On January 30th, CMS released its Final Rule regarding the Medicare Advantage (MA) Risk Adjustment Data Validation (RADV) program. You might be wondering how these new RADV rules will impact your MAO. Here are a few points to consider.
1. Many conditions in the Medicare Advantage population continue to be inappropriately coded
2. CMS has all the data they need to identify populations most likely to have a higher prevalence of these inappropriate codes
3. This is not a random audit. CMS is targeting the populations likely to have non-compliant diagnoses
In other words, plenty of erroneous codes exist, CMS knows where they are, and they are prepared to target them directly. The best thing you can do is to prepare your financial leadership for the expected outcome of any such audit and lay out the case for improving risk mitigation efforts. Read on to learn about the potential financial implications of an audit and some tools that will help you avoid unfavorable audit results.
To illustrate how an audit may play out financially, we will go through an example using a condition with which most everyone in the RADV space should be familiar: Ischemic or Unspecified Stroke, or HCC 100 in the v24 model. Every underlying diagnosis represents what is undoubtedly an acute emergent event that involves hospitalization. What we see in claim data, however, is this code showing up in an office setting (POS 11). We expect this to be a target for CMS, so let’s see what the potential impacts would be.
In this population, you can expect approximately 1,000 occurrences of valid strokes. This is based on an average incidence rate of about one percent per year.
Based on our own internal data, we know it is not uncommon to see over 40 percent of stroke diagnoses inappropriately added to a claim. In turn, we can reasonably expect to see 1,600 different members diagnosed with stroke (1,000 actual and 600 inappropriate).
Cleaning up these diagnoses prior to an audit will have multiple benefits:
1. Fewer erroneous diagnoses in your submissions, reducing the chances that you’ll need to pay for an overstated error due to an unlucky sample
2. Avoiding uncomfortable internal conversations about why the MAO must pay a $1.32MM penalty
3. Lowering your risk of an audit, especially if CMS is using their own algorithms to identify areas of high likelihood for overpayment
Having an ongoing risk mitigation plan to clean up submissions is key to navigate these new changes, and to ensure that you’re strengthening relationships with providers to help avoid errors in coding in the future. Advantmed’s Risk Mitigation Insights (ARMI) solution can help.
Click here to learn more about how we can support your efforts.