As Medicare Advantage (MA) member populations continue to grow in today’s extremely competitive, quality-focused and highly regulated market, risk management efforts are becoming more challenging. The growth of MA plans is just one of several business 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 – and as anyone in the business knows, it’s no cakewalk. Risk adjustment coding directly impacts quality of care provided, revenue, the accuracy of reimbursement for providers, and is the #1 predictor for financial resources. So, getting the coding right 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.
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.
To put it very simply, 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 for….
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 for 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.
Example: Application of HCC Model with RAF point values
HCC Coding Challenges
Are you facing HCC coding challenges with intermittent HCC model changes announced from year to year? It is all fairly complex, I know. 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. Some specific challenges include:
How to Improve HCC Coding Accuracy and Efficiency
The good news is that accuracy and efficiency in HCC coding documentation is very possible and it doesn’t have to be a time-consuming and 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:
Despite the challenges you may face while striving to deliver both high quality and 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 realize 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 — the central mission across healthcare stakeholders.
For more information on an automated solution that drives improved coding efficiency and accuracy for better outcomes and reduced costs, click here.