Health Language Blog

Hierarchical Condition Categories Part 1: What’s all the Buzz About?

Posted on 07/13/16

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There is quite a bit of discussion around Hierarchical Condition Categories (HCCs) these days. And for good reason: as the risk adjustment model used since 2004 to determine reimbursement for various Medicare plans, the HCC framework is progressively being applied to numerous healthcare reform initiatives. In this two-part series, we break down the basics of HCCs, why they matter and how all healthcare stakeholders should respond to them going forward.

What are Hierarchical Condition Categories?

There are two types of HCC’s:

1) The CMS-HCC model is used by the Center for Medicare and Medicaid Services (CMS) for risk adjustment of the Medicare Advantage Program and addresses a predominately elderly population (65 and over or those otherwise qualifying for Medicare). Within this framework, the CMS-RxHCC is used separately to address Medicare Part D.

2) The HSS-HCC model is maintained by the Department of Health and Human Services to address commercial payer populations and covers all ages.

Both models employ a risk adjustment score to predict future healthcare costs for plan enrollees. According to the CMS website, “risk adjustment allows CMS to pay plans for the risk of the beneficiaries they enroll, instead of an average amount for Medicare beneficiaries. By risk adjusting plan payments, CMS is able to make appropriate and accurate payments for enrollees with differences in expected costs. Risk adjustment is used to adjust bidding and payment based on the health status and demographic characteristics of an enrollee. Risk scores measure individual beneficiaries’ relative risk and risk scores are used to adjust payments for each beneficiary’s expected expenditures. By risk adjusting plan bids, CMS is able to use standardized bids as base payments to plans.”

How is the CMS HCC calculated?

This is a risk adjustment methodology and is determined by CMS using a combination of demographic data and diagnoses (based primarily on ICD-10 codes taken from claims data).

blog_image.pngBased on an average risk score of one, greater risk is represented by a number greater than one and less risk by a number less than one. In addition, the system operates within a hierarchical structure such that the more complex diagnoses absorb and incorporate less complex conditions.

How is patient information captured for submission to CMS?

We will walk through this in steps:

  1. Capturing demographic data is the easy part since this information is fixed and includes such parameters as patient age and address.
  2. Accurately aggregating diagnosis data is trickier since capture of this information relies on a face-to-face encounter and must be done annually. Data represented must be based on an active diagnosis. Providers can consider using the MEAT mnemonic:
    • Being MONITORED (signs/symptoms, disease progression/regression)
    • Being EVALUATED (test review, response to treatment)
    • Being ASSESSED (tests ordered, record review, counseling, discussion)
    • Being TREATED (meds, therapies, other modalities)

Each year, providers must conduct a face-to-face encounter with their patients, and all diagnoses must be documented in the medical record. Only diagnoses meeting the above criteria count towards the final HCC score. For example, if a provider forgets to document a below the knee amputation diagnosis code for a patient, the encounter does not exist for the purposes of HCC calculations.

The bottom line is that clinical documentation matters. The level of reimbursement—and the level of health plan service available—depends on accuracy and specificity of documentation by physicians. Better clinical documentation leads to payment reliability and can make many healthcare challenges—RAC, MAC, ICD-10, CMI and HCC’s—a non-issue.

Tune in next week for Part Two of this series, where we will discuss the importance of consistent documentation to support accurate coding. We'll provide examples of common pitfalls as well as best practices for implementing infrastructures and workflows to improve the outlook on HCCs.

 

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Topics: clinical documentation, HCCs

About the Author

Barbara Antuna & Deborah Szymanski. Dr. Barbara Antuna is a practicing Board Certified Emergency Physician, who practices part-time in the south Denver Metro area. She is also has a subspecialty certification in clinical Informatics via the American Board of Preventive Medicine. Her medical school training was at the University of Colorado, and EM residency at Denver Health. Dr. Antuna joined our content team this year where she is focusing on our Data Normalization solution, as well as providing expertise in regulatory reporting, physician engagement, and Epic EMR. And Deborah Szymanski RN, BSN is an Account Manager and Clinical Specialist at Health Language. Deborah started her healthcare career as a nurse in the Emergency Department. Deborah has spent the last six years working in medical software.