 2027. The message from CMS is clear: coding opportunities can no longer be found through retrospective chart review alone. 

Payment accuracy must be tied to actual clinical encounters and documented services. This represents one of the most impactful changes to MA payment in the past decade and signals CMS's commitment to three guiding principles:

1. Simplicity to reduce administrative burden

2. Competition that creates value for patients

3. Payments that accurately reflect beneficiary health risk

The days of relying on retrospective chart mining to drive risk capture are ending. Going forward, real-time, encounter-anchored clinical evidence will be the sustainable path to payment accuracy and quality performance. 

Organizations that modernize their coding strategy around objective clinical evidence, especially lab data, will protect revenue, strengthen quality scores and focus care management where it can change outcomes. 

The hidden cost of HCC coding gaps 

Additionally, the CMS transition from the V24 HCC model to a revised V28 model created additional focus on conditions and related coding that have a true impact on healthcare costs. HCC V28 is made up of 115 HCC codes describing mostly chronic conditions such as diabetes, congestive heart failure and kidney disease. 

This transition has resulted in a reduction in the number of ICD-10 codes associated with HCC and a decrease in RAF scores for some HCC-coded conditions. Medicare predicts an overall decrease in payment of 3.12% with a projected net savings of $11 billion.

In risk-bearing arrangements, reimbursement is tied to a member’s true disease burden. When chronic conditions are undocumented, miscoded or not recaptured periodically, the patient looks healthier on paper, leading to a cascade of negative consequences, including:

• Under-recognition of complexity that skews panel acuity and RAF scores
• Misalignment between quality scores and actual patient populations
• Growing audit and compliance exposure as regulatory scrutiny intensifies
• Missed reimbursement that fails to align payment with anticipated resource use
• Delayed or absent prioritization and clinical intervention for high-risk members since they aren’t surfaced earlier in care management workflows 

The financial impact of incomplete HCC coding is staggering. In one analysis of 417,772 patients managed by a large provider organization, Labcorp found that if the organization addressed all uncoded or miscoded diabetes and chronic kidney disease (CKD) patients alone, they would experience a 300% increase in cost baseline for just those two HCC conditions, with an estimated net impact of nearly $14 million annually.

The problem is pervasive. Working with large provider networks, Labcorp has discovered that coding for diabetes is well documented but still misses 20%-30% of patients. The situation is far worse for CKD, with 70% or more of CKD patients going uncoded or inaccurately coded for the appropriate stage. 

This represents enormous revenue leakage and, more critically, missed opportunities for timely clinical intervention that could prevent costly complications and improve patient outcomes.

Accurate HCC coding starts with accurate clinical insight

Federal sources and professional literature consistently recognize the central role of diagnostics in medical decision-making, yet labs representing only between 2% and 5% of the total cost of healthcare with outsized impact on outcomes. Practically, this means lab evidence is one of the most cost-effective ways to anchor accurate, auditable coding at scale. 

Coding can only be as precise as the clinical insights that underpin it. And clinical insight requires objective, encounter-linked evidence of disease presence and severity that lab data uniquely provides, allowing you to validate and refine HCC capture by:

• Surfacing undocumented disease presence and severity earlier. Lab data reveals undocumented chronic conditions in high-risk members earlier in disease progression, enabling more timely and accurate coding. Labs help distinguish uncomplicated diabetes vs. diabetes with glycemic complications and correctly stage CKD, indicated by sustained eGFR <60 with albuminuria or diabetes, which is first apparent in lab panels—details that materially affect RAF— before claims are generated. Lab data also provides A1c trends, eGFR staging, urine albumin, viral loads and more to support accurate patient lists with more specificity.
• Supporting annual recapture. HCCs “reset” every year. Lab results support annual recapture of chronic conditions, ensuring RAF accuracy and reimbursement alignment year over year. Lab data delivers current-year results tied to encounters and documented management that meet audit standards, align with CMS’s encounter-based direction and help ensure compliant recapture.
• Enabling targeted interventions. Care Management teams can leverage lab data to prioritize interventions with high-risk members before complications occur using clinically validated signals, not just historical coding. Longitudinal lab histories (including results from outside networks) enable proactive rather than reactive care and can illuminate diseases that never flowed through your EHR/claims, improving both risk capture and care planning. 

Labcorp’s diagnostic intelligence: Turning evidence into RAF accuracy 

For organizations dealing with under coded chronic disease, claims lag, RAF leakage, siloed data, limited resources and escalating audit pressure, our solutions can help you deliver diagnostic intelligence at scale. The key is strategic integration rather than wholesale process replacement. 

Labcorp can help you by:

• Surfacing clinical evidence that already exists. Labcorp analyzes national diagnostic data outside of your network to flag members with test patterns indicative of conditions like diabetes and CKD that are present but not fully documented or recaptured.
• Identifying misalignment between clinical reality and coded risk. Labcorp Insight Analytics® is designed to improve coding accuracy and identify and manage high-risk, high-cost patient populations by reviewing patient data and targeting gaps in care.
• Integrating with population health and coding workflows. Rather than creating an entirely new workflow, Labcorp uniquely integrates diagnostic data into existing coding, quality and care management processes. This allows you to compare lab-derived disease indicators with current coding, producing actionable lists for your teams to close gaps confidently without requiring massive operational overhaul.
• Focusing where V28 matters most. Labcorp has experience from objective, lab-informed coding projects that show two outsized opportunities under V28: recapturing uncoded diabetics and dramatically improving CKD identification. 

Instead of retrospective chart reviews that may no longer qualify under new CMS rules, our lab data can trigger prospective documentation opportunities when patients have upcoming appointments. Care coordinators can receive alerts about members whose lab results indicate undocumented conditions, enabling targeted outreach that serves both coding and clinical objectives. Quality teams can also prioritize interventions for members with uncontrolled chronic conditions that impact HEDIS and Star measures.

Rather than adding additional tasks for already stretched teams, better intelligence allows them to prioritize limited resources toward the highest-value opportunities—members who represent both clinical intervention needs and revenue recovery potential.

The measurable ROI of getting it right

When diagnostic intelligence closes documentation gaps:

• Cost of Care alignment improves. The financial impact is immediate and quantifiable, with increased RAF scores translating directly to higher payments that better reflect actual patient risk. RAF scores better reflect true acuity, reducing underpayment and stabilizing budgets under tighter CMS rules. Even modest improvements in coding accuracy can generate millions in additional annual revenue.
• Care management prioritization sharpens. High-risk members receive proactive outreach, medication optimization and guideline-concordant care. For example, appropriately identifying and coding advancing CKD in a patient doesn't just generate higher reimbursement; it means the patient gets enrolled in disease management programs, receive nephrology referrals and undergo medication reviews that may slow disease progression and prevent dialysis before expensive complications occur.
• Operational efficiency. Efficiency increases as diagnostic intelligence reduces the manual burden of retrospective chart reviews that will no longer qualify for risk adjustment under new CMS rules. 

From compliance to competitive advantage

Organizations that integrate diagnostic intelligence and lab-informed coding into their population health strategy will gain a sustainable competitive advantage. They'll:

• Capture payments that reflects actual patient complexity
• Identify high-risk members early enough to make a clinical difference
• Demonstrate stronger quality performance because interventions are targeted to members who truly need them
• Build coding and documentation practices that meet not only today's requirements but future requirements as regulatory expectations continue to evolve

In the shift toward value-based care, that dual benefit—better payment accuracy and better patient outcomes—represents the true untapped ROI of HCC coding accuracy.