Salud03 febrero, 2023

Understanding the RADV final rule from a medical coder

Medicare Advantage Organizations (MAOs) are in the crosshairs this week after CMS issued the final rule that determines the repayment methodology for errors found from RADV (Risk Adjustment Data Validation) audits. Increased media coverage has surrounded egregious cases of purposely upcoding and other questionable tactics by MAOs for higher reimbursement amounts by CMS.

While fraudulent intentions are not the norm, no MAO executives will be sleeping easy. Even those MAOs that have made efforts with compliance programs to accurately capture their members' health statuses are still found to be lacking in major areas according to previous OIG report findings.

Impacts of the RADV final rule

The final rule was released on January 30, 2023. It addressed three main points: fee for service adjuster (FFSA), extrapolation, and retroactivity. This ruling is a pivotal moment in the risk adjustment space and will impact every MAO.

To summarize the impact of these key components:

  • Fee for Service Error Rate Adjustment - CMS will not apply a FFS adjustment factor in RADV audits.
  • Extrapolation and retroactivity - CMS will not extrapolate RADV audit findings for payment years (PY) 2011-2017 but will begin extrapolation with the PY 2018 RADV Audit. It’s estimated that insurers will get to keep $2 billion by only doing extrapolation from 2018 and beyond.

As a former RADV coding auditor for a major health plan, I've performed the RADV audits, I've reviewed and trended the results from CMS, and I've read and analyzed the OIG reports. MAOs need to be hyper-focused on avoiding large repayment amounts by increasing their validation rates in these regulatory audits. This is accomplished by ensuring better accuracy. While that sounds very simplistic there are several key components that feed into this accuracy of coding.

Four keys to improving the accuracy of risk adjustment coding

1. Provider documentation

Providers need education on documentation. But they can't do this alone. While they shouldn't be experts in coding rules and guidelines, they should have basic knowledge of how coding works. They should have an understanding of the nomenclature in the ICD-10 classification.

One way to address this is to identify validation rates on a provider basis. Targeted education based on this feedback from clinical documentation (CDI) professionals, as these professionals bridge the information gap between provider and coder. To sum this up, providers need feedback from MAOs on audit findings if they expect change.

2. Coder education

The industry accuracy expectation average is 95%, although I don't believe I've ever seen a 95% validation rate in any OIG findings report. What does that one single data point indicate? It points to Official Guidelines for Coding and Reporting not being followed. Poor documentation from providers, inaccurate 'internal coding guidelines' developed by MAOs, and unrealistically high production standards are all significant factors that lead to coding errors.

Additionally, most of the ICD-10 diagnostic coding happens in areas of the healthcare ecosystem where the importance of accurately capturing chronic conditions isn't as much a priority due to how they are reimbursed. Specifically, outpatient providers get reimbursed by fee for service based on CPT coding. The focus is on the procedure that was performed, not on the documentation of all chronic conditions that coexist and affect care.

3. Two-way coding

The need to identify correct codes that can be submitted appropriately as well as identifying incorrect codes for deletion will be more important than ever because codes that are incorrect cannot be validated in a RADV audit. Repayment amounts are based on codes that cannot be validated. This is why two-way coding, that “looking both ways” is so important. Solid compliance programs will not only be adding codes upon review that are appropriate but also looking for deleting unsupported codes that previously were submitted.  

4. Analytics

Having meaningful data allows for baselines, benchmarks, and goals to keep moving forward. It informs population health, cost prediction, areas of risk and required internal and external reporting. It also includes being able to effectively manage large coding projects, track coder accuracy and production. Analytics from effective tools can identify providers that may benefit from or require additional education. It also means identifying diagnosis codes that are most often reported in error so that feedback can be given. Bottom line, being able to harness the power of data to guide decisions in areas of risk identified will increase validation rates.

Resources and tools to support accurate risk adjustment

At the end of the day, the final ruling will translate into increased demand for bandwidth from MAOs. Health plans will need to employ the best coders and auditors who understand all the intricacies of regulatory audits, effective tools to help meet these demands, and most importantly reviewing the results and adjusting their internal coding practices to align with the results of the audits.

The need to successfully perform a regulatory audit with high validation rates cannot be understated. Investing in effective risk adjustment tools that will help shore up key missing components of a solid compliance program should be a top priority for any MAO. Contact the Health Language team to help you navigate the future of risk adjustment.

Explore Resources for Risk Adjustment Audits
Melissa James, Senior Consultant, Health Language
Senior Consultant, Health Language
Health Language
Risk Adjustment
Enable accurate and efficient HCC risk coding for Medicare Advantage plans with an intuitive workbench that leverages AI and clinical NLP to support clinical validation.
Back To Top