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HCC Coding: How to Improve Risk Adjustment Coding Accuracy & Efficiency

HCC Coding: How to Improve Risk Adjustment Coding Accuracy & Efficiency

By Kate Marta, Associate Vice President, Clinical Training, Oversight, and Innovation

As Medicare Advantage (MA) member populations continue to grow in today’s competitive, quality-focused, and highly regulated market, risk adjustment management efforts are becoming more challenging. The growth of MA plans is just one of several challenges health plans face as they aim to optimize patient care while maintaining high-quality compliance standards. As a health plan, you are responsible for ensuring accuracy in your coding practices. As anyone in the business knows, it’s no cakewalk. Risk adjustment coding directly impacts the quality of care provided, revenue, and accuracy of reimbursement for providers. Getting the coding right and understanding how to reduce HCC gaps is essential. Given that more than 100 million medical records are reviewed each year in the United States, health plans are continually looking for efficient ways to achieve accurate HCC coding in an efficient manner.

In this blog, we’ll cover how to reduce HCC gaps by:

  • Understanding the basics of the Hierarchical Condition Category Risk Adjustment model
  • Identifying ways to better prevent coding gaps
  • Strategies to improve coding efficiency and accuracy

So, what’s best practice? Before we dive in, let’s take a step back and examine the Hierarchical Condition Category (HCC) model for a better understanding of HCC risk adjustment coding.

Back to Basics: What is the Hierarchical Condition Category (HCC) Risk Adjustment model and what does it mean for HCC Coding?

The HCC risk adjustment model is used to calculate risk scores to predict future healthcare costs. More technically put in the “CMS HCC Risk Adjustment Model 2019: Winners and Losers” white paper, CMS “uses risk adjustment models to determine capitated payments made to MA plans and makes periodic updates designed to improve the performance of the models. A key component of the risk adjustment model is the set of HCCs that CMS uses to determine payments to MA plans.” Got it? Good!  Now that we are clear on the major role the CMS-HCC model plays in estimating healthcare spending for MA beneficiaries, let’s get into the role it plays at the health plan level and why it’s so important.

HCC Coding Guidelines

Although the HCC model is not exactly intuitive, it was not designed to complicate the HCC coding process. On the contrary, its intent is to provide an accurate and complete snapshot of each member’s risk profile so as to better understand each member’s health status and predict cost of care. To dig in a little further into how this all works together for the greater good, having a Risk Adjustment Factor (RAF) point value assigned to each eligible MA beneficiary enables compensation for care and affects patient outcomes. This is a win-win for all parties involved – the member, health plan, physician and CMS – ensuring appropriate reimbursement. RAFs must be documented by standards set by CMS. The standards include documentation in a medical record that was based on face-to-face health service encounters between a patient and healthcare provider, coded in accordance with the ICD-10-CM Guidelines for Coding and Reporting, assigned based on dates of service within the data collection period, and from an acceptable RA physician specialty or hospital setting.

HCC Coding Challenges

Are you facing HCC coding challenges with intermittent HCC model changes announced from year to year? Many times, these changes require more work on your part as a health plan. As risk program management efforts become increasingly more challenging to maintain once these updates and changes are announced, you need to focus on arming your program with the appropriate support and stay focused on how to improve medical coding accuracy. Some specific challenges include:

Increase in Membership. According to KFF.org, in 2019, one-third of all Medicare beneficiaries – 22 million people – are enrolled in MA plans, representing a growth of 1.6 million from 2018. Membership has grown steadily by about 1 million each year since 2016. Increased membership translates to an increase in covered lives, and within the MA patient population, there are unique challenges that come along with that. This population has a greater disease burden and tends to need more care – increasing the complexity of care and the cost of treatment. There are also instances of patients never being previously insured, meaning their health status is unknown. This makes it challenging to anticipate costs and correctly determine their HCC risk scores.

Traditional Manual Processes. If you are at all familiar with the traditional process of HCC risk adjustment coding, then you know it is a resource-intensive and inefficient process that can often hinder operations. Coders are left to scour through massive amounts of medical records to assess claims for possible undocumented/incorrect codes to determine more accurate risk scores. This is a time-consuming and costly process that can potentially cause provider abrasion and increases the chances for human error (i.e., missed HCC risk codes) – missteps that can potentially cost a health plan millions of dollars. According to market research firm IDC, companies lose 20 to 30 percent in revenue every year due to inefficiencies associated with the traditional coding process. Additionally, manual processes are likely to become more and more difficult to sustain as coding methodologies continue to get more sophisticated. You need to continually advance your capabilities and ensure you have the proper strategies in place and provider coders ongoing training on how to reduce HCC gaps.

Data, Data and more Data. To ensure accurate reflection of risk scores at both the member and population level, it is essential that you understand and have access to the various data elements that are critical in the healthcare ecosystem. Administrative, clinical and supplemental data informs your risk adjustment accuracy program with pertinent details to enable better documentation of care for patients and to identify potential erroneous disease documentation. By not utilizing multiple data streams, you might find yourself with inaccurate HCC risk adjustment coding, improper compensation for quality of care and increased audit risk.

How to Reduce HCC Gaps and Improve HCC Coding Accuracy

The good news is that accuracy and efficiency in HCC coding documentation is possible and it doesn’t have to be a tedious process for your coding team. This is especially true if you are leveraging sophisticated data analytics to enable higher productivity and accuracy for coding practices to avoid major compliance and revenue shortfalls. Having tools and capabilities that will provide accurate data is a good start, but don’t stop there. Three great ways to improve HCC coding documentation accuracy and efficiency are:

Ensure Audit Preparedness. CMS’ Risk Adjustment Validation audit program exists to identify past and prevent future improper MA payments, which can be costly for health plans. A health plan organization’s biggest priority should be arming its risk adjustment accuracy program with a complete member risk profile to truly understand the member population, help inform care needs. and improve health outcomes. This, in turn, decreases risk exposure. A risk adjustment accuracy program built for success includes access to automated analytics software tools that provide coders with documented clinical evidence of accurate coding between the patient record and claim codes. These tools help significantly reduce the reconciliation process and mitigate risk exposure.

Review Patient Data. Improving patient care starts with identifying chronic illnesses, regulating costs, and identifying accurate HCC risk scores and reimbursements. Using technology such as natural language processing and machine learning to deliver data-driven insights to medical coders can enable the collection and evaluation of large amounts of disparate data and identify risk score-relevant conditions faster than the traditional review process. Leveraging technology in these areas allows you to quickly identify missed HCC conditions and close documentation gaps faster and more accurately, which could mean the difference between receiving or paying out transfer payments.

Automate Manual Processes. Today’s technological advances have made it easier for you to streamline the traditional risk adjustment accuracy process by decreasing time-consuming record reviews. Coding teams can now process patient records on a larger scale with increased accuracy, which improves efficiency. The backbone of an automated process is having it integrated into your coders’ workflow, enabling significant productivity. Health plans that leverage natural language processing technology within their practices can more easily address challenges around the increase in MA enrollment.

Despite the challenges you may face while striving to deliver high-quality, affordable care to your members, there are viable, effective solutions to support the process. One that is proving to be increasingly valuable is the ability to increase accuracy and coder productivity to enable more accurate transfer payments and reimbursements.  An automated data analysis solution leveraging natural language processing can enable this capability while simultaneously assisting you with improving the health of your member population by delivering quality care.

For information on an automation solution that drives coding efficiency and accuracy for better outcomes and cost reduction, click here.