Tyler
With over 3,700 Medicare Advantage (MA) plans serving nearly 33 million enrollees in 2024, securing adequate funding is essential for ensuring providers can meet member demand. Policymakers recognized the importance of accurate plan payments over two decades ago with the 1997 passage of the Balanced Budget Act (BBA), which mandated the creation of Medicare + Choice (M+C) plans (now Medicare Advantage) and the use of risk-adjusted payment for such plans. Implementing risk-adjusted payments within Medicare + Choice was a critical step in providing these plans accurate funding. This method incorporated both the health status and demographics of each patient, unlike traditional approaches that only focused on demographic attributes and failed to include key factors like patient acuity.
In response to the BBA risk-adjusted payment mandates, CMS spent several years collecting historical data to develop, test, and publish the first Hierarchical Condition Category (HCC) risk adjustment model, finalized in 2003. The completion of this model marked the first major step in calculating risk-adjusted payments for MA plans, which previously relied on demographic-based payments adjusted only for age, sex, county, and limited factors like Medicaid eligibility, often misaligning funding with patient needs. In 2003, the Medicare Modernization Act (MMA) reinforced this shift with reforms that incentivized plans to compete on efficiency and document diagnoses thoroughly to ensure accurate risk scores. In 2004, CMS released an updated ratebook that detailed standard reimbursement rates for services, reflecting improved pricing methodologies. The CMS began phasing in the HCC model for MA plan payments at the same time, achieving full implementation by 2007.
The CMS-HCC model is a predictive risk adjustment tool designed to forecast future healthcare costs for patients. The primary function of the model is outputting a patient’s Risk Adjustment Factor (RAF)—a key element in calculating the per-member-per-month (PMPM) payments that MA plans receive to manage a patient’s health.
The CMS-HCC V28 utilizes demographic data and medical conditions to assign a RAF to each MA patient, which predicts future healthcare costs and directly influences plan payments. The process of building the HCC model and calculating a patient’s RAF begins with data input. Calculations rely on a patient’s demographics (age, gender, socioeconomic status, disability status, and insurance status) and medical conditions. Medical conditions are captured using ICD-10 codes, which reflect a patient’s condition by specifying the category and etiology of the disease (see example below).
Once a patient’s data is recorded, it can be used to calculate their risk scores. ICD codes are first mapped to specific condition categories—groups of related medical diagnoses defined by a classification system—and organized by anticipated costs. These condition categories are then arranged into hierarchies weighted by complexity or severity of the disease. One of the most common hierarchical categories, diabetes (HCC 36-38), is illustrated below.
Patients will be classified under the highest severity code that applies to them. For example, if a patient triggers all three codes in the diabetes hierarchy (listed above), the HCC model would only use HCC 36 in its risk calculation as it represents the most severe condition in the hierarchy.
HCC codes are combined with previously documented demographic data to compute a patient’s RAF. The standard patient score of 1.0 represents the average individual who is expected to consume a standard amount of healthcare resources. A RAF of greater than 1.0 represents a sicker patient who is expected to consume more resources, while a healthier patient with fewer complications would have a RAF of less than 1.0. The model adjusts risk scores for disease interactions, recognizing that the presence of multiple conditions may snowball into higher costs.
Figure 1: Example RAF Calculation
After risk scores are defined, they are multiplied by a predetermined dollar amount to finalize the payments for each plan enrollee. These predetermined dollar amounts are set by CMS and published annually in forward-looking ratebooks that define payments to MA plans for the following year. Risk scores are also prospective as they use diagnoses from a prior year to calculate a patient’s expected costs and consumption of resources in the future. Achieving an accurate representation of patient health ensures that MA plans receive proper financial coverage to meet the health needs of enrollees in the following year.
CMS has periodically released new versions of HCC since 2007 to recalibrate the model using updated data, refined condition categories, and risk scores. There were seven primary models before V28—the creation of each major iteration was driven by evolving coding practices, changes in treatment costs, and new clinical understanding that shifted the ability to accurately capture patient health. The most recent jump from v24 to V28 reflects a significant overhaul rather than incremental updates due to a shift from ICD-9 to ICD-10 and a multi-year reclassification effort.
To better conform to industry standards (ICD-10 has been used for diagnosis and inpatient reporting in the U.S. since 2015) and enhance the CMS’s ability to accurately classify medical conditions, V28 increased the number of condition categories from 86 to 115 and altered HCC mappings by replacing 2,000 old diagnosis codes with 268 new ones. These updates leverage ICD-10’s granularity to improve risk score precision and align payments with current clinical practices. The condition categories that have suffered the largest decrease in HCC mappings include vascular disease, metabolic disease, and psychiatric disease. The vascular disease group is the most impacted HCC, with over an 82% average decline in HCCs mapped from V24 to V28.
By altering the mappings and coefficients assigned to each HCC category, the reconstruction of the HCC model and use of ICD-10 codes contributed to a 3.12% average decrease in patient RAF compared to V24. A significant reduction in RAF creates clear industry headwinds for MA plans because it directly lowers the multiple applied to base payment rates, reducing overall reimbursements. In an effort to mitigate the effects of this change, providers should focus on accurate documentation and other coding practices that ensure the payments they receive reflect the true health of their patient population.
If ethically possible, medical practices may recoup some of this lost revenue by seeking partnerships or entering into parallel lines of business that are expecting RAF coefficient increases like multiple sclerosis (MS) or stage 4 chronic kidney disease (CKD). Entering into an adjacent opportunity can not only provide more opportunities care for those in need, but also secure better financing that supports the health of an entire organization.
While a decrease in the average RAF presents clear challenges, MA payment rates will be receiving a notable bump next year. The Trump administration recently announced its final rule that “payments from the government to MA plans are expected to increase on average by 5.06% from 2025 to 2026”. Payer advocates are broadly pleased, as this ruling represents an increase of 2.83 percentage points since the 2026 Advance Notice and an increase of 2.19 percentage points compared to the average annual growth rate of MA plan payments. This change marks a major step in reverting some of the damage that is expected to come from changes to the HCC model. Providers must stay actively engaged with policy news to ensure their practice is well-positioned to capitalize on advances and mitigate against market uncertainty.
CMS-HCC V28 has broad implications as the U.S. population continues to age and a greater percent of Medicare enrollees sign up for Part C MA plans. Its expanded 115 HCC categories and phased-in revisions demand that providers and payers master its payment structure—linking ratebook amounts to risk scores—to thrive in their markets. By embracing these changes, stakeholders can ensure sustainable, equitable, and responsive care for millions of individuals across the country.
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