Using HCC coders alone is a costly approach to improving HCC capture and re-capture rates, which is why many organizations turn to technology to identify encounters that are under-coded or incorrectly coded. There are several different approaches that coding automation companies take to target this problem.
At RCxRules, we’re often asked about the difference between our HCC Coding Engine and Natural Language Processing, or NLP, for medical coding. Our software and NLP solutions both use technology to narrow down the list of encounters that warrant full coder review, but there are several key differences.
What is NLP Technology?
NLP for medical coding typically takes place prospectively, meaning before the patient visit, or retrospectively, meaning after the initial claim has been submitted to the payer. It focuses on the clinical notes and select clinical data points from EMRs and uses technology to identify keywords and phrases that indicate diagnosis codes likely related to an HCC condition. While this approach is certainly better than relying on a fully manual process, it still requires significant manual work.
Potential HCC codes are often referred to as suspect codes. Presenting suspect codes to a provider via a pre-visit planning process can be tricky since providers are burdened with many other administrative tasks. Adding one more task is problematic in and of itself, but when the suspect codes presented are inaccurate the process becomes untenable.
NLP is also often employed to handle retrospective reviews. As we’ve detailed in a previous blog post, retrospective reviews add time and complexity to the process of getting correct information to the payer.
What Are the Benefits of the HCC Coding Engine?
The RCxRules HCC Coding Engine works as part of a concurrent review process. The software makes sure the correct coding goes out on the initial claim. To do this, the HCC Coding Engine integrates into an organization’s existing workflow. Clinical encounters are processed by the rules engine as they move from the EMR to the revenue cycle process. Any encounter that does not have an HCC coding gap is immediately processed.
This rules-based approach to HCC coding has a very high accuracy rate. The software uses filtering logic to help prioritize and target the highest value encounters, so billing and coding teams can maximize efficiency and impact. In fact, coders can act on 30-40% of the encounters the HCC Coding Engine identifies. Once the coder makes the change, it is integrated directly back into the RCM system.
The HCC Coding Engine has a robust set of HCC coding best practice rules, making the solution uniquely able to identify coding issues that are not directly related to the clinical documentation, but rather related to the payer's ICD-10 coding guidelines. These coding rules have a very high impact rate for our customers.
Set up a meeting to learn how RCxRules can help your organization overcome these challenges and more to improve your HCC coding accuracy and processes.
