Every value-based care (VBC) provider benefit from HCC coding. But from documentation errors to outdated manual processes, many are still not optimizing their coding – and missing out on vital funding.
This guide explains how HCC coding is defined – and explores exactly how the processes influence Medicare reimbursement.
HCC coding is a risk adjustment process that captures individual patients’ medical requirements – and enables insurers to accurately predict their future care costs. In combination with a patient’s demographic information, HCC coding is used to produce a risk adjustment factor (RAF) score and ultimately determine how much the provider should be reimbursed for Medicare Advantage patients.
While primarily intended for risk adjustment, HCC coding also plays a role in value-based care. It enables healthcare providers to capture accurate data about their patient’s diagnoses and conditions, ensuring they can provide the right treatment solutions and claim appropriate reimbursements.
But how exactly does it do that?
Hierarchical condition categories (HCC) classify medical diagnoses into a smaller selection of codes. Numerous ICD-10 codes correlate to a single HCC code; for example, Heath failure, unspecific (150.22), Chronic systolic heart failure (150.22), and Chronic diastolic health failure (150.32) are all recorded as HCC code 85.
There are currently 115 HCC codes that map onto 7,700 ICD codes – enabling a vastly complex set of medical diagnoses to parsed more easily and turned into clear risk scores.
The CMS risk adjustment model involves five key steps to determine reimbursement rates:
Each patient’s medical conditions are coded using ICD-10 codes, which map to specific HCC categories. Only the most severe condition within a related category is used.
Each HCC category has a risk factor weight assigned by the Centers for Medicare & Medicaid Services (CMS). A patient’s total RAF score is determined by adding the weights of all applicable HCCs.
RAF scores also consider:
These demographic factors contribute baseline risk scores before adding HCC conditions.
If a patient has multiple conditions within the same category, only the highest-weighted condition is counted. For example: If a patient has both “Diabetes without complications” (low weight) and “Diabetes with complications” (higher weight), only the more severe diagnosis contributes to the score.
The base demographic RAF score + HCC RAF scores = Total RAF Score. This score is multiplied by a benchmark dollar amount to determine the reimbursement rate for a patient’s care.
The foundation of accurate HCC coding is comprehensive documentation. Providers must ensure that all chronic conditions are recorded annually, even if they were previously diagnosed. Using specific ICD-10 codes rather than generic ones prevents undercoding and ensures that risk scores reflect the patient’s actual health status.
HCC coding relies on a complete view of a patient’s medical history. A centralized EHR system helps consolidate patient data, ensuring that all encounters, diagnoses, and treatments are accessible. This minimizes duplicate records and prevents missing or incomplete coding due to fragmented documentation.
CMS requires that all reported diagnoses meet the MEAT criteria, ensuring conditions are actively managed. For every coded diagnosis, providers should:
AI-driven HCC coding tools like HCC Assistant help identify missed diagnoses, flag potential coding errors, and suggest appropriate HCC codes based on clinical documentation. Automated alerts within EHRs can remind providers to recapture chronic conditions or suggest higher-weighted diagnoses when applicable.
By implementing these strategies, providers can improve coding accuracy, enhance patient care, and optimize reimbursement under risk-adjusted payment models.
While this guide explains the basics of HC coding, many providers still struggle to fit coding within their physicians’ workflows – and risk missing out on vital Medicare funding as a result.
HCC Assistant removes that burden from physicians and helps the average provider increase their RAF scores by 35%.
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