The Importance of Medical Risk Adjuster Credential (CRC)

The shift toward providing more specific and complete documentation of all medical problems on an annual basis is a challenge for many practices. This is particularly problematic in the outpatient setting, which typically has less administrative infrastructure to support adequate coding. 

However, aligning current payment-focused billing and documentation practices with evolving risk-adjustment-focused coding practices will only become more important for all ambulatory settings in the future. 

What’s the solution? Hiring someone with a Medical Risk Adjuster Certification (CRC) is a good place to start.

 

What Medical Risk Adjusters Do

Risk Adjusters assign the correct diagnosis (ICD-10-CM) codes. In a Medical Risk Adjuster certification course, students learn to identify and communicate documentation deficiencies to providers in order to improve accurate risk and audit adjustment coding.

HCC stands for “Hierarchical Condition Category.” It is a risk-adjustment model created by CMS in 2004 and was designed to estimate future health care costs for patients. HCC relies on ICD-10 coding to assign risk scores to patients. Each HCC is mapped to an ICD-10 code. 

Along with demographic factors (such as age and gender), insurance companies use HCC coding to assign patients a risk adjustment factor (RAF) score. Using algorithms, insurances use the patient’s RAF score to predict future costs. Hierarchical condition category coding helps communicate patient complexity and paint a picture of the whole patient. 

 

Deriving HCCs from RAFs

In addition to helping predict health care resource utilization, RAF scores are used to risk adjust quality and cost metrics. CMS calculates an RAF score for each individual beneficiary and provides this information to each payer quarterly. 

Deriving these scores from HCCs is relatively straightforward. Adding the weights together produces a risk score for that beneficiary. Accurate coding drives accurate risk adjustment, which will increasingly affect the providers’ bottom line. 

In a Medical Risk Adjuster certification course, coders learn that risk adjustment greatly affects physicians’ reimbursement. Students will learn about HCCs and gain an understanding of how CMS uses them to calculate expenditure benchmarks for providers to earn shared savings and avoid shared loss. This understanding will become increasingly relevant to the operational viability of independent practice owners and health systems as well. The ultimate goal is to improve patient outcomes at reduced costs and to make providers accountable for them.

 

Coding Accuracy Is Important

In addition, it is no longer enough to correctly code the patient’s diagnosis. The plan of care must support the diagnosis for each condition listed, and the condition must be reestablished, along with the appropriate care plan, each year. In other words, the previous coding resets to zero if not reestablished. 

It may like that the coding for amputation would carry forward from year to year, but that is not the case. Each chronic medical problem must be coded and documented annually to be incorporated by CMS into an annual risk score recalculations.

The CMS-HCC risk-adjustment model was designed to most accurately predict spending at the group level, not the individual beneficiary level. Thus, the expenditure predicted for an individual beneficiary is likely to be less accurate than the expenditure predicted for a group of beneficiaries. It is more helpful, then, to look at risk scores at the practice level. 

 

Managing Risk Effectively

To manage risk effectively, a practice should know its risk score for each insurer with which it has a value-based contract. If your practice doesn’t have this data, it is important for the coder to ask for it. 

For example, a patient with few serious health conditions could be expected to have average medical costs for a given time. However, a patient with multiple chronic conditions would be expected to have higher health care utilization and costs. See the below example:

 

HCCs are derived from ICD-10 codes via retrospective review of claims data. ICD-10 codes are factored into the algorithm regardless of the:

 

• Site of service (inpatient or outpatient)
• Provider type (physician or extender)
• Order of diagnosis (primary or secondary)

An ICD-10 code maps to exactly one HCC, but not all ICD codes map to an HCC. The CMS-HCC model focuses on chronic health conditions likely to affect long-term health expenditures and purposefully excludes non-diagnostic diagnoses (e.g., a diagnosis of abdominal pain), clinically insignificant diagnoses (e.g., a sprain), or diagnoses that are definitively treated (e.g., acute appendicitis).

HCCs are called hierarchical because, for some disease states such as diabetes, multiple HCCs capture differing severity of illness. Within an HCC grouping, a patient is assigned only the HCC that corresponds to the most severe manifestation documented. For example, if review of a beneficiary’s claims data finds ICD codes mapping to both HCC 17, Diabetes with acute complications (e.g., ICD-10 code E0811), and HCC 19, Diabetes without complication (e.g., ICD-10 code E089), the model would assign only HCC 17, because it is the more severe manifestation of diabetes.

 

 

Managing Risk Moving Forward

Most physicians are aware of the various risk models however, many physicians are unaware of how critical accurate coding is under these models to characterize risk, enhance shared savings, and provide patient-centered care. Insurance companies are increasingly paying physicians based on how their system performs in value-based care. Insurance plans have leveraged the HCC methodology with significant financial success. 

Due to differences in resource use between groups of Medicare beneficiaries and the inability of a single statistical model to predict expenses for all groups, CMS employs several different versions of the CMS-HCC model. Risk for the majority of Medicare beneficiaries is calculated using the “community” model.

Failing to adequately capture a patient’s risk through documentation and coding may lead to an inaccurately low level of attributed risk and eventually to reduced reimbursements.