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Understanding the CMS Hierarchical Condition Category (HCC) Coding Model

By AGS Health

February 15, 2023

Risk Adjustment: Insights

Risk adjustment is a statistical method to predict a patient’s possible use of healthcare services and the associated costs. As defined by the Centers for Medicare and Medicaid Services (CMS), risk adjustment predicts the future healthcare expenditures of individuals based on diagnoses and demographics.

Risk adjustment modifies payments to all insurers based on an expectation of what the patient’s care will cost. Risk adjustment provides more accurate payments for Medicare Advantage (MA) organizations. Payments are higher for unhealthy enrollees and lower for healthy enrollees. MA uses the risk factor to adjust the capitated payments the federal government makes to cover the expected medical costs of enrollees.

History of CMS-HCC Model

Traditionally, payments to MA organizations were based on demographics. The CMS HCC model, launched in 2004 and fully implemented by 2007, has become essential as the healthcare payment model shifts to value-based care.

Critical Risk Adjustment Implementation Dates

  • Balanced Budget Act of 1997 - Mandated risk adjustment payment methodology to increase accuracy.
  • Benefits Improvement and Protection Act of 2000 - Established the implementation schedule to achieve 100 percent risk-adjusted payment in 2007.
  • January 2002 – CMS announced a new Risk Adjustment Data Processing System (RAPS).
  • May 12, 2003 – Published final M+C rates for 2004 payment. Announced the final CMS-HCC model, including the institutional and community models. Provided risk adjustment for new enrollee factors.
  • December 8, 2003 – Medicare Prescription Drug, Improvement, and Modernization Act of 2003. Created the MA program to replace the M+C program. Created Medicare drug benefit to begin in 2006. Established bidding methodology for MA organizations and drug plans in 2006.
  • January 16, 2004 - New rate book for 2004 published.
Understanding the CMS Hierarchical Condition Category (HCC) Coding Model

CMS HCC Code Mapping

The CMS HCC model filters ICD-10-CM codes into diagnosis groups and condition categories. Hierarchies or families of conditions are progressively assigned an HCC numeric code, which is translated to a risk adjustment factor (RAF) value. Not all ICD-10-CM codes carry value in risk adjustment models. Diagnoses that are costly to manage from a medical management or prescription drug treatment perspective are more likely to be found in risk adjustment models.

Each year CMS publishes the list of diagnosis codes and the HCC codes that each adjusts to within the model. Approximately 10,000 out of more than 70,000 diagnoses codes map to HCC codes. There are 19 different HCC categories with 86 total HCC codes.

Hierarchies are listed among related condition categories, hence the term HCC. These hierarchies set values based on the severity of illnesses, with more severe diagnoses carrying the overall risk scores for families.

For example, in the diabetes family:

  • Documentation of diabetes alone HCC 19 (diabetes without complications) has a lower risk score than diabetes with chronic complications HCC 18, such as diabetes with neuropathy or diabetic nephropathy.
  • Diabetes with chronic complications HCC 18 has a lower risk score than diabetes with acute complications HCC 17, such as diabetic ketoacidosis or diabetic hypoglycemic coma.

Diagnoses within families or hierarchies are inclusive of one another. In contrast, any additional diagnoses from other families (hierarchies) or stand-alone diagnoses are additive and increase the patient’s overall RAF score.

Calculating the RAF Score

The Risk-Adjustment Factor, known as an RAF score, measures the estimated cost of an individual’s care based on their disease burden and demographic information. The demographic information includes the patient’s: 

  • Age
  • Gender
  • Socioeconomic status
  • Disability status
  • Insurance status (Medicare, Medicaid, or dual eligibility)
  • Institutional status

Each HCC associated with a patient is assigned a relative factor that is averaged with any other HCC code factors and a demographic score. The resulting score is multiplied by a predetermined dollar amount to set the per-member-per-month (PMPM) capitated reimbursement for the following coverage period.

The PMPM is the payment amount a provider receives for a patient enrolled in an MA plan, regardless of the services provided.

An RAF score of 1.00 denotes the patient is expected to use average resources. If the RAF score is above that value, it is considered a high RAF score related to the most complicated patients who need complex care and resources.

Healthier patients have below-average RAF scores, while sicker patients will have higher RAF scores. Scores impact the calculated payment amounts and are calculated annually.

Example: A 68-year-old female patient with type 2 diabetes with diabetic polyneuropathy, morbid obesity with a BMI of 38.2, and congestive heart failure.

ICD-10-CM Code

Description

RAF Score

NA

Demographics (age and gender)

0.323

E11.42

Type 2 diabetes mellitus with diabetic polyneuropathy

0.302

E66.01 & Z68.38

Morbid (severe) obesity due to excess calories & body mass index 38.0-38.9

0.250

I50.9

Heart failure, unspecified (includes congestive heart failure NOS)

0.331

NA

Disease interaction (DM + CHF)

0.121

 

Total Optimized Risk

1.327

Importance of RAF Score

HCC Coding helps to calculate and communicate patient healthcare complexity and portrays a picture of the whole patient. In addition to helping predict healthcare resource needs, RAF scores are used to risk-adjust quality and cost metrics. By accounting for differences in patient complexity, costs can be estimated more accurately. HCC coding improves patients’ quality of care, again supporting the shift to value-based care.

Learn about risk adjustment and HCC coding, including the differences between the CMS and HHS models and when they are applied from our webinar.

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AGS Health is more than a revenue cycle management company—we’re a strategic partner for growth. Our distinctive methodology blends award-winning services with intelligent automation and high-touch customer support to deliver peak end-to-end revenue cycle performance and an empowering patient financial experience.

We employ a team of 12,000 highly trained and college-educated RCM experts who directly support more than 150 customers spanning a variety of care settings and specialties, including nearly 50% of the 20 most prominent U.S. hospitals and 40% of the nation’s 10 largest health systems. Our thoughtfully crafted RCM solutions deliver measurable revenue growth and retention, enabling customers to achieve the revenue to realize their vision.

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