Five Action Items to Improve HCC Coding Accuracy and Risk Adjustment With Analytics

As enrollment in Medicare Advantage plans increases, healthcare organizations need to be able to anticipate future healthcare financial resources and predict appropriate reimbursement for physicians. The Hierarchical Condition Category (HCC) risk adjustment model is used by CMS to estimate predicted costs for Medicare Advantage beneficiaries, and the results directly impact the reimbursement healthcare organizations receive. The HCC risk adjustment model was originally implemented in 2004, but is becoming more prevalent as value-based payment models gain popularity.

The HCC model assigns a Risk Adjustment Factor (RAF) to each Medicare patient as measurement of probable costs, which is then used to adjust capitation payments for patients enrolled in Medicare Advantage plans. According to the American Academy of Family Physicians, “hierarchical condition category coding helps communicate patient complexity and paint a picture of the whole patient,” helping to appropriately measure quality and cost performance. Consequently, accurate HCC coding and risk adjustment can have a significant impact on healthcare organizations’ financial viability and care delivery.

HCC Model Complexities and Risk Adjustment Use

CMS requires that all qualifying conditions be identified each year by provider organizations. Documentation linked to a non-specific diagnosis, as well as incomplete documentation, negatively affects reimbursement. Healthcare organizations that optimize their EMR, data, analtyics, and education can enable better documentation of care for patients with chronic diseases, leading to more accurate HCC risk adjustment coding, and more appropriate compensation for quality care.

The number of Medicare Advantage beneficiaries has continued to rise over the past ten years, with roughly one-in-three Medicare beneficiaries now enrolled in a Medicare Advantage plan. As with starting any new initiative, HCC coding is not intuitive, but accurate HCC coding is necessary for healthcare organizations in order to receive fair compensation. Below are a few highlights to know about the CMS HCC model complexities and risk adjustment use:

• CMS requires an encounter each calendar year and diagnosis by an APRN, PA or physician.
• Documentation must be accurate and support the diagnosis.
• Some codes have RAF value. Some do not. Increased severity doesn’t usually increase risk adjustment factor (RAF).
• HCC codes are not always intuitive. Physicians may require decision support.
• HCC codes are additive, and some have multipliers.
• Population complexity/severity affects payment in many Medicare contracts.
• RAF is used for benchmarking for quality and safety.
• RAF enables identification and stratification for patient management.

The Impact of Appropriate HCC Coding

Even though the payment model is not intuitive, an organization’s ability to perform well within this model should increase over time. For example, in Figure 1 below, the table shows sample patient data from a 76-year-old female patient with an RAF score of .448. The two options show how different diagnoses change the patient’s risk score, and, as a result, the annual member payment.

In option one, the patient shows a diagnosis of obesity, which has an HCC Risk Score of zero. In option two, the patient shows a diagnosis of morbid obesity with a BMI of 42 and an HCC Risk Score of .273. Similarly, option one shows a diagnosis of asthma and an HCC Risk Score of zero. Option two shows a diagnosis of COPD and a risk score of .328. When a patient has a diagnosis of major depression, if left uncategorized, the diagnosis adds no value. If any category is chosen, such as “single episode” in the case of option two below, this adds a risk score of .395. The two different options show a patient of similar complexity but varying diagnoses, which results in vastly different annual member payments. Option one showed a total RAF score 1.029 and an annual Medical Advantage member payment of $9,000; Option two showed a RAF score of 3.633 and a Medicare Advantage member payment of $32,000 annually. While physicians should not change their diagnoses, it is important to code accurately and take credit, where deserved, for serving a complex population.

Driving Improvement Through Interdisciplinary Workgroups

The example above provides a compelling rationale for organizations to improve their HCC coding accuracy. However, the first step is obtaining accurate data about Medicare populations before trying to make improvements in this space. It may also be beneficial to form a workgroup that is responsible for improving the accuracy of documentation and HCC coding. This workgroup could include members of the analytics team, Accountable Care Organization (ACO) team, clinicians, clinic managers, operations, and medical coders.

Forming the workgroup can help oversee the following five key action items necessary for improving HCC coding accuracy:

1. Having an accurate problem list. Many healthcare organizations have been inputting data in an EMR for years now, resulting in lots of data, and most likely an inaccurate problem list. Ensuring an accurate problem list involves removing duplicative and inactive diagnoses, identifying key areas with discrete data in the EMR, and using a diagnosis preference list to include HCC suffix codes and RAF values as well as prioritize results.
2. Ensuring patients are seen in each calendar year. The first question to ask is, “Can you identify patients with chronic illnesses who have not been seen during the calendar year?” If, so, the next step is then do so, which may be easier said than done. One way to do this at a glance is to build a clinical dashboard hat provides a snapshot of both EMR and claims data that provides a complete picture of patients not yet seen in a calendar year. Once the workgroup can identify these patients, they can match them with both visit and HCC coding gaps. The workgroup should acquire information at the system, region, clinic, or provider level and review with clinic staff regularly (such as on a quarterly basis). One best practice is to frontload visits for these patients early in the year when clinics have capacity.
3. Improving decision support and EMR optimization. Although educating providers is necessary to improve HCC coding accuracy, it’s also important to build appropriate coding into the daily encounter workflow. Some potential strategies include having an ACO identifier flagged in the EMR, decision-support tools that can be activated for select populations, and HCC diagnosis alerts for past codes.
4. Widespread education and communication. Because this is new work and certainly not intuitive, it’s important to educate clinicians along the way. The biggest educational point to drive home to clinicians is not what score they should look for, but the importance of accuracy. Workgroups can educate clinicians on the clinical and financial value of specificity. They can also educate clinic staff about the specifics of the tools and workflows for patient management and reporting, and at a system level, education should center around the importance of appropriate risk adjustment and the impact quantification to justify resource allocation, as well as compliance.
5. Tracking performance and identifying opportunities. The last, and perhaps most important key initiative of the workgroup is to track performance and identify future opportunities for improvement. Measuring results provides the workgroup with compelling data to bring to stakeholders that shows what improvements were made, such as an increase in average RAF score, improvement in key problem list diagnoses, decrease in the number of members without an annual visit, an increase in the percentage of persistent condition diagnoses resolved. Once the workgroup has data to bring to stakeholders, the next step is to identify future opportunities for further improvements. One place to look for these is by reviewing unresolved persistent conditions for specific populations.

Key Lessons

For organizations that are looking to embark on an effort to improve their HCC coding accuracy and risk adjustment, below are four valuable lessons learned:

• The magnitude and presence of coding accuracy opportunities are not evident without data.
• Using data to focus efforts helped find topics that were practice and valuable to end users and improve engagement.
• It is manageable and effective to use the EMR upstream and during encounters.
• Clinic staff want reports to see how they are doing compared to peers.

Fair Reimbursement for Serving Complex Patient Populations

Although HCC coding accuracy and risk adjustment requires changes to the way healthcare organizations are documenting and coding chronic conditions, doing so can help the organization capture more complete diagnoses, resulting in higher and more appropriate reimbursement and improved care delivery for complex patient populations.

With increasing Medicare Advantage numbers, healthcare organizations need to improve coding accuracy to remain financially viable. Creating a workgroup that’s responsible for key action items is crucial to the success of this initiative. They can help ensure the organization has an accurate problem list, chronically ill patients are seen once per calendar year, improve decision-support and EMR optimization, educate clinicians and staff, and track performance of the initiatives to share with stakeholders. They can then look for further opportunities for improvement. If healthcare organizations appropriately document the complexity of their patients, they are eligible for greater CMS revenue that can then be reinvested to better meet the needs of their patient population.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

1. ACOs: Four Ways Technology Contributes to Success
2. Healthcare Data Management: Three Principles of Using Data to Its Full Potential
3. Why Clinical Quality Should Drive Healthcare Business Strategy
4. Linking Clinical and Financial Data: The Key to Real Quality and Cost Outcomes

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