by Barbara Antuna & Deborah Szymanski via Health Language May 4, 2017
Published on May 23, 2017
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).
Based 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:
- Capturing demographic data is the easy part since this information is fixed and includes such parameters as patient age and address.
- 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.
PhoenixMed Comment: In the hospital, utilization review specialists (URS) generally provide the MEAT for the payer. The more frequent the interactions between the URS and the payer, the greater the chance that all MEAT information will be pushed by the URS and captured and used for reimbursing the MA plan. This is the primary reason why payers prefer telephonic reviews. But a better and more productive option is providing the payer a direct portal into the hospital EMR and allow them to pull needed information from the record. Its a win-win situation....the time spent by the URS regurgitating medical record info to the payer is significantly reduced and the payer is able to target specific information they need to generate a HCC score.