• Hierarchical Condition Category Coding

    What is hierarchical condition category (HCC) coding?

    Hierarchical condition category (HCC) coding is a risk-adjustment model originally designed to estimate future health care costs for patients. The Centers for Medicare & Medicaid Services (CMS) HCC model was initiated in 2004 but is becoming increasingly prevalent as the environment shifts to value-based payment models.

    Hierarchical condition category relies on ICD-10 coding to assign risk scores to patients. Each HCC is mapped to an ICD-10 code. Along with demographic factors (such as age and gender), insurance companies use HCC coding to assign patients a risk adjustment factor (RAF) score. Using algorithms, insurances can use a patient’s RAF score to predict costs. For example, a patient with few serious health conditions could be expected to have average medical costs for a given time. However, a patient with multiple chronic conditions would be expected to have higher health care utilization and costs.

    Family Medicine Practice Hack

    Using Team-based Strategies to Improve Risk Adjustment 

    Watch this Practice Hack video to learn how to use team-based strategies to improve risk adjustment for success in value-based payment. 

    Why is HCC coding important?

    Hierarchical condition category coding helps communicate patient complexity and paint a picture of the whole patient. In addition to helping predict health care resource utilization, RAF scores are used to risk adjust quality and cost metrics. By accounting for difference in patient complexity, quality and cost performance can be more appropriately measured.

    Interested in learning more about HCC?

    Watch the HCC Crash Course: Absorbing the Impact webcast for all you need to know about HCC coding, including practical application in your practice.


    *Example #1: A 68-year-old female patient with type 2 diabetes with no complications, hypertension, and a body mass index (BMI) of 38.2  

    ICD-10 DESCRIPTION RAF
      Demographics 0.323
    E11.9                      Type 2 Diabetes with no Complications
    0.105
    I10 Hypertension 0.000
    Z68.38 BMI of 38.2 0.000
        Total Risk= 0.428

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

    ICD-10 DESCRIPTION RAF
      Demographics 0.323
    E11.42 Type 2 diabetes with diabetic polyneuropathy 0.302
    I10 Hypertension  
    E66.01 & Z68.38 Morbid obesity with a BMI of 38.2
    0.250
    I50.9 Congestive heart failure 0.331
      Disease interaction (DM + CHF) 0.121
        Total Optimized Risk 1.327

    *These are sample patients only, using 2020 CMS HCC model values and 2021 ICD-10 codes. 


     See Also