Executive Summary

• Medicare Advantage (MA) is the private-sector based counterpart to fee-for-service Medicare and operates on a capitated payment model, where Medicare gives plans an agreed-upon amount per plan enrollee in exchange for assuming financial risk and complying with program rules.
• MA premiums, though calculated on a geographic basis through a benchmark-bid-rebate formula, are further adjusted through several mechanisms to ensure that plans are appropriately funded to provide coverage for beneficiaries.
• This primer explains how the Centers for Medicare and Medicaid Services adjusts the base capitated rate that CMS pays to plans for MA beneficiaries, including risk adjustment and coding intensity for MA plans.

This primer is part of a three-part series on Medicare Advantage. The first paper focused on the annual rulemaking cycle, and the second paper focused on premium calculations.

Introduction

Medicare Advantage (MA) is the private-plan alternative to traditional fee-for-service Medicare. Under the program, beneficiaries receive their Medicare Part A and Part B benefits through private health plans that contract with the federal government. These plans may also include prescription drug coverage (Medicare Part D) and supplemental benefits such as dental, vision, hearing, or reduced cost sharing. Rather than paying providers directly for each covered service, Medicare generally pays MA plans a fixed monthly amount for each enrollee, and plans are responsible for managing care within that payment structure.

That basic design makes payment accuracy especially important. Not all beneficiaries have the same expected health care costs. A plan enrolling beneficiaries with multiple chronic conditions, disabilities, or other complex needs will face different financial obligations than a plan enrolling a healthier population. Risk adjustment is the mechanism the Centers for Medicare and Medicaid (CMS) uses to account for those differences and to reduce incentives for plans to avoid higher-cost enrollees.

As discussed in a previous primer, MA is governed by an annual, highly structured policy and rate-setting cycle. CMS updates payment benchmarks, risk-adjustment parameters, and operational rules each year through the Advance Notice, Rate Announcement, and MA-Part D rulemaking, supplemented by Health Plan Management System guidance. These processes define the environment in which plans design benefits, build bids, and market products to beneficiaries.

Within that regulatory environment, MA plans calculate premiums to offer to beneficiaries. As discussed in another previous primer, premiums follow a benchmark-bid-rebate formula that determines both Medicare’s payments to plans and the amounts that beneficiaries pay in premiums and cost-sharing. County-level benchmarks derived from fee-for-service spending, plan bids for Part A and B benefits, and quality bonus payments together determine plan payments, rebates, and beneficiary premiums.

This primer explains how risk adjustment and coding operate within Medicare Advantage. It describes how CMS uses the CMS-Hierarchical Condition Category model to translate demographic and diagnosis data into risk scores, how those scores affect plan payments, and why diagnosis documentation has become such an important part of MA operations.

The Basics of Risk Adjustment

In a capitated program such as MA, plans receive a fixed monthly payment per beneficiary to provide Part A and Part B benefits. If all payers were paid the same amount regardless of health status, plans would have a strong incentive to attract healthier beneficiaries and avoid sicker ones. Risk adjustment is designed to reduce this incentive and ensure payments more closely reflect expected costs.

To create this adjustment, CMS calculates a risk score for each enrollee based on demographic characteristics and diagnoses. A risk score of 1.0 represents a beneficiary with expected costs equal to the national average in traditional Medicare; scores above 1.0 indicate higher-than-average expected costs, and scores below 1.0 indicate lower-than-average expected costs. CMS then multiplies plan benchmarks and bids by these risk scores to determine actual per-member, per-month payments. The risk score does not reimburse a plan for treating a specific condition; instead, it adjusts payment based on the expected relative cost of the enrollee.

Risk adjustment serves several policy purposes. It supports adequate payment for plans enrolling high-need beneficiaries, promotes fairer competition among plans with different enrollee populations, and helps reduce incentives for favorable selection. At the same time, because diagnosis coding affects payment, the model creates strong incentives for plans to document enrollee conditions fully and accurately.

How the CMS-HCC Risk Adjustment Model Works

MA risk scores are calculated using the CMS-Hierarchical Condition Category (CMS-HCC) model. The model relies on two main inputs:

1. Demographic factors, such as age, sex, disability status, dual-eligibility status, and institutional status; and
2. Diagnosis information, primarily drawn from claims and encounter data for the prior year.

CMS maps diagnosis codes into HCCs, but only a subset of HCCs are included in the payment model. Those categories are selected based on factors such as clinical relatedness, specificity, severity, predictive value, and the potential to create coding incentives. The model is not simply a list of payable diagnoses; not every diagnosis increases a beneficiary’s risk score. It is a predictive tool designed to estimate relative expected cost across beneficiaries.

The “hierarchical” structure is one of the model’s key features. Within related disease categories, more severe conditions generally supersede less severe ones. This helps prevent double counting when multiple diagnoses reflect the same underlying disease process. For example, a more severe complication within a disease hierarchy may count for risk adjustment while a less severe related diagnosis does not add a separate payment weight.

The model is also prospective. Diagnoses documented in one period are used to predict expected costs in a later payment year. This means that MA payments are not reconciled to an enrollee’s actual utilization in real time. Instead, CMS uses prior demographic and diagnostic information to estimate the relative cost of caring for that beneficiary in the upcoming year.

The current framework for most MA organizations is based on the 2024 CMS-HCC model, often referred to as Version 28 (V28). CMS began using this model in calendar year (CY) 2024, continued the transition in CY 2025, and completed it in CY 2026. The updated model restructured condition categories around the International Classification of Diseases, 10th Revision (ICD-10), incorporated more recent fee-for-service diagnosis and expenditure data, and revised certain HCCs to improve payment accuracy and address coding variation. In CY 2026 CMS also finalized policies to exclude diagnoses from certain sources, including audio-only encounters and unlinked chart review records, with a limited exception for beneficiaries who switch MA organizations. These source-of-diagnosis policies are important because they focus the risk-adjustment system more clearly on diagnoses connected to documented encounters.

Risk Scores in the Payment System

Once CMS translates demographic characteristics and diagnoses into a raw risk score, that score is adjusted before it is used to determine payment. These additional adjustments are intended to preserve the predictive function of the model while preventing changes in coding patterns, model calibration, or population characteristics from automatically increasing total payments.

The first major adjustment is normalization. Normalization factors are designed to keep average fee-for-service risk scores anchored at 1.0 over time, even as coding practices, demographics, and model inputs change. Without normalization, changes in the underlying model or broad shifts in documented diagnoses could increase average risk scores and payments even if the relative health status of the Medicare population had not changed.

The second major adjustment is the MA coding pattern adjustment. This adjustment accounts for differences in diagnosis coding between Medicare Advantage and traditional Medicare. MA organizations generally have stronger financial incentives to capture diagnoses completely because risk scores directly affect payment. Fee-for-service providers are typically paid based on services delivered rather than the full predictive value of a beneficiary’s diagnosis profile. By statute, CMS must apply at least a 5.9 percent downward adjustment to MA risk scores; CMS sets this factor annually in the Advance Notice and Rate Announcement.

After these adjustments are applied, the final risk score is used to scale payment. In general terms, CMS determines the county-level benchmark and compares the plan’s bid with that benchmark for a beneficiary with a risk score of 1.0. The applicable payment amount is then adjusted upward or downward based on the enrollee’s final risk score. A beneficiary with a score above 1.0 generates a higher payment than an average-risk beneficiary, while a beneficiary with a score below 1.0 generates a lower payment.

For plans that bid below the benchmark, higher risk scores increase the risk-adjusted benchmark and can increase the dollar value of the rebate, which plans may use to reduce cost sharing, lower premiums, or finance supplemental benefits. Thus, risk adjustment does not only affect how much a plan is paid for covering a particular enrollee; it can also influence the resources available to support the plan’s broader benefit package.

As a result, plans have strong financial incentives both to manage real clinical risk (to control costs for sicker populations) and to ensure that diagnoses are thoroughly and accurately documented, because higher risk scores yield higher payments.

CMS also incorporates an expected risk score trend into its annual payment projections. That risk score trend is separate from formal policy changes such as updates to the risk adjustment model, normalization factors, or coding pattern adjustment. An increase in projected MA payments can reflect both policy decisions and underlying changes in risk scores.

Coding Behavior and Intensity

Because diagnoses are a key input in the CMS-HCC model, Medicare Advantage plans have strong incentives to ensure that enrollee conditions are fully documented. Traditional Medicare claims may not always capture the full clinical profile of beneficiaries, particularly for chronic conditions that affect expected costs but are not central to a specific billing encounter. In MA, more complete documentation can help align payment with the actual complexity of the enrolled population and support care-management programs. This has given rise to the concept of coding intensity, or the tendency for more diagnosis codes to be recorded for MA enrollees than for similar beneficiaries in traditional Medicare.

Coding intensity can arise through several mechanisms. Plans may educate providers on documentation standards, review medical records, conduct in-home assessments, use vendors to identify potentially missing diagnoses, or structure provider contracts to encourage more complete coding. These tools can identify legitimate conditions that were previously under-documented. They can also create incentives to search for diagnoses that increase payment even when the diagnosis has limited connection to ongoing care management or treatment.

The issue is not simply whether a diagnosis is clinically valid. For risk adjustment, the policy question is whether diagnoses used for payment are documented through appropriate sources, predict future costs, and are comparable to how diagnoses are captured in the fee-for-service data used to calibrate the model. If MA plans capture diagnoses more intensively than traditional Medicare, risk scores may become less comparable across the two systems, even if many of the individual codes are supported in the record.

Plans that document more diagnoses for enrollees with similar underlying health risk can receive higher payments and, where bids are below benchmarks, larger rebates. Those additional rebate dollars can support lower cost sharing, supplemental benefits, or lower premiums, making the plan more attractive to beneficiaries. As a result, coding intensity can affect not only federal spending but also competition among MA organizations.

Recent CMS policy changes are partly aimed at narrowing the gap between appropriate documentation and coding-driven payment growth. The completed phase-in of V28 reduced the weight of certain diagnoses that were viewed as susceptible to coding variation. CMS also finalized policies excluding diagnoses from unlinked chart review records and certain audio-only encounters from risk-score calculation, with a limited exception for beneficiaries who switch MA organizations.

Accurate diagnosis capture remains essential, particularly for beneficiaries with multiple chronic conditions or significant medical needs. At the same time, plans must increasingly demonstrate that coding practices are supported, encounter-based, and consistent with CMS risk-adjustment rules. The distinction between accurate coding and coding intensity will remain central to MA payment policy, oversight, and debates about the program’s cost to Medicare.

Risk Adjustment Oversight and Compliance

Because diagnosis coding directly affects federal payments, risk adjustment is also a program integrity issue. The program depends on diagnoses that accurately reflect a beneficiary’s health status and are supported by the patient’s documented health record. Oversight mechanisms are intended to ensure that payments are based on valid clinical information rather than unsupported coding.

The primary tool for this purpose is Risk Adjustment Data Validation (RADV). RADV audits review whether diagnoses submitted for risk-adjustment purposes are supported by the appropriate clinical documentation for the patient. If a diagnosis is not reflected in the patient’s records, the associated payment may be treated as an overpayment. Unlike ordinary claims review, RADV focuses on the validity of the diagnosis data used to calculate risk scores rather than whether a particular service should have been paid.

The broader goal is to ensure that risk-adjusted payments reflect actual differences in enrollee health status. To do that, plans must balance two related responsibilities. First, they must capture diagnoses completely enough to reflect the true health status of their members. Second, they must ensure that submitted diagnoses are properly documented and derived from valid sources of care. Risk adjustment is intended to reward accurate documentation, not unsupported diagnosis reporting.

As a result, compliance is a core part of MA operations. Plans are expected to maintain controls around provider education, coding practices, chart review activities, encounter data submission, and audit response. The key question is not simply whether a diagnosis appears somewhere in the record, but whether it was documented, submitted, and supported in a manner consistent with program rules.

Oversight remains central to MA payment policy because risk-adjusted payments represent a large share of Medicare spending. RADV and related compliance requirements are intended to preserve the integrity of the risk-adjustment system by helping ensure that payment differences reflect real differences in health status rather than unsupported coding. These efforts have prompted significant legal and policy debate. Courts have weighed in on the extent to which CMS can extrapolate audit findings and recover large overpayments from MA organizations, and some recent rulings have constrained aspects of CMS’s approach. At the same time, the Office of Inspector General and other oversight bodies continue to publish findings on unsupported diagnoses, chart review practices, and outlier coding patterns.

For plans, this environment creates a dual set of incentives: to fully capture legitimate diagnoses for risk-adjustment purposes, and to invest in compliance infrastructure – coding governance, provider education, internal audits – to mitigate audit and enforcement risk

Implications for Beneficiaries and Market Behavior

For beneficiaries, risk adjustment is mostly invisible. Enrollees generally do not see their risk scores, HCCs, or the diagnosis data that shape plan payments. Instead, they experience the effects indirectly through premiums, supplemental benefits, cost sharing, provider networks, and care-management programs.

The core purpose of risk adjustment is to align payments more closely with expected health care costs. By providing higher payments for beneficiaries with greater medical complexity, the system is intended to reduce incentives for plans to avoid high-cost enrollees and to support access for people with chronic conditions, disabilities, or other significant health care needs.

Risk adjustment also influences benefit design and market competition. Because plan revenue is tied in part to enrollee risk scores, differences in risk-adjusted payments can affect the resources available for supplemental benefits, lower cost sharing, or lower premiums. In this way, coding and risk adjustment help shape the products available to beneficiaries.

At the same time, risk adjustment creates incentives for plans to invest in coding and documentation. Ideally, competition would focus on care management, quality, efficiency, and beneficiary experience. Yet because diagnoses affect payment, plans also have incentives to improve the completeness of diagnosis capture. This creates an ongoing policy challenge of encouraging accurate documentation while limiting payment increases that stem primarily from coding practices rather than underlying health status.

The broader debate over MA payment policy reflects this tension of ensuring adequate payments for sicker beneficiaries, limiting incentives for purely documentation-driven revenue, maintaining manageable administrative complexity, and supporting a competitive MA market. Risk adjustment is intended to make capitated payments more accurate and reduce incentives for favorable selection. At the same time, because diagnoses affect revenue, the system inevitably creates incentives around coding and documentation. Much of MA payment policy is therefore focused on balancing those two objectives. Accurate recognition of clinical complexity married with confidence that payments are supported by documented care are keys to a health Medicare Advantage program.