Manage HCC Accuracy and Increase Coding Productivity with Datalyst™

Hierarchical Condition Categories or HCC codes are codes that are linked or mapped to specific clinical diagnosis codes and, when aggregated, represent the complexity of a patient’s health conditions.  When aggregated across a population, this illustrates the complexity of that population’s overall health.  

HCC Coding Accuracy

CMS uses these codes in their prospective, risk-adjusted model to project the expected cost of the patient based on their demographic and overall health risk factors for the following year. When aggregated across a population, a provider organization or ACO can determine what CMS is projecting the cost of care for that population over the next year and, subsequently, the reimbursement for your Medicare Advantage or value-based care plan.  

At the beginning of each year, Medicare resets its risk-adjusted model for patients enrolled in Medicare Advantage or value-based care programs.  This means that each diagnosis code recorded for each patient seen this year will be used to calculate the following year's risk and reimbursement rates.  Failing to accurately and specifically document each patient’s history and chronic conditions with specific diagnosis codes this year could result in a lower risk score next year and a reduction in your reimbursement.  Simply put, this year's reimbursements are based on how specific and accurate your coding was last year.

For any organization currently or planning to participate in one of CMS' value-based care plans such as Medicare Advantage, MSSP, or the ACO REACH, HCC codes are a critical component to deliver better quality, accurately represent a patient and population's health, and ensure your projected reimbursement accurately represents the true health risk of the patients or population you're caring for.  

HCC Re-documentation

Many organizations struggle to answer the question, how can we ensure we are accurately coding each patient based on their condition or disease?

The HCC Scoring module of Datalyst^TM features analytics to help organizations improve HCC accuracy and increase coding productivity. Specifically, the HCC module of Datalyst indicates how well HCC codes are being captured within a given population year over year. Clients can track important HCC metrics (score, potential gaps, recapture rates) at the organization, population, group, provider, and patient levels.

Clients can easily identify the providers and groups with the greatest number of potential HCC gaps, as well as the HCCs that are most commonly missed. The insights available in Datalyst can help organizations plan effective initiatives and evaluate their re-documentation success.

Datalyst also provides actionable patient-level data, including each patient’s HCC gaps and severity changes, as well as pertinent details for each gap, such as the last visit the HCC was coded and the specific diagnosis code that triggered the HCC. This level of detail enables consistent, accurate, and complete documentation.

Interested in knowing more about Koan Health's HCC capabilities? Send us an email and we will share a case study about how one of our clients realized a 36% increase in HCC count per member over two years.