In April 2021, the Office of the Inspector General (OIG) initiated an inquiry into a notable Medicare Advantage plan regarding its purported failure to accurately submit specific diagnosis codes to CMS in accordance with federal regulations. Upon examining the risk scores for a subset of 200 beneficiaries, the OIG concluded that 1,322 (86.7%) out of 1,525 Hierarchical Condition Categories (HCCs) had verifiable supporting documentation, while 203 did not. Additionally, it was revealed that 37 HCCs were either misclassified or overlooked.
Is your organization prepared to address these changes?
This blog discusses the sweeping changes in the risk adjustment landscape, central to determining RAF scoreability. RAFs have a direct correlation to payment aspects and patient outcomes for all stakeholders involved.
Understanding RAF Scores – The Whys & Hows
Health plans receive CMS payments adjusted by a risk adjustment factor (RAF) system to ensure fair compensation for healthcare services and population health management. These payments, calculated using a complex formula, consider location, actuarial adjustments, and patient health status. RAF Medicare scores increase with more HCC conditions, but hierarchy logic ensures the score reflects only the most severe condition. Medicare and Medicaid risk adjustment modifies capitated payments for beneficiaries, requiring providers to document all relevant HCC conditions annually.
RAF Calculation – One Score, Many Dependencies
CMS calculates a “risk adjustment factor” (RAF) score for each beneficiary, distributing this data to relevant entities (ACOs, PPOs, MAOs) on a quarterly basis. These scores are derived from Hierarchical Condition Categories (HCCs), with each demographic adjustment and HCC assigned a specific score or weight within the model. The sum of these weights determines the beneficiary’s risk score, normalized to a base value of 1.0. Typically, risk scores range from 0.9 to 1.7, with scores below 1.0 indicating relatively healthier beneficiaries.
Annually, CMS publishes a “denominator” to convert risk scores into monetary values; for example, in 2014, this denominator was $9,050. Multiplying a risk score by this figure estimates a beneficiary’s annual healthcare expenditure, while multiplying an HCC’s weight by the denominator estimates its marginal contribution to overall costs.
The CMS-HCC risk adjustment model is primarily designed to predict spending at the group level rather than the individual beneficiary level. Therefore, predictions of individual expenditures may be less accurate compared to those for a group of beneficiaries.
It is more effective to evaluate risk scores at the practice level. To manage risk efficiently, practices should be aware of their risk scores for each insurer with whom they have a value-based contract. If your practice lacks this data, it is advisable to request it.
The Importance of Accurate RAF Scoring
While maintaining compliance with CMS is a key external driver for ensuring risk adjustment accuracy, there are significant internal factors to consider as well. For organizational stakeholders, a primary concern is securing accurate reimbursement for the treatments provided, based on the patient’s individual risk profile. Under-documenting a patient’s risk score can result in lower reimbursement than what is actually owed by CMS.
To illustrate the impact of this at scale, consider the following data: The average RAF Score is 1.0, with higher scores indicating sicker or higher-risk patients and lower scores indicating healthier ones. Nationally, CMS reimbursement increases by approximately 10% for each 0.1 increase in the Risk Adjustment Factor score. Given that the national average reimbursement for a non-MA patient is around $9,000, each tenth of a point in a patient’s score could result in a $900 shift per patient.
Inaccurate scores, therefore, can lead to significant over- or under-reimbursements from CMS, creating a range of issues that will need to be addressed.
Thus, maintaining high-quality risk adjustment coding and conducting regular internal audits and reporting are essential for the financial health of payers and risk-bearing providers.
Ways of Optimizing RAF Scores
Fully embracing the entire continuum of care and the essential steps that collectively generate value is crucial, particularly in the context of value-based care and risk adjustment. Opportunities to augment risk capture and minimize provider effort can be achieved by channeling the HCC capture efforts through a multidisciplinary approach mentioned below:
- Suspecting: Enhance data quality in EHR and analytics tools to identify chronic condition documentation gaps, such as missing diabetes codes for insulin-treated patients, improving risk capture accuracy.
- Patient Engagement: Extend care beyond routine appointments by leveraging annual wellness visits, telehealth, specialty care, and new CMS-HCC Version 28 opportunities to address chronic conditions effectively.
- Pre-Service Clinical: Integrate Clinical Documentation Improvement (CDI) expertise with RN pre-charting to reduce time spent searching charts, allowing providers to focus on preparing for critical conditions.
- Point-of-Care: Use an efficient, integrated EHR system to support provider workflows and address chronic conditions with minimal disruption and streamlined processes.
- Post-Service: Utilize CDI and analytics insights to correct coding/documentation errors and address missed targets, steering teams back on track before goals become unachievable.
- Retrospective: Continuously improve risk capture through audits, update outdated education, and use analytic insights to identify inefficiencies, monitor KPIs, and adjust trends.
RAF Matters to Patient Care
Preventive Care and Chronic Disease Management
HCC RAF scoring incentivizes healthcare providers to thoroughly document and diagnose chronic conditions, ensuring they receive appropriate reimbursements that match the complexity of patient care. This system aids providers in implementing effective care management and preventive strategies by directly linking financial rewards to the quality of care delivered. It encourages active patient engagement, personalized care plans, and the use of health IT for efficient care delivery. Regular reassessments required by RAF scoring maintain focus on chronic disease management, promoting a culture of collaboration and ongoing care improvement.
HCC RAF’s Role in Care Coordination
The HCC RAF score plays a critical role in value-based care beyond its financial impact. It provides a comprehensive view of a patient’s medical complexities, enabling care teams to align on a unified understanding of patient needs. This shared perspective is essential for creating and implementing cohesive, patient-centered care plans. By ensuring RAF scores accurately reflect patient conditions, healthcare professionals can coordinate treatment more effectively, leading to enhanced health outcomes.
Transition from V24 to V28 Etches a Permanent Change in RAF Scoring
CMS’s recent updates to RAF scores introduce significant challenges for health plans, with the transition from V24 to V28 requiring system upgrades, data algorithm changes, and process refinements. The new model expands HCC categories from 86 to 115, adds 268 diagnostic codes, and removes 2,296 codes. Health plans must also reconfigure custom internal risk models to align with V28, a transition that will be phased in over three years.
The extent of this impact will hinge on each plan’s level of readiness and preparation. To effectively navigate this transition, health plans should consider the following actions:
- Assess how outcomes differ between models for the same member condition.
- Ensure that new conditions identified in V28 are accurately represented in your data and coding practices.
- Optimize data flow and processes to maintain clean, actionable data that is effectively summarized.
- Leverage modern technology to enable team members to focus on members with missing or anomalous data identifiers.
Streamlining Risk-Capture Reporting in the Face of Changes
Even before CMS introduced changes to risk adjustment, health plans were already facing unexpected revenue declines due to incomplete and inaccurate encounter data, which adversely affected their risk profiles. The recent changes will further complicate compliance reporting processes.
Common root causes of inaccurate reporting include:
- Lack of centralized governance to identify and address issues.
- Insufficient knowledge and training related to the reporting submission process.
- Ineffective data quality control processes.
- IT system designs that do not facilitate easy report submission.
Organizations must establish a continuous process for analyzing and correcting common coding errors or oversights. Strategic initiatives that focus on members, providers, or codes with the most significant coding gaps can lead to positive outcomes.
Charting the Course Forward
A healthcare organization with a flawed risk adjustment system risks severe financial instability and compromised patient care. Inaccurate risk assessments can lead to skewed reimbursements, either under-compensating for high-risk patients or creating regulatory issues due to overpayments. This instability may result in unfair resource distribution, undermining the organization’s ability to provide quality care and exacerbating disparities in patient outcomes. Additionally, non-compliance with regulations can lead to legal consequences and reputational damage, ultimately threatening both financial health and the quality of care.
AI-Enabled Advancements in RAF Analytics
AI technologies, including machine learning algorithms, enable the efficient analysis of vast datasets to identify patterns, predict risk scores, and optimize reimbursement models. RAAPID’s Risk Adjustment Solution offers a comprehensive 360-degree member HCC record by utilizing adaptive learning data processes to analyze data from EMR, claims, billing, pharmacy, lab results, ADT, mental health, HRA, and SDoH sources. RAAPID’s algorithms employ NLP and data matching to meticulously review member HCC records, while proprietary prioritization algorithms generate RAF Opportunity Scores for each member.
By integrating AI into RAF analytics, healthcare organizations can improve the accuracy of risk assessments, optimize resource allocation, and ensure fair compensation for managing patients with diverse health conditions. This technology-driven approach enhances the efficiency of RAF scoring, enabling more informed decision-making and transforming how providers address risk adjustment challenges in an evolving healthcare landscape.
Integration of SDoH into Risk Adjustment Models Could Impact RAF Scoring
Simply put, diagnoses should be determined by the providers involved in the patient’s ongoing care, rather than by external providers who conduct one-time assessments. Specific and meaningful financial value must be assigned to social determinants of health (SDoH), which can be captured using Z-codes, to incentivize the provision of adequately resourced doctors for those who would benefit most from quality primary and preventive care. By making risk adjustment the responsibility of treating providers and incorporating SDoH, we can continue progressing toward a more equitable and healthier society.
Moreover, policy changes like the Medicare Advantage 2024 Advance Notice and the newly issued RADV final rule are expected to transform the Risk adjustment dynamics for MAOs and other stakeholders. Ongoing benchmarking of current member health statuses and an assessment of how these changes will affect the organization are essential. Investing in advanced AI-powered technologies that facilitate accurate and efficient coding of extensive clinical documentation will be crucial for MAOs and other stakeholders to manage their risk adjustment programs effectively.
Conclusion
Healthcare organizations must adopt a proactive approach to ensure accurate RAF scoring, compliance with CMS regulations, and optimal patient care. The transition from V24 to V28, AI integration, and SDoH factors present challenges and opportunities. Embracing these changes, investing in advanced analytics, and prioritizing documentation are crucial for financial stability, better patient outcomes, and navigating healthcare complexities.
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