Importance of MA RADV program
For those managing Medicare Advantage (MA) products or preparing to offer products on the health insurance exchanges, the mention of Risk Adjustment Data Validation (RADV) audits often elicits a palpable sense of anxiety.
It’s a term that calls for considering potential challenges to their plan and the timing of the eagerly anticipated notification if a plan is indeed selected for a RADV.
In the context of risk adjustment, it grapples with questions about the necessary resources for the annual audit, which members might be selected, and the financial stakes involved.
In fact as per the new final rule CMS anticipates recovering approximately $5 billion in risk adjustment overpayments from MA organizations (MAOs)
These concerns are undoubtedly valid if a plan is indeed selected for a RADV. However, this article aims to offer a fresh perspective and practical strategies for preparation. By embracing certain behaviors and shifting how they view RADV preparation, plans can significantly improve their ability to manage and mitigate these challenges.
Before delving into the intricacies of RADV, it is essential to understand the origins of the RADV program and its performance within the current regulatory framework.
MA RADV Background – How it works
CMS adjusts Medicare Advantage (MA) payments based on enrollee health and demographics. Sicker enrollees lead to higher payments, discouraging MAOs from only enrolling healthier individuals. The CMS Hierarchical Condition Category (HCC) model groups 10,000 diagnosis codes into about 90 HCCs. These HCCs contribute to enrollees’ risk scores, but CMS doesn’t validate diagnoses before payment, risking overpayments. CMS’s RADV program audits MAOs annually to recover such overpayments.
Current State of RADV:
The current MA RADV program is the culmination of years of internal review, information requests, and market testing by CMS.
The following aspects point to the present state of RADV:
RADV Audits: Primary Corrective Tool of CMS
CMS uses RADV audits as the main corrective tool to address overpayments to MAOs lacking sufficient medical record documentation for risk adjustment diagnosis codes. RADV audits are crucial for CMS to correct overpayments by MAOs when medical records do not adequately support reported diagnosis codes, a key tool in risk adjustment coding.
Targeted Approach for RADV Audits:
CMS shifts from random to targeted RADV audits, focusing on contracts and diagnoses identified as posing the highest risk of improper payments, aligning with GAO recommendations. CMS now targets RADV audits on high-risk contracts and diagnoses, which are recommended by the GAO (Government Accountability Office) to reduce improper payments effectively, departing from random selection.
Extrapolation Methodology Introduced:
The final rule allows CMS to audit MA claims from PY 2018 onward using extrapolation, aiming to identify and correct billing errors across all claims, enhancing the audit process. CMS introduces extrapolation for auditing MA claims from 2018 onward, improving the identification of billing errors across all claims to recover significant overpayments.
Dual Recognition of Audits:
Both CMS and OIG audits are recognized under the final rule, enforcing audits from PY 2018 onward with authority to recover improper payments, emphasizing efficiency and consistency. Audits by both CMS and OIG are valid under the final rule, ensuring recovery of improper payments since 2018 with enhanced enforcement and recovery measures.
This strategic targeting approach aims to enhance efficiency in recovering improper payments, reduce burdens on MAOs with low rates of improper payments, and maximize overall recoveries.
Recent Misconceptions Surrounding RADV Audits
Impact on Plan Liabilities and Provider Oversight: Uncertainty over audit methods (random vs. stratified sampling) in the final rule may increase health plan oversight to ensure accurate coding, affecting provider burdens and Medicare Advantage plan premiums. Smaller plans may struggle with RADV compliance costs, influencing their bidding strategies and premium adjustments.
Misconception 1: RADV Audit Findings are Always Accurate and Beyond Dispute: Not necessarily. Errors or disputes can occur in audit findings. Plans can appeal if inaccuracies are found. CMS will use any statistically valid method for extrapolation, applying findings to all claims from PY 2018 onwards. Overpayments for PYs 2011-2017 will be collected without extrapolation.
Misconception 2: The Criteria and Methods Used in RADV Audits are Consistent and Predictable: No, RADV audit focus and methodology can change with policy updates, CMS guidelines, or emerging issues in risk adjustment practices. CMS rejected proposals to introduce an FFS Adjuster and found no systematic bias in MA risk scores from Medicare FFS data. MAOs must comply with existing documentation standards during RADV audits starting from PY 2018.
Misconception 3: The primary goal of RADV audits is to impose financial penalties on Medicare Advantage organizations: This is not entirely true. CMS focuses on accuracy, requiring MAOs to handle the remittance of extrapolated recovery amounts from RADV audits. The final rule amends 42 C.F.R. § 422.310(e), mandating MAOs return improper payments identified by RADV audits. For PYs 2011-2017, CMS recovers at the enrollee level. From PY 2018, repayment methods remain unclear, possibly using MARx offsets. MAOs must refund overpayments only if diagnoses lack evidence. The main goal is ensuring risk adjustment data accuracy to maintain Medicare’s integrity.
RADV Audit Trigger Points
RADV findings can arise due to various factors, including:
- Missing components of MEAT documentation
- The claim was sent to the wrong managing company.
- Chronic diagnosis recaptured but never clinically present.
- The provider is not paneled with the insurance company.
- The service may have already been rendered.
- The company may have lost the claim, and it expired.
- Services were rendered at the wrong location.
- The patient has an out-of-state insurance plan
Improve Audit Readiness
Enhancing Coding Accuracy, Specificity, and Comprehensiveness
Use advanced analytics, AI, and natural language processing to improve coding quality. Regular training for clinical coders on high-risk diagnosis codes is essential.
Deploy Intelligent Smart RA Analytics
Retrospective analytics verify chronic conditions and identify erroneous codes. Prospective analytics support early intervention and address CMS concerns about unsupported diagnoses.
Foster Collaboration among Providers
MA plans should ensure provider documentation meets HCC standards and reflects patient evaluations. Communicate diagnoses from HRAs to primary care physicians for proper integration into treatment plans.
RADV Phased Activity Approach for Compliant-ROI
The RADV audit validates diagnosis codes via medical records, ensuring accurate risk-adjusted payments. CMS audits 30 plans annually, reviewing 201 members each. Plans must provide well-formatted records to support reported HCCs, requiring meticulous planning and coding expertise.
Four Phases of RADV Activity
- Preparation Phase: Plan and organize resources & Evaluate and contract third-party vendors
- Operational Phase: Execute coding and documentation tasks by following up with provider networks
- Submission Phase: Label & select the most suitable records & submit them to CMS.
- Post-RADV Activity Phase: Review and learn from the audit process.
The post-RADV activity phase is important because the appeal process can potentially overturn any failures. An after-action review may benefit future audits, and the medical record review results may be used for provider education purposes.
Let AI Help You Stay Confident amidst RADV Turbulence
Leveraging advanced AI-powered RA solutions is vital for managing RADV audit demands under CMS deadlines.AI tools streamline time management, identify precise HCC details, and ensure audit readiness, potentially averting fines.
Accurate documentation of substantiated HCCs is crucial for verifying payments. CMS emphasizes insurer accountability in the final rule for Risk Adjustment Data Validation.
Tools like RAAPID’s Clinical NLP enhance chase list prioritization and HCC coding efficiency, ensuring precise value-based reimbursements.
Conclusion
Health plans must decide between a passive “wait and see” approach or proactive steps to tackle RADV challenges. CMS’s 2023 final rule replaces FFSA with new standards for extrapolating overpayments from insurers, introducing retroactive rulemaking. Uncertainty surrounds CMS’s compliance with permissible retroactive rulemaking, based on precedent and best practices from HHS. Given the volatile MA regulatory landscape, proactive management of RADV audit challenges is advised.
