Hierarchical condition category coding for family physicians

Person’s hand on a computer keyboard with electronic medical accounts and codes in a spreadsheet on a computer screen.

Your ICD-10-CM diagnosis codes drive awareness of patient risk level for payers and practices under the Hierarchical Condition Category model, ultimately affecting payment.

What is hierarchical condition category (HCC) coding?

Hierarchical condition category (HCC) coding is a risk-adjustment model originally designed to provide patients with an estimate of future health care costs. The Centers for Medicare & Medicaid Services (CMS) introduced its HCC model in 2004, and more practices have adopted HCC coding as part of a shift 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 each patient a risk adjustment factor (RAF) score, which is then fed into an algorithm to predict costs. For example, a patient in mostly good health could be expected to have average medical costs for a given time, while a patient with multiple chronic conditions would be expected to have higher costs.


Why is HCC coding important?

HCC coding helps paint a picture of the whole patient and the often-complex specifics of their condition. In addition to helping predict usage of resources, physicians can use RAF scores to risk-adjust quality and cost metrics by accounting for differences among each patient's unique circumstance.


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) systems. 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 bottom line. 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 could 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 use more resources.

Team-based strategies for risk adjustment

This Practice Hack video will teach you how to use team-based strategies to improve risk adjustment in value-based payment.


Risk adjustment score examples

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 (age and gender) 0.323
E11.9 Type 2 diabetes mellitus without complications 0.105
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*

ICD-10 DESCRIPTION RAF
  Demographics (age and gender) 0.323
E11.42 Type 2 diabetes mellitus with diabetic polyneuropathy 0.302
I10 Essential (primary) hypertension 0.000
E66.01
and
Z68.38
Morbid (severe) obesity due to excess calories and body mass index (BMI) 38.0–38.9, adult 0.250
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

*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 doesn't 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 factors has been challenging for several reasons, including difficulty capturing data. Some models have begun incorporating data from area-deprivation and social-deprivation indices, which include generalized local information, but not data on individual patients.

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.

  • HCCs are additive, so it's important to code all conditions that coexist at the time of the encounter, or which affect patient care or treatment.

  • Conditions previously treated that no longer exist shouldn't be coded. History codes may be used as secondary codes if the condition or family history impacts care.

  • Documentation must support the diagnoses reported. A good document rule of thumb to remember is MEAT: a diagnosis should be Monitored, Evaluated, Assessed, or Treated.

  • 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.

It’s important to 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

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