The CMS-HCC model has two components, the hierarchy, and the condition category.
EDITOR’S NOTE: Colleen Deighan will be conducting a Talk Ten Tuesdays Listener Survey on CDI today.
Hierarchical Condition Categories or HCCs have been used by the Center for Medicare and Medicaid Services (CMS) as part of a risk-adjustment payment model for the Medicare Advantage program in since 2004. The CMS-HCC model is the most known model and is a prospective model using the current year’s data to predict (and pay) for expected medical cost in the following year. In addition to the CMS model there is a Health and Human Services (HHS) model, it came after the passage of the Affordable Care Act (ACA) for use in commercial payer health plans and is also used by some state Medicaid programs. The HHS model includes all ages and is a concurrent model using this year’s data to predict (and pay) for expected medical costs for the current year.
The CMS-HCC model has two components, the hierarchy, and the condition category. The hierarchies are the compilation of all the condition categories. The hierarchy determines payment and consists of related conditions or diseases with similar costs based on severity and expected use of resources. A patient can have multiple HCC categories assigned to them, but within an HCC category only the highest and most severe condition in the hierarchy will be assigned an HCC.
A Risk Adjustment Factor score, or RAF score, is assigned to each beneficiary based on their disease burden and demographic factors such as their age, gender and whether they live in the community or in an institution such as a skilled nursing facility. In concept, the higher the RAF score the sicker the patient. A good example of the HCC hierarchy is the diagnosis of chronic kidney disease (CKD). In the current CMS-HCC model CKD unspecified is not assigned an HCC, CKD stage 3 is assigned to HCC category 138 with a 0.069 RAF score, CKD stage 4, stage 5 and end stage renal disease are assigned to HCC category 137 and 136 and have an RAF score of 0.289. Acute renal failure is assigned to HCC category 135 with a RAF score of 0.435. Additionally, HCC category 134 consists of ICD-10-CM codes reflecting the need for dialysis and is assigned also assigned a RAF score of 0.435. In the CKD hierarchy then only the most severe condition would be reported as an HCC. This example supports the need for complete and accurate documentation and code assignment to ensure the predictive cost aligns with disease acuity.
With the aged Medicare Advantage population disease progression and new conditions are expected to develop, the CMS-HCC methodology requires providers to capture each beneficiary’s HCCs at least once annually. The HCC diagnoses must be captured in a face-to-face visit by a physician or non-physician practitioner (NPP), must be appropriately documented in the medical record and supporting clinical evidence for all diagnoses must be documented. The MEAT criteria are used for supporting HCC diagnosis capture. MEAT stands for Monitor, Evaluate, Assess or Treat; documenting one element of MEAT criteria support compliant HCC coding and reporting.
Why do HCCs matter and why are more and more organizations focusing their efforts on HCC capture? The Medicare Advantage enrollment has more than doubled in the past decade. CMS national data for 2019 shows 42 percent of Medicare Beneficiaries are participating in a Medicare Advantage plan…in 2009 that was 28 percent. That means Medicare Advantage patients are and will continue to be a growing part of a healthcare organizations’ payer mix going forward. Expanding CDI efforts into the outpatient setting with a focus on HCCs is essential for risk adjustment models.
For coding and documentation professionals this is an area where you can where find your passion, expand your knowledge, and continue to evolve your skills and value!
Programming Note: Listen to Colleen Deighan report this story live today during a special, 60-minute edition of Talk Ten Tuesdays, 10 Eastern.
Resources:
AHIMA Outpatient Clinical Documentation (CDI) Toolkit available at: https://bok.ahima.org
2021 ACDIS Outpatient Pocket Guide
State MA participation available at Public Use File | CMS
EDITOR’S NOTE: The author of this article used artificial intelligence (AI)-assisted tools in its composition, but all content, analysis, and conclusions were based on the
Creating industry guidance is a challenging task. It is often difficult to remove ambiguity that could lead to unintended interpretations. I think the contributors to
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