• 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 and is becoming increasingly prevalent as the environment shifts to value-based payment models.

    HCC coding relies on ICD-10-CM coding to assign risk scores to patients. Each HCC is mapped to an ICD-10-CM 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, insurance companies 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.

    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 differences in patient complexity, quality and cost performance can be more appropriately measured.

    Risk Adjustment and Value-Based Payment

    Risk adjustment can play an important role in payment, and this is particularly true in value-based payment (VBP). VBP arrangements use a practice’s performance on cost and quality metrics to determine revenue, which means risk adjustment can have a direct impact on a practice’s revenue. When risk scores do not accurately reflect patient complexity, it may appear patients had higher costs and/or lower quality outcomes than would be expected. In certain payment models, this may cause a practice to fall below quality and cost performance targets and potentially miss out on the opportunity for shared savings.

    In other models, such as capitation, a practice’s payment rate may be based on a patient or practice’s average risk score. For example, in Primary Care First, the population-based payment (PBP) is calculated using the average RAF of the practice’s attributed beneficiaries. Practices with more complex patients, based on RAF scores, receive a higher PBP as it is expected their patients will require more resources and have higher utilization.

    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. 

    Examples of Risk Adjustment Scoring

    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* 

      Demographics (age and gender) 0.323
    E11.9                      Type 2 diabetes mellitus without complications
    I10 Essential (primary) hypertension 0.000
    Z68.38 Body mass index (BMI) 38.0-38.9, adult 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*

      Demographics (age and gender) 0.323
    E11.42 Type 2 diabetes mellitus with diabetic polyneuropathy 0.302
    I10 Essential (primary) hypertension  
    E66.01 & Z68.38 Morbid (severe) obesity due to excess calories and body mass index (BMI) 38.0-38.9
    I50.9 Heart failure, unspecified (includes congestive heart failure not otherwise specified) 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-CM codes. 

    Other Types of Risk Adjustment

    A common critique of the HCC model is that it does not account for other factors that impact a patient’s health and well-being, such as health-related social needs. Developing a risk adjustment model that adjusts for social risk has been challenging for several reasons, including difficulty capturing data. Some models have begun incorporating area deprivation index or social deprivation index data. While these indices include data at the local level, they do not include data at the individual-patient level.

    Z Codes

    One option to collect individual-level data is with Z codes. Z codes are ICD-10-CM diagnosis codes that capture factors influencing a patient’s health. A subset of Z codes (Z55-Z65) is designed to capture potential health hazards related to socioeconomic and psychosocial circumstances. Whether and how Z codes will interact with risk adjustment models is yet to be determined. Z codes do not currently have HCC values associated with them. However, some payers have begun requiring practices to report Z codes. 

    Social determinants of health Z codes are included in the following Z code categories: 

    • Z55 – Problems related to education and literacy
    • Z56 – Problems related to employment and unemployment
    • Z57 – Occupational exposure to risk factors
    • Z58 – Problems related to physical environment
    • Z59 – Problems related to housing and economic circumstances
    • Z60 – Problems related to social environment
    • Z62 – Problems related to upbringing
    • Z63 – Other problems related to primary support group, including family circumstances
    • Z64 – Problems related to certain psychosocial circumstances
    • Z65 – Problems related to other psychosocial circumstances

    Z codes Z55-Z65 cannot be reported as the primary diagnosis.

    Z codes can be based on self-reported data and/or information. The information must be signed off on and incorporated into the medical record by the physician or clinician. 

    Reminders for HCC coding

    • Risk adjustment scores reset every year. Practices need to report active diagnoses annually, even chronic conditions. The annual wellness visit is a good opportunity to capture all appropriate diagnoses. Preventive screenings, such as screening of risk factors for depression, aid in identifying additional diagnoses that contribute to a patient’s risk.
    • HCCs are additive, so it is important to code all conditions that coexist at the time of the encounter or affect patient care or treatment.
    • Conditions that were previously treated and no longer exist should not be coded. History codes may be used as secondary codes if the condition or family history impacts current care or influences treatment.
    • Documentation must support the diagnoses reported. A good rule of thumb is to document to the MEAT principles: a diagnosis should be monitored, evaluated, assessed, or treated (MEAT). Diagnoses that are not supported by documentation will not be upheld in the event of an audit. Coding should comply with the ICD-10-CM coding guidelines.
    • The medical record must contain a legible signature with credentials.
    • Code to the highest level of specificity and ensure the diagnoses are properly sequenced on the claim. Some things to consider when selecting the appropriate diagnosis code:
      • Type and underlying cause (e.g., diabetes type 1 or 2, due to underlying condition, postprocedural or due to genetic defects, etc.)
      • Control status
      • Severity
      • Site, location, or laterality
      • Associated co-morbid conditions
      • Substance use/exposure

    HCC Resources from FPM Journal